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Sensors and Robot Control

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 123976

Special Issue Editors

Prof. Dr. Radu-Emil Precup

E-Mail Website
Guest Editor
Department of Automation and Applied Informatics, Faculty of Automation and Computers, Politehnica University of Timişoara, Bulevardul Vasile Pârvan, Nr. 2, 300223 Timişoara, Romania
Interests: new control structures and algorithms; soft computing; computer-aided design of control systems; modelling; optimization; mechatronic systems; embedded systems; control of power plants; servo systems; electrical driving systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sašo Blažič

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia
Interests: control of nonlinear systems; modeling of nonlinear systems; autonomous mobile systems; mobile robotics; motion control; trajectory tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the robot sensor is a key component of the robot, the last decade has led to a serious step forward regarding the development of robot sensors for robot control, autonomous robots, robot perception, and human–robot interaction. Robot sensor design and analysis continues to be a challenging task, which is also related to other hot topics such as sensor materials, structure design, manufacturing processes, calibration techniques, signal processing, data fusion, and pattern recognition.

The research on robot control covers a wide range of applications, including mechanical systems, industrial robots, autonomous systems, medical and rehabilitation robotics, and bipedal and humanoid robots. For these applications, the dynamic environments are usually changing, and the control systems should adapt themselves accordingly. Therefore, by employing intelligent approaches (dealing, for example, with fuzzy systems, neural networks and nature-inspired optimization), advanced control systems have been developed. With this regard, more efforts should be focused on the methodology of learning systems. However, classical and modern analysis tools should be involved to systematically guarantee control system performance improvements.

The main objective of this Special Issue is to create a platform for scientists, engineers, and practitioners to share their latest theoretical and technological results and to discuss several issues for the research directions in the field of advanced sensors and robot control systems. The papers to be published in this Special Issue are expected to provide recent results in advanced sensor design and advanced modeling and controller design, and especially for cross-fertilizations between the fields of control systems and sensors. Papers containing experimental results regarding advanced sensors and robot control systems are especially welcome.

Prof. Dr. Radu-Emil Precup
Prof. Dr. Sašo Blažič
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Advanced intelligent techniques for robot control
  • Data-driven control and learning-based control
  • Sensor fusion
  • Robotic systems modeling, parameter estimation, and optimization
  • Metaheuristics for robot modeling and controller tuning
  • Machine learning for robot control and optimization
  • Adaptive and predictive control
  • Simulation and optimization of intelligent robotic systems
  • Nonlinear observers for robotic systems
  • Soft sensors

Published Papers (28 papers)

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28 pages, 10674 KiB  
Article
Model Predictive Torque Control for Velocity Tracking of a Four-Wheeled Climbing Robot
by Higor Barbosa Santos, Marco Antonio Simoes Teixeira, Nicolas Dalmedico, Andre Schneider de Oliveira, Flavio Neves-Jr, Julio Endress Ramos and Lucia Valeria Ramos de Arruda
Sensors 2020, 20(24), 7059; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247059 - 10 Dec 2020
Cited by 9 | Viewed by 2947
Abstract
Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot’s mobility. Additionally, the wheel friction and [...] Read more.
Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot’s mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot’s movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot’s dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot’s climbing motion by the proposed MPC controller. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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19 pages, 1006 KiB  
Article
Path-Tracking of a WMR Fed by Inverter-DC/DC Buck Power Electronic Converter Systems
by Victor Manuel Hernández-Guzmán, Ramón Silva-Ortigoza, Salvador Tavera-Mosqueda, Mariana Marcelino-Aranda and Magdalena Marciano-Melchor
Sensors 2020, 20(22), 6522; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226522 - 15 Nov 2020
Cited by 5 | Viewed by 1875
Abstract
This paper is concerned with path-tracking control of a wheeled mobile robot. This robot is equipped with two permanent magnet brushed DC-motors which are fed by two inverter-DC/DC Buck power converter systems as power amplifiers. By taking into account the dynamics of all [...] Read more.
This paper is concerned with path-tracking control of a wheeled mobile robot. This robot is equipped with two permanent magnet brushed DC-motors which are fed by two inverter-DC/DC Buck power converter systems as power amplifiers. By taking into account the dynamics of all the subsystems we present, for the first time, a formal stability proof for this control problem. Our control scheme is simple, in the sense that it is composed by four internal classical proportional-integral loops and one external classical proportional-derivative loop for path-tracking purposes. This is the third paper of a series of papers devoted to control different nonlinear systems, which proves that the proposed methodology is a rather general approach for controlling electromechanical systems when actuated by power electronic converters. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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23 pages, 5526 KiB  
Article
High Performance Both in Low-Speed Tracking and Large-Angle Swing Scanning Based on Adaptive Nonsingular Fast Terminal Sliding Mode Control for a Three-Axis Universal Inertially Stabilized Platform
by Yuanchao Wang, Yongming Yang, Haipeng Kuang, Dongming Yuan, Chunfeng Yu, Juan Chen, Nan Hua and Han Hou
Sensors 2020, 20(20), 5785; https://0-doi-org.brum.beds.ac.uk/10.3390/s20205785 - 13 Oct 2020
Cited by 2 | Viewed by 1932
Abstract
In order to improve the performance in the practical engineering applications including so called low-speed video tracking and large-angle swing scanning imaging at the same time for a three-axis universal inertially stabilized platform (UISP), we propose an adaptive nonsingular fast terminal sliding mode [...] Read more.
