Efficient Multi-Robot Exploration with Energy Constraint based on Optimal Transport Theory

Kabir, Rabiul Hasan; Lee, Kooktae

doi:10.48550/arxiv.2009.00862

Cited by 1 publication

(1 citation statement)

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“…As time passed, all aspects of multirobot systems began to be considered. Whether it is task assignment from the top level or motor control learning from the bottom level, the complexity of multirobot systems has called new challenges to roboticists, ranging from multirobot task allocation [21], [22], path planning [23], [24] to exploration [25], [26]. However, except for limited works on heterogeneous settings (e.g., [2], [13], [27], [28], [29]), most of the multirobot problems focused on homogeneous settings.…”

Section: Related Workmentioning

confidence: 99%

Asymmetric Self-Play-Enabled Intelligent Heterogeneous Multirobot Catching System Using Deep Multiagent Reinforcement Learning

Gao

Chen

et al. 2023

IEEE Trans. Robot.

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Aiming to develop a more robust and intelligent heterogeneous system for adversarial catching in security and rescue tasks, in this article, we discuss the specialities of applying asymmetric self-play and curriculum learning techniques to deal with the increasing heterogeneity and number of different robots in modern heterogeneous multirobot systems (HMRS). Our method, based on actor-critic multiagent reinforcement learning, provides a framework that can enable cooperative behaviors among heterogeneous multirobot teams. This leads to the development of an HMRS for complex catching scenarios that involve several robot teams and real-world constraints. We conduct simulated experiments to evaluate different mechanisms' influence on our method's performance, and real-world experiments to assess our system's performance in complex real-world catching problems. In addition, a bridging study is conducted to compare our method with a state-of-the-art method called S2M2 in heterogeneous catching problems, and our method performs better in adversarial settings. As a result, we show that the proposed framework, through fusing asymmetric self-play and curriculum learning during training, is able to successfully complete the HMRS catching task under realistic constraints in both simulation and the real world, thus providing a direction for future large-scale intelligent security & rescue HMRS.

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