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Advances in Robot Path Planning
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Advances in Robot Path Planning

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 38997

Special Issue Editors

Dr. Byung-Cheol Min

E-Mail Website
Guest Editor
Department of Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA
Interests: robotics; multi-robot systems; human-robot interaction; field robotics; assistive robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jonghoek Kim

E-Mail Website
Guest Editor
Defense System Engineering Department, Sejong University, Seoul 05006, Republic of Korea
Interests: robotics; target tracking; multi-agent robotics; optimal estimation; path planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning is fundamental and crucial for various kinds of robots, such as autonomous vehicles, multiple robots, or robot arms. It is crucial to generate a safe path without colliding with obstacles or other robots, in the case of path planning of multiple robots. Considering an aerial robot or underwater robot, the safe path must be planned considering the 3D environment. The complexity of path planning of a robot arm increases significantly as the number of degrees of freedom increases. Thus, safe paths must be generated for high-dimensional systems in a time-efficient manner. In practice, an obstacle may move and, thus, a robot’s path must be replanned if necessary. Moreover, it is desirable to consider the dynamic model of a robot when generating a path for the robot. This Special Issue will present the recent research advances in these research topics.

We welcome original research papers that focus on theory, practice, and applications of robot path planning. Survey papers or tutorial papers on this topic are also encouraged.

Dr. Byung-Cheol Min
Dr. Jonghoek Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • Online and dynamic path planning
  • Energy-efficient path planning
  • Motion planning
  • SLAM
  • Coverage problem
  • Mobile robots
  • Multiple robots
  • Robot arms
  • Underwater robots
  • UAVs

Published Papers (18 papers)

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Research

17 pages, 1250 KiB  
Article
RJ-RRT: Improved RRT for Path Planning in Narrow Passages
by Qisen Chai and Yujun Wang
Appl. Sci. 2022, 12(23), 12033; https://0-doi-org.brum.beds.ac.uk/10.3390/app122312033 - 24 Nov 2022
Cited by 5 | Viewed by 1650
Abstract
As a representative of sampling-based planning algorithms, rapidly exploring random tree (RRT), is extensively welcomed in solving robot path planning problems due to its wide application range and easy addition of nonholonomic constraints. However, it is still challenging for RRT to plan the [...] Read more.
As a representative of sampling-based planning algorithms, rapidly exploring random tree (RRT), is extensively welcomed in solving robot path planning problems due to its wide application range and easy addition of nonholonomic constraints. However, it is still challenging for RRT to plan the path for configuration space with narrow passages. As a variant algorithm of RRT, rapid random discovery vine (RRV) gives a better solution, but when configuration space contains more obstacles instead of narrow passages, RRV performs slightly worse than RRT. In order to solve these problems, this paper re-examines the role of sampling points in RRT. Firstly, according to the state of the random tree expanding towards the current sampling point, a greedy sampling space reduction strategy is proposed, which decreases the redundant expansion of the random tree in space by dynamically changing the sampling space. Secondly, a new narrow passage judgment method is proposed according to the environment around of sampling point. After the narrow passage is identified, the narrow passage is explored by generating multiple subtrees inside the passage. The subtrees can be merged into the main tree that expands in a larger area by subsequent sampling. These improvements further enhance the value of sampling points. Compared with the existing RRT algorithms, the adaptability for different environments is improved, and the planning time and memory usage are saved. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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18 pages, 5330 KiB  
Article
APF-IRRT*: An Improved Informed Rapidly-Exploring Random Trees-Star Algorithm by Introducing Artificial Potential Field Method for Mobile Robot Path Planning
by Daohua Wu, Lisheng Wei, Guanling Wang, Li Tian and Guangzhen Dai
Appl. Sci. 2022, 12(21), 10905; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110905 - 27 Oct 2022
Cited by 6 | Viewed by 2210
Abstract
An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of the state space. However, IRRT* [...] Read more.
