Multi-robot Social-aware Cooperative Planning in Pedestrian Environments Using Multi-agent Reinforcement Learning

Safe and efficient co-planning of multiple robots in pedestrian participation environments is promising for applications. In this work, a novel multi-robot social-aware efficient cooperative planner that on the basis of off-policy multi-agent reinforcement learning (MARL) under partial dimension-varying observation and imperfect perception conditions is proposed. We adopt temporal-spatial graph (TSG)-based social encoder to better extract the importance of social relation between each robot and the pedestrians in its field of view (FOV). Also, we introduce K-step lookahead reward setting in multi-robot RL framework to avoid aggressive, intrusive, short-sighted, and unnatural motion decisions generated by robots. Moreover, we improve the traditional centralized critic network with multi-head global attention module to better aggregates local observation information among different robots to guide the process of individual policy update. Finally, multi-group experimental results verify the effectiveness of the proposed cooperative motion planner.

Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots

In this paper, a novel hybrid multi-robot motion planner that can be applied under non-communication and local observable conditions is presented. The planner is model-free and can realize the end-to-end mapping of multi-robot state and observation information to final smooth and continuous trajectories. The planner is a front-end and back-end separated architecture. The design of the front-end collaborative waypoints searching module is based on the multi-agent soft actor-critic algorithm under the centralized training with decentralized execution diagram. The design of the back-end trajectory optimization module is based on the minimal snap method with safety zone constraints. This module can output the final dynamic-feasible and executable trajectories. Finally, multi-group experimental results verify the effectiveness of the proposed motion planner.

A review of mobile robot motion planning methods: from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and stochasticity of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. In recent years, with the development of intelligent computation technology, the deep reinforcement learning (DRL) based motion planning algorithm has gradually become a research hotspot due to its advantageous features such as not relying on the map prior, model-free, and unified global and local planning paradigms. In this paper, we provide a systematic review of various motion planning methods. First, we summarize the representative and cutting-edge algorithms for each submodule of the classical motion planning architecture and analyze their performance limitations. Subsequently, we concentrate on reviewing RL-based motion planning approaches, including RL optimization motion planners, map-free end-to-end methods that integrate sensing and decision-making, and multi-robot cooperative planning methods. Last but not least, we analyze the urgent challenges faced by these mainstream RL-based motion planners in detail, review some state-of-the-art works for these issues, and propose suggestions for future research.