In order to improve the performance in the practical engineering applications including so called low-speed video tracking and large-angle swing scanning imaging at the same time for a three-axis universal inertially stabilized platform (UISP), we propose an adaptive nonsingular fast terminal sliding mode control (ANFTSMC) strategy subjected to the uncertain disturbances and input saturation constraints. First of all, a second-order dynamic model is established with uncertain disturbances and input saturation constraints. Secondly, a nonsingular fast terminal sliding mode controller (NTSMC) is constructed to ensure the system error converges to zero fast in a finite time; meanwhile, a novel reaching law based on a modified normal distribution function is designed to adjust the control gain. Thirdly, an adaptive control law is designed to online estimate the parameters of the lumped uncertain disturbances. Additionally, the stability of the control system is proved by Lyapunov theory. Finally, extensive comparative simulations and experiments are carried out, the results comprehensively show the effectiveness and superiority of the proposed control method, which can accelerate convergence, weaken the chattering, and has the better control accuracy and robust performance both in the low-speed tracking and large-angle swing scanning applications. Moreover, the exact dynamic model and the prior knowledge of the upper bounds of the disturbances are not required during the procedure of the controller design, which make it have more extensive application value in practical engineering. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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25 pages, 5668 KiB  
Article
A Novel Dynamic Three-Level Tracking Controller for Mobile Robots Considering Actuators and Power Stage Subsystems: Experimental Assessment
by José Rafael García-Sánchez, Salvador Tavera-Mosqueda, Ramón Silva-Ortigoza, Victor Manuel Hernández-Guzmán, Magdalena Marciano-Melchor, José de Jesús Rubio, Mario Ponce-Silva, Miguel Hernández-Bolaños and Jesús Martínez-Martínez
Sensors 2020, 20(17), 4959; https://0-doi-org.brum.beds.ac.uk/10.3390/s20174959 - 02 Sep 2020
Cited by 7 | Viewed by 2597
Abstract
In order to solve the trajectory tracking task in a wheeled mobile robot (WMR), a dynamic three-level controller is presented in this paper. The controller considers the mechanical structure, actuators, and power stage subsystems. Such a controller is designed as follows: At the [...] Read more.
In order to solve the trajectory tracking task in a wheeled mobile robot (WMR), a dynamic three-level controller is presented in this paper. The controller considers the mechanical structure, actuators, and power stage subsystems. Such a controller is designed as follows: At the high level is a dynamic control for the WMR (differential drive type). At the medium level is a PI current control for the actuators (DC motors). Lastly, at the low level is a differential flatness-based control for the power stage (DC/DC Buck power converters). The feasibility, robustness, and performance in closed-loop of the proposed controller are validated on a DDWMR prototype through Matlab-Simulink, the real-time interface ControlDesk, and a DS1104 board. The obtained results are experimentally assessed with a hierarchical tracking controller, recently reported in literature, that was also designed on the basis of the mechanical structure, actuators, and power stage subsystems. Although both controllers are robust when parametric disturbances are taken into account, the dynamic three-level tracking controller presented in this paper is better than the hierarchical tracking controller reported in literature. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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53 pages, 10840 KiB  
Article
Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton
by Sumit Aole, Irraivan Elamvazuthi, Laxman Waghmare, Balasaheb Patre and Fabrice Meriaudeau
Sensors 2020, 20(13), 3681; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133681 - 30 Jun 2020
Cited by 25 | Viewed by 4714
Abstract
Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and [...] Read more.
Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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18 pages, 6506 KiB  
Article
Terminal Sliding Mode Control with a Novel Reaching Law and Sliding Mode Disturbance Observer for Inertial Stabilization Imaging Sensor
by Xin Che, Dapeng Tian, Ping Jia, Yang Gao and Yan Ren
Sensors 2020, 20(11), 3107; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113107 - 31 May 2020
Cited by 6 | Viewed by 2168
Abstract
High-performance control of inertial stabilization imaging sensors (ISISs) is always challenging because of the complex nonlinearities induced by friction, mass imbalance, and external disturbances. To overcome this problem, a terminal sliding mode controller (TSMC) based on a novel exponential reaching law (NERL) method [...] Read more.
