Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
4.8
Journals
Drones
Special Issues
Drone Mission Planning
share announcement

Drone Mission Planning

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 9935

Special Issue Editor

Dr. Cristian Ramírez Atencia

E-Mail Website
Guest Editor
Department of Computer Systems, Polytechnic University of Madrid, 28031 Madrid, Spain
Interests: multi-objective optimization; constraint programming; bio-inspired algorithms; multi-criteria decision making; planning and scheduling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

We are pleased to invite you to submit your papers to this Drones Special Issue on Drones Mission Planning.

Drones, also called unmanned aerial vehicles (UAV), are extremely popular and useful for many tasks. In the last decade, the use of UAVs has become very popular, and it is expected to grow even more over the coming years. This growth is due to interest from both the industrial and research communities. Industries, such as agriculture or forestry, are attracted by the different potential applications, which include surveillance, disaster, and crisis management; while the research community is also interested in UAV due to the challenging problems that must be faced by different fields such as human–machine interfaces, augmented reality, and mission planning.

The field of mission planning has expanded substantially in recent years, due to the boom of artificial intelligence methods and their applications to different real-life problems, including mission planning for UAVs. In this context, different methods including black-box optimization techniques (including metaheuristics) and decision-making methods have been widely used to tackle the complexity of the mission planning problem, where one or several UAVs must perform different tasks in some geographic area within a specific time interval.

One of the most challenging problems to face in this topic is the effort required for replanning a mission in real-time. Very few works have been published in this area, due to the difficulty of the problem and the limitations in the computational power of current UAV systems. Nevertheless, advancement in this area is a key point to reduce the workload of UAV operators.

Within this context, we invite papers focusing on current advances in the area of mission planning and replanning for UAVs.

Related References:

Karaman, S. and Frazzoli, E. “Linear temporal logic vehicle routing with applications to multi‐UAV mission planning.” Int. J. Robust Nonlinear Control (2011) 21: 1372. https://0-doi-org.brum.beds.ac.uk/10.1002/rnc.1715

Evers, L., Dollevoet, T., Barros, A.I., et al. “Robust UAV mission planning.” In: Annals of Operations Research (2014) 222: 293. https://0-doi-org.brum.beds.ac.uk/10.1007/s10479-012-1261-8

Ramirez-Atencia, C., Bello-Orgaz, G., R-Moreno, M.D. et al. “Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms.” In: Soft Computing (2017) 21: 4883. https://0-doi-org.brum.beds.ac.uk/10.1007/s00500-016-2376-7

Qiao, Y., Yang, J., Zhang, Q. et al. "Multi-UAV Cooperative Patrol Task Planning Novel Method Based on Improved PFIH Algorithm." In: IEEE Access (2019) 7: 167621. https://0-doi-org.brum.beds.ac.uk/10.1109/ACCESS.2019.2952877

Dr. Cristian Ramírez Atencia
Guest Editor

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. Drones is an international peer-reviewed open access monthly 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

  • Application of artificial intelligence methods for the resolution of the UAV mission planning problem
  • Techniques for the minimization of path planning in surveillance and target detection missions
  • Black-box optimization methods and metaheuristics applied to the resolution of mission planning problems
  • Multi-agent and swarm systems for the coordination and planning of missions
  • Large-scale aerial datasets and standardized benchmarks of missions for the application of planning algorithms
  • Mission planning in uncertain and dynamic environments
  • Robust mission planning and mission replanning for UAVs
  • Decision-making techniques for the selection of mission plans

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 15599 KiB  
Article
Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles
by Christoph Steup, Simon Parlow, Sebastian Mai and Sanaz Mostaghim
Drones 2020, 4(4), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/drones4040063 - 25 Sep 2020
Cited by 6 | Viewed by 3890
Abstract
The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy [...] Read more.
The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%. Full article
(This article belongs to the Special Issue Drone Mission Planning)
►▼ Show Figures

Graphical abstract

19 pages, 1192 KiB  
Article
Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones
by Amin Majd, Mohammad Loni, Golnaz Sahebi and Masoud Daneshtalab
Drones 2020, 4(3), 48; https://0-doi-org.brum.beds.ac.uk/10.3390/drones4030048 - 26 Aug 2020
Cited by 8 | Viewed by 4232
Abstract
Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should [...] Read more.
Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions. Full article
(This article belongs to the Special Issue Drone Mission Planning)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop