This work presents a decentralized motion planning framework for addressing the task of
multi-robot navigation using deep reinforcement learning. A custom simulator was …

The challenges of multi-robot navigation in dynamic environments lie in uncertainties in
obstacle complexities, partially observation of robots, and policy implementation from …

Multi-robot navigation is a challenging task in which multiple robots must be coordinated
simultaneously within dynamic environments. We apply deep reinforcement learning (DRL) …

We study the problem of multi-robot mapless navigation in the popular Centralized Training
and Decentralized Execution (CTDE) paradigm. This problem is challenging when each …

Developing a safe and efficient collision-avoidance policy for multiple robots is challenging
in the decentralized scenarios where each robot generates its paths with limited observation …

In this paper, we propose the reinforcement learning based multi-robot collision avoidance
approach by learning collision. Dynamical path re-planning, which is massively used in …

Compared with a single robot, Multi-robot Systems (MRSs) can undertake more challenging
tasks in complex scenarios benefiting from the increased transportation capacity and fault …

We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense
scenarios using deep reinforcement learning (DRL). Our approach uses local and global …

In this paper, a novel deep reinforcement learning (DRL)-based method is proposed to
navigate the robot team through unknown complex environments, where the geometric …

The challenges to solving the collision avoidance problem lie in adaptively choosing optimal
robot velocities in complex scenarios full of interactive obstacles. In this letter, we propose a …