High-performance control of inertial stabilization imaging sensors (ISISs) is always challenging because of the complex nonlinearities induced by friction, mass imbalance, and external disturbances. To overcome this problem, a terminal sliding mode controller (TSMC) based on a novel exponential reaching law (NERL) method with a high-order terminal sliding mode observer (HOTSMO) is suggested. First, the TSMC based on NERL is adopted to improve system performance. The NERL incorporates the power term and switching gain term of the system state variables into the conventional exponential reaching law, and the convergent speed of the TSMC is accelerated. Then, an HOTSMO is designed, which considers the speed and lumped disturbances of the system as the observation object. The estimated disturbance is then provided as a compensation for the controller, which enhances the disturbance rejection ability of the system. Comparative simulation and experimental results show that the proposed method achieves the best tracking performance and the strongest robustness than PID and the traditional TSMC methods. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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22 pages, 6676 KiB  
Article
Joint Angle Estimation of a Tendon-Driven Soft Wearable Robot through a Tension and Stroke Measurement
by Byungchul Kim, Jiwon Ryu and Kyu-Jin Cho
Sensors 2020, 20(10), 2852; https://0-doi-org.brum.beds.ac.uk/10.3390/s20102852 - 17 May 2020
Cited by 7 | Viewed by 5237
Abstract
The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptable to the human body. However, [...] Read more.
The size of a device and its adaptability to human properties are important factors in developing a wearable device. In wearable robot research, therefore, soft materials and tendon transmissions have been utilized to make robots compact and adaptable to the human body. However, when used for wearable robots, these methods sometimes cause uncertainties that originate from elongation of the soft material or from undefined human properties. In this research, to consider these uncertainties, we propose a data-driven method that identifies both kinematic and stiffness parameters using tension and wire stroke of the actuators. Through kinematic identification, a method is proposed to find the exact joint position as a function of the joint angle. Through stiffness identification, the relationship between the actuation force and the joint angle is obtained using Gaussian Process Regression (GPR). As a result, by applying the proposed method to a specific robot, the research outlined in this paper verifies how the proposed method can be used in wearable robot applications. This work examines a novel wearable robot named Exo-Index, which assists a human’s index finger through the use of three actuators. The proposed identification methods enable control of the wearable robot to result in appropriate postures for grasping objects of different shapes and sizes. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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26 pages, 8001 KiB  
Article
Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments
by Fatin Hassan Ajeil, Ibraheem Kasim Ibraheem, Ahmad Taher Azar and Amjad J. Humaidi
Sensors 2020, 20(7), 1880; https://0-doi-org.brum.beds.ac.uk/10.3390/s20071880 - 28 Mar 2020
Cited by 128 | Viewed by 9275
Abstract
Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path [...] Read more.
Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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17 pages, 20917 KiB  
Article
Grip Stabilization through Independent Finger Tactile Feedback Control
by Filipe Veiga, Benoni Edin and Jan Peters
Sensors 2020, 20(6), 1748; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061748 - 21 Mar 2020
Cited by 24 | Viewed by 5239
Abstract
Grip force control during robotic in-hand manipulation is usually modeled as a monolithic task, where complex controllers consider the placement of all fingers and the contact states between each finger and the gripped object in order to compute the necessary forces to be [...] Read more.
Grip force control during robotic in-hand manipulation is usually modeled as a monolithic task, where complex controllers consider the placement of all fingers and the contact states between each finger and the gripped object in order to compute the necessary forces to be applied by each finger. Such approaches normally rely on object and contact models and do not generalize well to novel manipulation tasks. Here, we propose a modular grip stabilization method based on a proposition that explains how humans achieve grasp stability. In this biomimetic approach, independent tactile grip stabilization controllers ensure that slip does not occur locally at the engaged robot fingers. Local slip is predicted from the tactile signals of each fingertip sensor i.e., BioTac and BioTac SP by Syntouch. We show that stable grasps emerge without any form of central communication when such independent controllers are engaged in the control of multi-digit robotic hands. The resulting grasps are resistant to external perturbations while ensuring stable grips on a wide variety of objects. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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22 pages, 5198 KiB  
Article
Recycling and Updating an Educational Robot Manipulator with Open-Hardware-Architecture
by Antonio Concha Sánchez, Juan Felipe Figueroa-Rodríguez, Andrés Gerardo Fuentes-Covarrubias, Ricardo Fuentes-Covarrubias and Suresh Kumar Gadi
Sensors 2020, 20(6), 1694; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061694 - 18 Mar 2020
Cited by 7 | Viewed by 3453
Abstract
This article presents a methodology to recycle and upgrade a 4-DOF educational robot manipulator with a gripper. The robot is upgraded by providing it an artificial vision that allows obtaining the position and shape of objects collected by it. A low-cost and open-source [...] Read more.
