Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

By Junyan Hu, Hanlin Niu, J. Carrasco, B. Lennox, F. Arvin. IEEE Transactions on Vehicular Technology, 2020

361 citations recorded by the source corpus.

Autonomous exploration is an important application of multi-vehicle systems, where a team of networked robots are coordinated to explore an unknown environment collaboratively. This technique has earned significant research interest due to its usefulness in search and rescue, fault detection and monitoring, localization and mapping, etc. In this paper, a novel cooperative exploration strategy is proposed for multiple mobile robots, which reduces the overall task completion time and energy costs compared to conventional methods. To efficiently navigate the networked robots during the collaborative tasks, a hierarchical control architecture is designed which contains a high-level decision making layer and a low-level target tracking layer. The proposed cooperative exploration approach is developed using dynamic Voronoi partitions, which minimizes duplicated exploration areas by assigning different target locations to individual robots. To deal with sudden obstacles in the unknown environment, an integrated deep reinforcement learning based collision avoidance algorithm is then proposed, which enables the control policy to learn from human demonstration data and thus improve the learning speed and performance. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed scheme.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f8d8e192979ab5d8d593e76a0c4e2c7581778732

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Citations in the corpus

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms (2021, methodology): “[300] enhanced the learning speed of DDPG by integrating it with a prioritized experience replay algorithm.”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, methodology): “[205] designed a hierarchical control approach for cooperative exploration, combining a high-level region-assignment layer and a low-level safe-navigation layer.”
A Review of Safe Reinforcement Learning: Methods, Theory and Applications (2022, background): “transportation schedule [22, 53, 159, 253], traffic signal control [50, 59], energy management [180], wireless security [172], satellite docking [76], edge computing [299], chemical processes [241], video games [28, 153, 192, 211, 262, 300], board games of Go, shogi, chess and arcade game PAC-MAN [1”