An Informed RRT* (IRRT*) algorithm is one of the optimized versions of a Rapidly-exploring Random Trees (RRT) algorithm which finds near-optimal solutions faster than RRT and RRT* algorithms by restricting the search area to an ellipsoidal subset of the state space. However, IRRT* algorithm has the disadvantage of randomness of sampling and a non-real time process, which has a negative impact on the convergence rate and search efficiency in path planning applications. In this paper, we report a hybrid algorithm by combining the Artificial Potential Field Method (APF) with an IRRT* algorithm for mobile robot path planning. By introducing the virtual force field of APF into the search tree expansion stage of the IRRT* algorithm, the guidance of the algorithm increases, which greatly improves the convergence rate and search efficiency of the IRRT* algorithm. The proposed algorithm was validated in simulations and proven to be superior to some other RRT-based algorithms in search time and path length. It also was performed in a real robotic platform, which shows that the proposed algorithm can be well executed in real scenarios. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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18 pages, 21118 KiB  
Article
Tight Maneuvering for Path Planning of Hyper-Redundant Manipulators in Three-Dimensional Environments
by Okan Minnetoglu and Erdinc Sahin Conkur
Appl. Sci. 2022, 12(17), 8882; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178882 - 04 Sep 2022
Cited by 3 | Viewed by 1609
Abstract
An effective path-planning algorithm in three-dimensional (3D) environments based on a geometric approach for redundant/hyper-redundant manipulators are presented in this paper. The method works within confined spaces cluttered with obstacles in real-time. Using potential fields in 3D, a middle path is generated for [...] Read more.
An effective path-planning algorithm in three-dimensional (3D) environments based on a geometric approach for redundant/hyper-redundant manipulators are presented in this paper. The method works within confined spaces cluttered with obstacles in real-time. Using potential fields in 3D, a middle path is generated for point robots. Beams are generated tangent to the path points, which constructs a basis for preparing a collision-free path for the manipulator. Then, employing a simply control strategy without interaction between the links, the motion planning is achieved by advancing the end-effector of the manipulator through narrow terrains while keeping each link’s joints on this path until the end-effector reaches the goal. The method is simple, robust and significantly increases maneuvering ability of the manipulator in 3D environments compared to existing methods as illustrated with examples. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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11 pages, 347 KiB  
Article
Robust Distributed Rendezvous Using Multiple Robots with Variable Range Radars
by Chunhyung Cho and Jonghoek Kim
Appl. Sci. 2022, 12(17), 8535; https://0-doi-org.brum.beds.ac.uk/10.3390/app12178535 - 26 Aug 2022
Cited by 1 | Viewed by 1044
Abstract
This paper considers multi-robot systems, such that each robot has a radar for detecting its neighbor robots. We consider a practical scenario in which a radar sensor contains measurement noise, and the environmental disturbance generates process noise in a robot’s maneuvering. We consider [...] Read more.
This paper considers multi-robot systems, such that each robot has a radar for detecting its neighbor robots. We consider a practical scenario in which a radar sensor contains measurement noise, and the environmental disturbance generates process noise in a robot’s maneuvering. We consider a 3D scenario such that the network can be split initially. For instance, complete failures of one or more robots can split the network. Considering 3D environments, the goal of our paper is to let all robots rendezvous in a distributed manner so that the network connectivity can be recovered even after the network is split. Robust distributed rendezvous control is designed so that the network connectivity is maintained (or recovered) during the maneuvering of a robot. To recover the network connectivity, we adaptively control the robot’s radar footprint by increasing the transmission power level (adjust the amplifier in the transmitter). To the best of our knowledge, this paper is novel in applying a radar with a variable sensing range in order to make all robots rendezvous in 3D environments. We address MATLAB simulations to demonstrate the outperformance of our rendezvous approach with variable range radars by comparing it with the state-of-the-art in multi-robot rendezvous controls. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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17 pages, 550 KiB  
Article
Three-Dimensional Formation Control for Robot Swarms
by Jonghoek Kim
Appl. Sci. 2022, 12(16), 8078; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168078 - 12 Aug 2022
Cited by 1 | Viewed by 1144
Abstract
This article addresses a distributed 3D algorithm for coordinating a swarm of autonomous robots (ARs) to spatially self-aggregate into an arbitrary shape based on only local interactions. Each AR has local proximity sensors for measuring the relative coordinates of its nearby AR. We [...] Read more.