This article presents a methodology to recycle and upgrade a 4-DOF educational robot manipulator with a gripper. The robot is upgraded by providing it an artificial vision that allows obtaining the position and shape of objects collected by it. A low-cost and open-source hardware solution is also proposed to achieve motion control of the robot through a decentralized control scheme. The robot joints are actuated through five direct current motors coupled to optical encoders. Each encoder signal is fed to a proportional integral derivative controller with anti-windup that employs the motor velocity provided by a state observer. The motion controller works with only two open-architecture Arduino Mega boards, which carry out data acquisition of the optical encoder signals. MATLAB-Simulink is used to implement the controller as well as a friendly graphical interface, which allows the user to interact with the manipulator. The communication between the Arduino boards and MATLAB-Simulink is performed in real-time utilizing the Arduino IO Toolbox. Through the proposed controller, the robot follows a trajectory to collect a desired object, avoiding its collision with other objects. This fact is verified through a set of experiments presented in the paper. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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28 pages, 9469 KiB  
Article
A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment
by Haiyun Shi, Jie Li and Zhi Li
Sensors 2020, 20(6), 1606; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061606 - 13 Mar 2020
Cited by 8 | Viewed by 2568
Abstract
Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. [...] Read more.
Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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33 pages, 9059 KiB  
Article
Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking
by Zefang Li, Zhong Liu and Jianqiang Zhang
Sensors 2020, 20(3), 864; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030864 - 06 Feb 2020
Cited by 6 | Viewed by 2491
Abstract
Multi-under-actuated unmanned surface vehicles (USV) path tracking control is studied and decoupled by virtue of decentralized control. First, an improved integral line-of-sight guidance strategy is put forward and combined with feedback control to design the path tracking controller and realize the single USV [...] Read more.
Multi-under-actuated unmanned surface vehicles (USV) path tracking control is studied and decoupled by virtue of decentralized control. First, an improved integral line-of-sight guidance strategy is put forward and combined with feedback control to design the path tracking controller and realize the single USV path tracking in the horizontal plane. Second, graph theory is utilized to design the decentralized velocity coordination controller for USV formation, so that multiple USVs could consistently realize the specified formation to the position and velocity of the expected path. Third, cascade system theory and Lyapunov stability are used to respectively prove the uniform semi-global exponential stability of single USV path tracking control system and the global asymptotic stability and uniform local exponential stability of coordinated formation system. At last, simulation and field experiment are conducted to analyze and verify the advancement and effectiveness of the proposed algorithms in this paper. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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23 pages, 4352 KiB  
Article
Depth Image–Based Deep Learning of Grasp Planning for Textureless Planar-Faced Objects in Vision-Guided Robotic Bin-Picking
by Ping Jiang, Yoshiyuki Ishihara, Nobukatsu Sugiyama, Junji Oaki, Seiji Tokura, Atsushi Sugahara and Akihito Ogawa
Sensors 2020, 20(3), 706; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030706 - 28 Jan 2020
Cited by 34 | Viewed by 7488
Abstract
Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image–based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for [...] Read more.
Bin-picking of small parcels and other textureless planar-faced objects is a common task at warehouses. A general color image–based vision-guided robot picking system requires feature extraction and goal image preparation of various objects. However, feature extraction for goal image matching is difficult for textureless objects. Further, prior preparation of huge numbers of goal images is impractical at a warehouse. In this paper, we propose a novel depth image–based vision-guided robot bin-picking system for textureless planar-faced objects. Our method uses a deep convolutional neural network (DCNN) model that is trained on 15,000 annotated depth images synthetically generated in a physics simulator to directly predict grasp points without object segmentation. Unlike previous studies that predicted grasp points for a robot suction hand with only one vacuum cup, our DCNN also predicts optimal grasp patterns for a hand with two vacuum cups (left cup on, right cup on, or both cups on). Further, we propose a surface feature descriptor to extract surface features (center position and normal) and refine the predicted grasp point position, removing the need for texture features for vision-guided robot control and sim-to-real modification for DCNN model training. Experimental results demonstrate the efficiency of our system, namely that a robot with 7 degrees of freedom can pick randomly posed textureless boxes in a cluttered environment with a 97.5% success rate at speeds exceeding 1000 pieces per hour. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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21 pages, 5558 KiB  
Article
Tracking Control for Wheeled Mobile Robot Based on Delayed Sensor Measurements
by El-Hadi Guechi, Karim Belharet and Sašo Blažič
Sensors 2019, 19(23), 5177; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235177 - 26 Nov 2019
Cited by 5 | Viewed by 3476
Abstract
This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used [...] Read more.
This paper proposes a novel Takagi-Sugeno fuzzy predictor observer to tackle the problem of the constant and known delay in the measurements. The proposed observer is developed for a trajectory-tracking problem of a wheeled mobile robot where a parallel-distributed compensation control is used to control the robot. The L2-stability of the proposed observer is also proven in the paper. Both, the control and the observer gains are obtained by solving the proposed system of linear matrix inequalities. To illustrate the efficiency of the proposed approach, an experimental comparison with another predictor observer was done. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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19 pages, 3859 KiB  
Article
Actuator Fault Detection and Fault-Tolerant Control for Hexacopter
by Ngoc Phi Nguyen, Nguyen Xuan Mung and Sung Kyung Hong
Sensors 2019, 19(21), 4721; https://0-doi-org.brum.beds.ac.uk/10.3390/s19214721 - 30 Oct 2019
Cited by 37 | Viewed by 5541
Abstract
In this paper, fault detection and fault-tolerant control strategies are proposed to handle the issues of both actuator faults and disturbances in a hexacopter. A dynamic model of a hexacopter is first derived to develop a model-based fault detection system. Secondly, the altitude [...] Read more.
In this paper, fault detection and fault-tolerant control strategies are proposed to handle the issues of both actuator faults and disturbances in a hexacopter. A dynamic model of a hexacopter is first derived to develop a model-based fault detection system. Secondly, the altitude control based on a sliding mode and disturbance observer is presented to tackle the disturbance issue. Then, a nonlinear Thau observer is applied to estimate the states of a hexacopter and to generate the residuals. Using a fault detection unit, the motor failure is isolated to address the one or two actuator faults. Finally, experimental results are tested on a DJI F550 hexacopter platform and Pixhawk2 flight controller to verify the effectiveness of the proposed approach. Unlike previous studies, this work can integrate fault detection and fault-tolerant control design as a single unit. Moreover, the developed fault detection and fault-tolerant control method can handle up to two actuator failures in presence of disturbances. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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19 pages, 9192 KiB  
Article
A Robust Balance-Control Framework for the Terrain-Blind Bipedal Walking of a Humanoid Robot on Unknown and Uneven Terrain
by Hyun-Min Joe and Jun-Ho Oh
Sensors 2019, 19(19), 4194; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194194 - 27 Sep 2019
Cited by 31 | Viewed by 4639
Abstract
Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This paper describes the design, [...] Read more.
Research on a terrain-blind walking control that can walk stably on unknown and uneven terrain is an important research field for humanoid robots to achieve human-level walking abilities, and it is still a field that needs much improvement. This paper describes the design, implementation, and experimental results of a robust balance-control framework for the stable walking of a humanoid robot on unknown and uneven terrain. For robust balance-control against disturbances caused by uneven terrain, we propose a framework that combines a capture-point controller that modifies the control reference, and a balance controller that follows its control references in a cascading structure. The capture-point controller adjusts a zero-moment point reference to stabilize the perturbed capture-point from the disturbance, and the adjusted zero-moment point reference is utilized as a control reference for the balance controller, comprised of zero-moment point, leg length, and foot orientation controllers. By adjusting the zero-moment point reference according to the disturbance, our zero-moment point controller guarantees robust zero-moment point control performance in uneven terrain, unlike previous zero-moment point controllers. In addition, for fast posture stabilization in uneven terrain, we applied a proportional-derivative admittance controller to the leg length and foot orientation controllers to rapidly adapt these parts of the robot to uneven terrain without vibration. Furthermore, to activate position or force control depending on the gait phase of a robot, we applied gain scheduling to the leg length and foot orientation controllers, which simplifies their implementation. The effectiveness of the proposed control framework was verified by stable walking performance on various uneven terrains, such as slopes, stone fields, and lawns. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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12 pages, 1772 KiB  
Article
Cooperative Localization Approach for Multi-Robot Systems Based on State Estimation Error Compensation
by Shijie Zhang and Yi Cao
Sensors 2019, 19(18), 3842; https://0-doi-org.brum.beds.ac.uk/10.3390/s19183842 - 05 Sep 2019
Cited by 11 | Viewed by 2669
Abstract
In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then [...] Read more.
In order to improve the localization accuracy of multi-robot systems, a cooperative localization approach with communication delays was proposed in this paper. In the proposed method, the reason for the time delay of the robots’ cooperative localization approach was analyzed first, and then the state equation and measure equation were reconstructed by introducing the communication delays into the states and measurements. Furthermore, the cooperative localization algorithm using the extended Kalman filtering technique based on state estimation error compensation was proposed to reduce the state estimation error of delay filtering. Finally, the simulation and experiment results demonstrated that the proposed algorithm can achieve good performance in location in the presence of communication delay while having reduced computational and communicative cost. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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18 pages, 1872 KiB  
Article
Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning
by Junjie Zeng, Rusheng Ju, Long Qin, Yue Hu, Quanjun Yin and Cong Hu
Sensors 2019, 19(18), 3837; https://0-doi-org.brum.beds.ac.uk/10.3390/s19183837 - 05 Sep 2019
Cited by 46 | Viewed by 5278
Abstract
In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its [...] Read more.
In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot’s capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration commands. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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26 pages, 16741 KiB  
Article
CPG-Based Gait Generation of the Curved-Leg Hexapod Robot with Smooth Gait Transition
by Long Bai, Hao Hu, Xiaohong Chen, Yuanxi Sun, Chaoyang Ma and Yuanhong Zhong
Sensors 2019, 19(17), 3705; https://0-doi-org.brum.beds.ac.uk/10.3390/s19173705 - 26 Aug 2019
Cited by 39 | Viewed by 4926
Abstract
This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is [...] Read more.
