Toward a Generic Framework for Mission Planning and Execution with a Heterogeneous Multi-Robot System

被引:0
作者
Denguir, Mohsen [1 ]
Touir, Ameur [2 ]
Gazdar, Achraf [1 ]
Qasem, Safwan [3 ]
机构
[1] King Saud Univ, Coll Comp & Informat Sci, Dept Software Engn, Riyadh 11543, Saudi Arabia
[2] King Saud Univ, Coll Comp & Informat Sci, Dept Comp Sci, Riyadh 11543, Saudi Arabia
[3] Al Yamamah Univ, Coll Engn, Dept Comp Engn, Riyadh 11512, Saudi Arabia
关键词
heterogeneous multi-robot systems; UGV; UAV; mission planning; decentralized control; formation control; formation stability; task allocation;
D O I
10.3390/s24216881
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework's architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.
引用
收藏
页数:25
相关论文
共 25 条
[1]   Decentralized planning and control for UAV-UGV cooperative teams [J].
Arbanas, Barbara ;
Ivanovic, Antun ;
Car, Marko ;
Orsag, Matko ;
Petrovic, Tamara ;
Bogdan, Stjepan .
AUTONOMOUS ROBOTS, 2018, 42 (08) :1601-1618
[2]  
bitcraze, Bitcraze Crazyflie 2.1
[3]   Optimizing UAV-UGV coalition operations: A hybrid clustering and multi-agent reinforcement learning approach for path planning in obstructed environment [J].
Brotee, Shamyo ;
Kabir, Farhan ;
Razzaque, Md. Abdur ;
Roy, Palash ;
Mamun-Or-Rashid, Md. ;
Hassan, Md. Rafiul ;
Hassan, Mohammad Mehedi .
AD HOC NETWORKS, 2024, 160
[4]   Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning [J].
Dai, Yanyan ;
Kim, Deokgyu ;
Lee, Kidong .
ELECTRONICS, 2024, 13 (15)
[5]   Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments [J].
de Castro, Gabriel G. R. ;
Santos, Tatiana M. B. ;
Andrade, Fabio A. A. ;
Lima, Jose ;
Haddad, Diego B. ;
Honorio, Leonardo de M. ;
Pinto, Milena F. .
MACHINES, 2024, 12 (03)
[6]   End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System [J].
Elfakharany, Ahmed ;
Ismail, Zool Hilmi .
APPLIED SCIENCES-BASEL, 2021, 11 (07)
[7]   Role and task allocation framework for Multirobot collaboration with latent knowledge estimation [J].
Gianni, Mario ;
Uddin, Mohammad Salah .
ENGINEERING REPORTS, 2020, 2 (09)
[8]  
Giernacki W, 2017, 2017 22ND INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN AUTOMATION AND ROBOTICS (MMAR), P37, DOI 10.1109/MMAR.2017.8046794
[9]   End-to-end decentralized formation control using a graph neural network-based learning method [J].
Jiang, Chao ;
Huang, Xinchi ;
Guo, Yi .
FRONTIERS IN ROBOTICS AND AI, 2023, 10
[10]   A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning [J].
Lei, Tingjun ;
Chintam, Pradeep ;
Luo, Chaomin ;
Liu, Lantao ;
Jan, Gene Eu .
SENSORS, 2023, 23 (11)