This article addresses a distributed 3D algorithm for coordinating a swarm of autonomous robots (ARs) to spatially self-aggregate into an arbitrary shape based on only local interactions. Each AR has local proximity sensors for measuring the relative coordinates of its nearby AR. We assume that only a single AR, called the leader, can localize itself in global coordinate systems, while accessing the arbitrary user-specified 3D shape. We further assume that the leader has a communication ability superior to all other ARs so that the leader can directly send a communication signal to any other AR inside the shape. Our aim is to make the ARs maneuver while maintaining a user-specified 3D formation such that the network connection of all ARs is maintained during the maneuver. Our approach results in a 3D formation shape and does not need the global localization of an AR, except for the leader. To the best of our knowledge, our study is novel in the construction of a 3D formation for covering an arbitrary shape such that network connection is maintained while ARs maneuver based on local communication. We further show that the proposed control yields reliable accuracy against significant AR failures and movement error. Utilizing MATLAB simulations, we demonstrate the outperformance of the proposed formation controls. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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23 pages, 5631 KiB  
Article
Trajectory Generation and Optimization Using the Mutual Learning and Adaptive Ant Colony Algorithm in Uneven Environments
by Feng Qu, Wentao Yu, Kui Xiao, Chaofan Liu and Weirong Liu
Appl. Sci. 2022, 12(9), 4629; https://0-doi-org.brum.beds.ac.uk/10.3390/app12094629 - 04 May 2022
Cited by 6 | Viewed by 1723
Abstract
Aiming at the trajectory generation and optimization of mobile robots in complex and uneven environments, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) is proposed in this paper. In order to describe the uneven environment with various obstacles, a [...] Read more.
Aiming at the trajectory generation and optimization of mobile robots in complex and uneven environments, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) is proposed in this paper. In order to describe the uneven environment with various obstacles, a 2D-H map is introduced in this paper. Then an adaptive ant colony algorithm based on simulated annealing (SA) is proposed to generate initial trajectories of mobile robots, where based on a de-temperature function of the simulated annealing algorithm, the pheromone volatilization factor is adaptively adjusted to accelerate the convergence of the algorithm. Moreover, the length factor, height factor, and smooth factor are considered in the comprehensive heuristic function of ACO to adapt to uneven environments. Finally, a mutual learning algorithm is designed to further smooth and shorten initial trajectories, in which different trajectory node sequences learn from each other to acquire the shortest trajectory sequence to optimize the trajectory. In order to verify the effectiveness of the proposed scheme, MuL-ACO is compared with several well-known and novel algorithms in terms of running time, trajectory length, height, and smoothness. The experimental results show that MuL-ACO can generate a collision-free trajectory with a high comprehensive quality in uneven environments. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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23 pages, 4962 KiB  
Article
Interoperability between Real and Virtual Environments Connected by a GAN for the Path-Planning Problem
by Javier Maldonado-Romo and Mario Aldape-Pérez
Appl. Sci. 2021, 11(21), 10445; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110445 - 07 Nov 2021
Cited by 4 | Viewed by 2060
Abstract
Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate [...] Read more.
Path planning is a fundamental issue in robotic systems because it requires coordination between the environment and an agent. The path-planning generator is composed of two modules: perception and planning. The first module scans the environment to determine the location, detect obstacles, estimate objects in motion, and build the planner module’s restrictions. On the other hand, the second module controls the flight of the system. This process is computationally expensive and requires adequate performance to avoid accidents. For this reason, we propose a novel solution to improve conventional robotic systems’ functions, such as systems having a small-capacity battery, a restricted size, and a limited number of sensors, using fewer elements. A navigation dataset was generated through a virtual simulator and a generative adversarial network to connect the virtual and real environments under an end-to-end approach. Furthermore, three path generators were analyzed using deep-learning solutions: a deep convolutional neural network, hierarchical clustering, and an auto-encoder. Since the path generators share a characteristic vector, transfer learning approaches complex problems by using solutions with fewer features, minimizing the costs and optimizing the resources of conventional system architectures, thus improving the limitations with respect to the implementation in embedded devices. Finally, a visualizer applying augmented reality was used to display the path generated by the proposed system. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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19 pages, 7414 KiB  
Article
Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles
by Hongchao Zhuang, Kailun Dong, Yuming Qi, Ning Wang and Lei Dong
Appl. Sci. 2021, 11(16), 7378; https://0-doi-org.brum.beds.ac.uk/10.3390/app11167378 - 11 Aug 2021
Cited by 12 | Viewed by 2260
Abstract
In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of [...] Read more.