This paper presents a novel CPG-based gait generation of the curved-leg hexapod robot that can enable smooth gait transitions between multi-mode gaits. First, the locomotion of the curved leg and instability during the gait transitions are analyzed. Then, a modified Hopf oscillator is applied in the CPG control, which can realize multiple gaits by adjusting a simple parameter. In addition, a smooth gait switching method is also proposed via smooth gait transition functions and gait planning. Tripod gait, quadruped gait, and wave gait are planned for the hexapod robot to achieve quick and stable gait transitions smoothly and continuously. MATLAB and ADAMS simulations and corresponding practical experiments are conducted. The results show that the proposed method can achieve smooth and continuous mutual gait transitions, which proves the effectiveness of the proposed CPG-based hexapod robot control. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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22 pages, 3469 KiB  
Article
Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive
by Chih-Hong Lin and Kuo-Tsai Chang
Sensors 2019, 19(16), 3616; https://0-doi-org.brum.beds.ac.uk/10.3390/s19163616 - 20 Aug 2019
Viewed by 2793
Abstract
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, [...] Read more.
In order to cut down influence on the uncertainty disturbances of a linear motion single axis robot machine, such as the external load force, the cogging force, the column friction force, the Stribeck force, and the parameters variations, the micrometer backstepping control system, using an amended recurrent Gottlieb polynomials neural network and altered ant colony optimization (AACO) with the compensated controller, is put forward for a linear motion single axis robot machine drive system mounted on the linear-optical ruler with 1 um resolution. To achieve high-precision control performance, an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is proposed to estimate the lumped uncertainty. Besides this, a novel error-estimated law of the compensated controller is also proposed to compensate for the estimated error between the lumped uncertainty and the amended recurrent Gottlieb polynomials neural network with the adaptive law. Meanwhile, the AACO is used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. The main contributions of this paper are: (1) The digital signal processor (DSP)-based current-regulation pulse width modulation (PWM) control scheme being successfully applied to control the linear motion single axis robot machine drive system; (2) the micrometer backstepping control system using an amended recurrent Gottlieb polynomials neural network with the compensated controller being successfully derived according to the Lyapunov function to diminish the lumped uncertainty effect; (3) achieving high-precision control performance, where an adaptive law of the amended recurrent Gottlieb polynomials neural network based on the Lyapunov function is successfully applied to estimate the lumped uncertainty; (4) a novel error-estimated law of the compensated controller being successfully used to compensate for the estimated error; and (5) the AACO being successfully used to regulate two variable learning rates in the weights of the amended recurrent Gottlieb polynomials neural network to speed up the convergent speed. Finally, the effectiveness of the proposed control scheme is also verified by the experimental results. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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31 pages, 15283 KiB  
Article
Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs
by Tuan-Tang Le and Chyi-Yeu Lin
Sensors 2019, 19(16), 3602; https://0-doi-org.brum.beds.ac.uk/10.3390/s19163602 - 19 Aug 2019
Cited by 28 | Viewed by 7697
Abstract
Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves [...] Read more.
Random bin-picking is a prominent, useful, and challenging industrial robotics application. However, many industrial and real-world objects are planar and have oriented surface points that are not sufficiently compact and discriminative for those methods using geometry information, especially depth discontinuities. This study solves the above-mentioned problems by proposing a novel and robust solution for random bin-picking for planar objects in a cluttered environment. Different from other research that has mainly focused on 3D information, this study first applies an instance segmentation-based deep learning approach using 2D image data for classifying and localizing the target object while generating a mask for each instance. The presented approach, moreover, serves as a pioneering method to extract 3D point cloud data based on 2D pixel values for building the appropriate coordinate system on the planar object plane. The experimental results showed that the proposed method reached an accuracy rate of 100% for classifying two-sided objects in the unseen dataset, and 3D appropriate pose prediction was highly effective, with average translation and rotation errors less than 0.23 cm and 2.26°, respectively. Finally, the system success rate for picking up objects was over 99% at an average processing time of 0.9 s per step, fast enough for continuous robotic operation without interruption. This showed a promising higher successful pickup rate compared to previous approaches to random bin-picking problems. Successful implementation of the proposed approach for USB packs provides a solid basis for other planar objects in a cluttered environment. With remarkable precision and efficiency, this study shows significant commercialization potential. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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26 pages, 8003 KiB  
Article
Real-Time Robust and Optimized Control of a 3D Overhead Crane System
by Arash Khatamianfar and Andrey V. Savkin
Sensors 2019, 19(15), 3429; https://0-doi-org.brum.beds.ac.uk/10.3390/s19153429 - 05 Aug 2019
Cited by 11 | Viewed by 7044
Abstract
A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, [...] Read more.
