Motion planning is critical to realize the autonomous operation of mobile robots. As the
complexity and randomness of robot application scenarios increase, the planning capability …

Learning distributed cooperative policies for large-scale multirobot systems remains a
challenging task in the multiagent reinforcement learning (MARL) context. In this work, we …

Safe and efficient cooperative planning of multiple robots in pedestrian participation
environments is promising for applications. In this paper, a novel multi-robot social-aware …

This article investigates the multirobot efficient search (MuRES) for a nonadversarial moving
target problem from the multiagent reinforcement learning (MARL) perspective. MARL is …

The cooperative navigation algorithm is the crucial technology for multirobot systems to
accomplish autonomous collaborative operations, and it is still a challenge for researchers …

Multi-agent area coverage aims to accomplish the complete traversal of the target area
through cooperation between agents. Focusing on the problems of low coverage efficiency …

Collision avoidance is a crucial technique to achieve safe and efficient robotic vehicle
navigation in unknown environments. However, moving obstacles with unpredictability in …

Effective communication among autonomous agents is crucial for coordination and solving
complex tasks within multi-agent systems. To formalize interactions between agents, social …

Improving the performance of flocking control policies in practical scenarios is of great value
in promoting the practical application of multiagent flocking collaborative control algorithms …

This paper investigates the multi-robot efficient search (MuRES) problem with a focus on
maximizing the probability of capturing a moving target within a predefined time constraint …