In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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16 pages, 20176 KiB  
Article
A Composite Metric Routing Approach for Energy-Efficient Shortest Path Planning on Natural Terrains
by Mohamed Saad, Ahmed I. Salameh, Saeed Abdallah, Ali El-Moursy and Chi-Tsun Cheng
Appl. Sci. 2021, 11(15), 6939; https://0-doi-org.brum.beds.ac.uk/10.3390/app11156939 - 28 Jul 2021
Cited by 7 | Viewed by 1651
Abstract
This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy path planning algorithm based on a composite metric routing approach that combines the energy consumption and distance of the [...] Read more.
This paper explores the problem of energy-efficient shortest path planning on off-road, natural, real-life terrain for unmanned ground vehicles (UGVs). We present a greedy path planning algorithm based on a composite metric routing approach that combines the energy consumption and distance of the path. In our work, we consider the Terramechanics between the UGV and the terrain soil to account for the wheel sinkage effect, in addition to the terrain slope and soil deformation limitations in the development of the path planning algorithm. As benchmarks for comparison, we use a recent energy-cost minimization approach, in addition to an ant colony optimization (ACO) implementation. Our results indicate that the proposed composite metric routing approach outperforms the state-of-the-art energy-cost minimization method in terms of the resulting path distance, with a negligible increase in energy consumption. Moreover, our results indicate also that the proposed greedy algorithm strongly outperforms the ACO implementation in terms of the quality of the paths obtained and the algorithm running time. In fact, the running time of our proposed algorithm indicates its suitability for large natural terrain graphs with thousands of nodes and tens of thousands of links. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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15 pages, 692 KiB  
Article
Constructing 3D Underwater Sensor Networks without Sensing Holes Utilizing Heterogeneous Underwater Robots
by Jonghoek Kim
Appl. Sci. 2021, 11(9), 4293; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094293 - 10 May 2021
Cited by 7 | Viewed by 1642
Abstract
This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such [...] Read more.
This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no sensing hole remains. As far as we know, this article is novel in enabling multiple heterogeneous robots to build underwater sensor networks in a 3D cluttered environment, while satisfying the following conditions: (1) Remove all sensing holes. (2) Network connectivity is maintained. (3) Localize all underwater robots. In addition, we address how to handle the case where a robot is broken, and we discuss how to estimate the number of robots required, considering the case where an obstacle inside the workspace is not known a priori. Utilizing MATLAB simulations, we demonstrate the effectiveness of the proposed network construction methods. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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8 pages, 302 KiB  
Article
Autonomous Balloon Controls for Protection against Projectiles with Known Destinations
by Jonghoek Kim
Appl. Sci. 2021, 11(9), 4077; https://0-doi-org.brum.beds.ac.uk/10.3390/app11094077 - 29 Apr 2021
Cited by 1 | Viewed by 1535
Abstract
This article tackles autonomous balloon controls for protection against projectiles with known destinations. We introduce a defense strategy against an enemy projectile trying to reach a destination, such as a military base, which is known a priori. We further assume that the position [...] Read more.
This article tackles autonomous balloon controls for protection against projectiles with known destinations. We introduce a defense strategy against an enemy projectile trying to reach a destination, such as a military base, which is known a priori. We further assume that the position of the platform that launches the projectile is known in advance. Because both the platform and the projectile’s destination are known in advance, we can predict the trajectory of the projectile before the projectile is launched. The proposed defense strategy is to deploy multiple balloons on the projectile’s feasible paths so that they block the incoming projectile effectively. Each balloon has GPS sensors for locating itself and IR sensors to detect an incoming projectile. Once the projectile is sufficiently close to a balloon, the balloon explodes to destroy the projectile. Since the projectile’s purpose is reaching its destination, the balloons can effectively intercept the projectile using this blocking strategy. As far as we know, this article is novel in utilizing multiple balloons for protection against an enemy projectile. The effectiveness of our defense strategy is further verified utilizing MATLAB simulations. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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21 pages, 3743 KiB  
Article
Broadcast Event-Triggered Control Scheme for Multi-Agent Rendezvous Problem in a Mixed Communication Environment
by Nohaidda Sariff and Zool Hilmi Ismail
Appl. Sci. 2021, 11(9), 3785; https://0-doi-org.brum.beds.ac.uk/10.3390/app11093785 - 22 Apr 2021
Cited by 3 | Viewed by 1715
Abstract
This paper addresses the communication issue encountered by a hybrid controller when finding consensus in terms of the rendezvous target point in a broadcast and communication environment. This issue may result in a high level of computation and the utilization of agent resources [...] Read more.