A new and advanced control system for three-dimensional (3D) overhead cranes is proposed in this study using state feedback control in discrete time to deliver high performance trajectory tracking with minimum load swings in high-speed motions. By adopting the independent joint control strategy, a new and simplified model is developed where the overhead crane actuators are used to design the controller, with all the nonlinear equations of motions being viewed as disturbances affecting each actuator. A feedforward control is then designed to tackle these disturbances via computed torque control technique. A new load swing control is designed along with a new motion planning scheme to robustly minimize load swings as well as allowing fast load transportation without violating system’s constraints through updating reference trolley accelerations. The stability and performance analysis of the proposed discrete-time control system are demonstrated and validated analytically and practically. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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26 pages, 8652 KiB  
Article
Modelling and Control of Mechatronics Lines Served by Complex Autonomous Systems
by Florin Dragomir, Eugenia Mincă, Otilia Elena Dragomir and Adrian Filipescu
Sensors 2019, 19(15), 3266; https://0-doi-org.brum.beds.ac.uk/10.3390/s19153266 - 24 Jul 2019
Cited by 14 | Viewed by 2998
Abstract
The aim of this paper is to reverse an assembly line, to be able to perform disassembly, using two complex autonomous systems (CASs). The disassembly is functioning only in case of quality default identified in the final product. The CASs are wheeled mobile [...] Read more.
The aim of this paper is to reverse an assembly line, to be able to perform disassembly, using two complex autonomous systems (CASs). The disassembly is functioning only in case of quality default identified in the final product. The CASs are wheeled mobile robots (WMRs) equipped with robotic manipulators (RMs), working in parallel or collaboratively. The reversible assembly/disassembly mechatronics line (A/DML) assisted by CASs has a specific typology and is modelled by specialized hybrid instruments belonging to the Petri nets class, precisely synchronized hybrid Petri nets (SHPN). The need of this type of models is justified by the necessity of collaboration between the A/DML and CASs, both having characteristics and physical constraints that should be considered and to make all systems compatible. Firstly, the paper proposes the planning and scheduling of tasks necessary in modelling stage as well as in real time control. Secondly, two different approaches are proposed, related to CASs collaboration: a parallel approach with two CASs have simultaneous actions: one is equipped with robotic manipulator, used for manipulation, and the other is used for transporting. This approach is correlated with industrial A/D manufacturing lines where have to transport and handle weights in a wide range of variation. The other is a collaborative approach, A/DML is served by two CASs used for manipulation and transporting, both having simultaneous movements, following their own trajectories. One will assist the disassembly in even, while the other in odd workstations. The added value of this second approach consists in the optimization of a complete disassembly cycle. Thirdly, it is proposed in the paper the real time control of mechatronics line served by CASs working in parallel, based on the SHPN model. The novelty of the control procedure consists in the use of the synchronization signals, in absence of the visual servoing systems, for a precise positioning of the CASs serving the reversible mechatronics line. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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20 pages, 4819 KiB  
Article
A Sensorless and Low-Gain Brushless DC Motor Controller Using a Simplified Dynamic Force Compensator for Robot Arm Application
by Shih-Hsiang Yen, Pei-Chong Tang, Yuan-Chiu Lin and Chyi-Yeu Lin
Sensors 2019, 19(14), 3171; https://0-doi-org.brum.beds.ac.uk/10.3390/s19143171 - 18 Jul 2019
Cited by 13 | Viewed by 4556
Abstract
Robot arms used for service applications require safe human–machine interactions; therefore, the control gain of such robot arms must be minimized to limit the force output during operation, which slows the response of the control system. To improve cost efficiency, low-resolution sensors can [...] Read more.
Robot arms used for service applications require safe human–machine interactions; therefore, the control gain of such robot arms must be minimized to limit the force output during operation, which slows the response of the control system. To improve cost efficiency, low-resolution sensors can be used to reduce cost because the robot arms do not require high precision of position sensing. However, low-resolution sensors slow the response of closed-loop control systems, leading to low accuracy. Focusing on safety and cost reduction, this study proposed a low-gain, sensorless Brushless DC motor control architecture, which performed position and torque control using only Hall-effect sensors and a current sensor. Low-pass filters were added in servo controllers to solve the sensing problems of undersampling and noise. To improve the control system’s excessively slow response, we added a dynamic force compensator in the current controllers, simplified the system model, and conducted tuning experiments to expedite the calculation of dynamic force. These approaches achieved real-time current compensation, and accelerated control response and accuracy. Finally, a seven-axis robot arm was used in our experiments and analyses to verify the effectiveness of the simplified dynamic force compensators. Specifically, these experiments examined whether the sensorless drivers and compensators could achieve the required response and accuracy while reducing the control system’s cost. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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19 pages, 2390 KiB  
Article
Development of a Virtual Force Sensor for a Low-Cost Collaborative Robot and Applications to Safety Control
by Shih-Hsiang Yen, Pei-Chong Tang, Yuan-Chiu Lin and Chyi-Yeu Lin
Sensors 2019, 19(11), 2603; https://0-doi-org.brum.beds.ac.uk/10.3390/s19112603 - 07 Jun 2019
Cited by 25 | Viewed by 8401
Abstract
To protect operators and conform to safety standards for human–machine interactions, the design of collaborative robot arms often incorporates flexible mechanisms and force sensors to detect and absorb external impact forces. However, this approach increases production costs, making the introduction of such robot [...] Read more.
