Multi-UAV Cooperative Exploration Based on Task-Density Space Partition

doi:10.3969/j.issn.1671-0940.2024.02.004

Abstract

Abstract: This paper proposes a multi-UAV cooperative exploration approach based on task-density space partition. In the research of multi-UAV cooperative exploration, it is a prevalent cooperative scheme to control robots to work independently in partitioned spaces. Nonetheless, only considering the position of robots during space partition cannot effectively ensure the overall cooperative efficiency. According to research on task density of current time points and positions of robots during exploration, robots with fewer task points are assigned to work in spaces with more tasks in the rolling horizon optimization planning mode, which can reduce the redundancy of multi-robot cooperative work. Comparative research suggests that the overall exploration efficiency is improved.

Key words: exploration of unknown environments, multi-UAV cooperation, rolling horizon planning, task density, space partition

YU Jiafa. Multi-UAV Cooperative Exploration Based on Task-Density Space Partition[J]. AEROSPACE CHINA, 2024, 25(2): 28-35.

References

[1] Lindqvist B, Agha-Mohammadi A A, Nikolakopoulos G. Exploration-RRT: a multi-objective path planning and exploration framework for unknown and unstructured environments [C].2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 27-October 01, 2021, Prague, Czech Republic.
[2] XU Z, DENG D, Shimada K.Autonomous UAV exploration of dynamic environments via incremental sampling and probabilistic roadmap[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 27292736.
[3] Lee E M, Choi J, Lim H, et al. REAL: Rapid exploration with active loop-closing toward large-scale 3D mapping using UAVs [C].2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 27-October 01, 2021, Prague, Czech Republic.
[4] Ciesielski T, Kaufmann E, Scaramuzza D. Rapid exploration with multi-rotors: A frontier selection method for high speed flight [C].2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 24-28, 2017, Vancouver, BC, Canada.
[5] ZHOU X, ZHU J, ZHOU H, et al. Ego-swarm: a fully autonomous and decentralized quadrotor swarm system in cluttered environments [C].2021 IEEE International Conference on Robotics and Automation (ICRA). May 30-June 06, 2021, Xi’an, China.
[6] Mannucci A, Nardi S, Pallottino L. Autonomous 3D exploration of large areas: a cooperative frontier-based approach [C]. International Workshop on Modelling and Simulation for Autonomous Systems. October 24-26, 2017, Rome, Italy.
[7] HU J, NIU H, Carrasco J, et al.Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 14413-14423.
[8] Masaba K, Li A Q. GVGExp: communication-constrained multi-robot exploration system based on generalized voronoi graphs [C].2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). November 04-05, 2021, Cambridge, United Kingdom.
[9] Schmid L, Reijgwart V, Ott L, et al.A unified approach for autonomous volumetric exploration of large scale environments under severe odometry drift[J]. IEEE Robotics and Automation Letters, 2021, 6(3): 4504-4511.
[10] WANG C, CHI W, SUN Y, et al.Autonomous robotic exploration by incremental road map construction[J]. IEEE Transactions on Automation Science and Engineering, 2019, 16(4): 1720-1731.