This paper addresses the communication issue encountered by a hybrid controller when finding consensus in terms of the rendezvous target point in a broadcast and communication environment. This issue may result in a high level of computation and the utilization of agent resources when a continuous communication is required by agents to meet convergence requirements. Thus, an event-triggered system was integrated into the design of a broadcast and distributed consensus linear controller using the simultaneous perturbation stochastic algorithm (SPSA). The agent’s movement towards the rendezvous point is based on the broadcast value, whereas the next agent’s state position depends on the distributed local controller output. The communication error obtained during communication between the agent and neighbors is only added to the gradient approximation error of the SPSA if the event-triggered function is violated. As a result, in our model, the number of channel utilizations was lower and the agents’ performances were preserved. The efficiencies and effectiveness of the proposed controller have been compared with the traditional sampling broadcast time-triggered (BTT) approach. The time and iterations required by the broadcast event-triggered (BET) system were less than 40.42% and 21% on average as compared to BTT. The trajectory was not the same—the BET showed scattered movements at the initial stage, whereas BTT showed a linear movement. In terms of the number of channels, 28.91% of channels were preserved during the few hundred iterations. Consequently, a variety of hybrid controllers with event-triggered mechanisms can be proposed for other multi-agent motion coordination tasks. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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19 pages, 3452 KiB  
Article
Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft
by Kyuman Lee, Daegyun Choi and Donghoon Kim
Appl. Sci. 2021, 11(7), 3103; https://0-doi-org.brum.beds.ac.uk/10.3390/app11073103 - 31 Mar 2021
Cited by 9 | Viewed by 2281
Abstract
Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes [...] Read more.
Collision avoidance (CA) using the artificial potential field (APF) usually faces several known issues such as local minima and dynamically infeasible problems, so unmanned aerial vehicles’ (UAVs) paths planned based on the APF are safe only in a certain environment. This research proposes a CA approach that combines the APF and motion primitives (MPs) to tackle the known problems associated with the APF. Since MPs solve for a locally optimal trajectory with respect to allocated time, the trajectory obtained by the MPs is verified as dynamically feasible. When a collision checker based on the k-d tree search algorithm detects collision risk on extracted sample points from the planned trajectory, generating re-planned path candidates to avoid obstacles is performed. After rejecting unsafe route candidates, one applies the APF to select the best route among the remaining safe-path candidates. To validate the proposed approach, we simulated two meaningful scenario cases—the presence of static obstacles situation with local minima and dynamic environments with multiple UAVs present. The simulation results show that the proposed approach provides smooth, efficient, and dynamically feasible pathing compared to the APF. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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15 pages, 4799 KiB  
Article
Improved Artificial Potential Field and Dynamic Window Method for Amphibious Robot Fish Path Planning
by Wenlin Yang, Peng Wu, Xiaoqi Zhou, Haoliang Lv, Xiaokai Liu, Gong Zhang, Zhicheng Hou and Weijun Wang
Appl. Sci. 2021, 11(5), 2114; https://0-doi-org.brum.beds.ac.uk/10.3390/app11052114 - 27 Feb 2021
Cited by 27 | Viewed by 3347
Abstract
Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem [...] Read more.
Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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17 pages, 493 KiB  
Article
Autonomous Underwater Vehicle Localization Using Sound Measurements of Passing Ships
by Jonghoek Kim
Appl. Sci. 2020, 10(24), 9139; https://0-doi-org.brum.beds.ac.uk/10.3390/app10249139 - 21 Dec 2020
Cited by 3 | Viewed by 1737
Abstract
This paper introduces the localization method of an Autonomous Underwater Vehicle (AUV) in environments (such as harbors or ports) where there can be passing ships near the AUV. It is assumed that the AUV can access the trajectory and approximate source level of [...] Read more.
This paper introduces the localization method of an Autonomous Underwater Vehicle (AUV) in environments (such as harbors or ports) where there can be passing ships near the AUV. It is assumed that the AUV can access the trajectory and approximate source level of a passing ship, while identifying the ship by processing the ship’s sound. This paper considers an AUV which can localize itself by integrating propeller and Inertial Measurement Units (IMU). Suppose that the AUV has been moving in underwater environments for a long time, under the IMU-only localization. To fix long-term drift in the IMU-only localization, we propose that the AUV localization uses sound measurements of passing ships whose trajectories are known a priori. As far as we know, this AUV localization method is novel in using sound measurements of passing ships of which the trajectories are known a priori. The performance of the proposed localization method is verified utilizing MATLAB simulations. The simulation results show significant estimation improvements, compared to IMU-only localization. Moreover, using measurements from multiple ships gives better estimation results, compared to the case where the measurement of a single ship is used. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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21 pages, 2992 KiB  
Article
Sampling-Based Motion Planning for Free-Floating Space Robot without Inverse Kinematics
by Hongwen Zhang and Zhanxia Zhu
Appl. Sci. 2020, 10(24), 9137; https://0-doi-org.brum.beds.ac.uk/10.3390/app10249137 - 21 Dec 2020
Cited by 8 | Viewed by 2040
Abstract
Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, [...] Read more.
Motion planning is one of the most important technologies for free-floating space robots (FFSRs) to increase operation safety and autonomy in orbit. As a nonholonomic system, a first-order differential relationship exists between the joint angle and the base attitude of the space robot, which makes it pretty challenging to implement the relevant motion planning. Meanwhile, the existing planning framework must solve inverse kinematics for goal configuration and has the limitation that the goal configuration and the initial configuration may not be in the same connected domain. Thus, faced with these questions, this paper investigates a novel motion planning algorithm based on rapidly-exploring random trees (RRTs) for an FFSR from an initial configuration to a goal end-effector (EE) pose. In a motion planning algorithm designed to deal with differential constraints and restrict base attitude disturbance, two control-based local planners are proposed, respectively, for random configuration guiding growth and goal EE pose-guiding growth of the tree. The former can ensure the effective exploration of the configuration space, and the latter can reduce the possibility of occurrence of singularity while ensuring the fast convergence of the algorithm and no violation of the attitude constraints. Compared with the existing works, it does not require the inverse kinematics to be solved while the planning task is completed and the attitude constraint is preserved. The simulation results verify the effectiveness of the algorithm. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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20 pages, 44653 KiB  
Article
Social and Robust Navigation for Indoor Robots Based on Object Semantic Grid and Topological Map
by Jiadong Zhang, Wei Wang, Xianyu Qi and Ziwei Liao
Appl. Sci. 2020, 10(24), 8991; https://0-doi-org.brum.beds.ac.uk/10.3390/app10248991 - 16 Dec 2020
Cited by 8 | Viewed by 2726
Abstract
For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor [...] Read more.
For the indoor navigation of service robots, human–robot interaction and adapting to the environment still need to be strengthened, including determining the navigation goal socially, improving the success rate of passing doors, and optimizing the path planning efficiency. This paper proposes an indoor navigation system based on object semantic grid and topological map, to optimize the above problems. First, natural language is used as a human–robot interaction form, from which the target room, object, and spatial relationship can be extracted by using speech recognition and word segmentation. Then, the robot selects the goal point from the target space by object affordance theory. To improve the navigation success rate and safety, we generate auxiliary navigation points on both sides of the door to correct the robot trajectory. Furthermore, based on the topological map and auxiliary navigation points, the global path is segmented into each topological area. The path planning algorithm is carried on respectively in every room, which significantly improves the navigation efficiency. This system has demonstrated to support autonomous navigation based on language interaction and significantly improve the safety, efficiency, and robustness of indoor robot navigation. Our system has been successfully tested in real domestic environments. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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22 pages, 1534 KiB  
Article
Event-Based Path-Planning and Path-Following in Unknown Environments for Underactuated Autonomous Underwater Vehicles
by Sergey Ulyanov, Igor Bychkov and Nikolay Maksimkin
Appl. Sci. 2020, 10(21), 7894; https://0-doi-org.brum.beds.ac.uk/10.3390/app10217894 - 07 Nov 2020
Cited by 7 | Viewed by 2831
Abstract
The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered [...] Read more.
The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board. Full article
(This article belongs to the Special Issue Advances in Robot Path Planning)
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