To protect operators and conform to safety standards for human–machine interactions, the design of collaborative robot arms often incorporates flexible mechanisms and force sensors to detect and absorb external impact forces. However, this approach increases production costs, making the introduction of such robot arms into low-cost service applications difficult. This study proposes a low-cost, sensorless rigid robot arm design that employs a virtual force sensor and stiffness control to enable the safety collision detection and low-precision force control of robot arms. In this design, when a robot arm is subjected to an external force while in motion, the contact force observer estimates the external torques on each joint according to the motor electric current and calculation errors of the system model, which are then used to estimate the external contact force exerted on the robot arm’s end-effector. Additionally, a torque saturation limiter is added to the servo drive for each axis to enable the real-time adjustment of joint torque output according to the estimated external force, regulation of system stiffness, and achievement of impedance control that can be applied in safety measures and force control. The design this study developed is a departure from the conventional multisensor flexible mechanism approach. Moreover, it is a low-cost and sensorless design that relies on model-based control for stiffness regulation, thereby improving the safety and force control in robot arm applications. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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17 pages, 2603 KiB  
Article
Dynamic Parameter Identification for a Manipulator with Joint Torque Sensors Based on an Improved Experimental Design
by Jidong Jia, Minglu Zhang, Xizhe Zang, He Zhang and Jie Zhao
Sensors 2019, 19(10), 2248; https://0-doi-org.brum.beds.ac.uk/10.3390/s19102248 - 15 May 2019
Cited by 34 | Viewed by 4659
Abstract
As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, [...] Read more.
As the foundation of model control, robot dynamics is crucial. However, a robot is a complex multi-input–multi-output system. System noise seriously affects parameter identification results, thereby inevitably requiring us to conduct signal processing to extract useful signals from chaotic noise. In this research, the dynamic parameters were identified on the basis of the proposed multi-criteria embedded optimization design method, to obtain the optimal excitation signal and then use maximum likelihood estimation for parameter identification. Considering the movement coupling characteristics of the multi-axis, experiments were based on a two degrees-of-freedom manipulator with joint torque sensors. Simulation and experimental results showed that the proposed method can reasonably resolve the problem of mutual opposition within a single criterion and improve the identification robustness in comparison with other optimization criteria. The mean relative standard deviation was 0.04 and 0.3 lower in the identified parameters than in F1 and F3, respectively, thus signifying that noise is effectively alleviated. In addition, validation experimental curves were close to the estimation model, and the average of root mean square (RMS) is 0.038, thereby confirming the accuracy of the proposed method. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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16 pages, 1486 KiB  
Letter
Neural Network Based Contact Force Control Algorithm for Walking Robots
by Byeongjin Kim and Soohyun Kim
Sensors 2021, 21(1), 287; https://0-doi-org.brum.beds.ac.uk/10.3390/s21010287 - 04 Jan 2021
Cited by 2 | Viewed by 2655
Abstract
Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed [...] Read more.
Walking algorithms using push-off improve moving efficiency and disturbance rejection performance. However, the algorithm based on classical contact force control requires an exact model or a Force/Torque sensor. This paper proposes a novel contact force control algorithm based on neural networks. The proposed model is adapted to a linear quadratic regulator for position control and balance. The results demonstrate that this neural network-based model can accurately generate force and effectively reduce errors without requiring a sensor. The effectiveness of the algorithm is assessed with the realistic test model. Compared to the Jacobian-based calculation, our algorithm significantly improves the accuracy of the force control. One step simulation was used to analyze the robustness of the algorithm. In summary, this walking control algorithm generates a push-off force with precision and enables it to reject disturbance rapidly. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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15 pages, 3148 KiB  
Letter
Disturbance Recognition and Collision Detection of Manipulator Based on Momentum Observer
by Xiaojun Zhang, Jian Zhao, Minglu Zhang and Xiaoyu Liu
Sensors 2020, 20(15), 4187; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154187 - 28 Jul 2020
Cited by 7 | Viewed by 2575
Abstract
Increasing requirements for the safety of human-robot interaction and the cost-effectiveness of collision detection rapidly promote the development of collision detection technology without torque sensors. To address nonlinear disturbance factors in collision detection that may cause unstable or even incorrect detection, this paper [...] Read more.
Increasing requirements for the safety of human-robot interaction and the cost-effectiveness of collision detection rapidly promote the development of collision detection technology without torque sensors. To address nonlinear disturbance factors in collision detection that may cause unstable or even incorrect detection, this paper proposed a research strategy that considered the friction as the disturbance term in manipulator motion for the collision detection. The manipulator joint disturbance model was established based on the LuGre dynamic friction model, and the external torque observer was designed based on the generalized momentum. Then, the friction measurement was realized using the external torque observer, and the model parameters were identified through the genetic algorithm. The collision detection can be reduced errors after the friction model by compensating the disturbance and can be applicable to variable working conditions. Finally, the accuracy of the constructed disturbance model and the performance of the proposed collision detection method were validated by the experimental studies. Full article
(This article belongs to the Special Issue Sensors and Robot Control)
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