Deep reinforcement learning (DRL) is widely applied in motion planning for multi-robot
systems as DRL leverages the offline training process to improve the real-time computation …

Dispatching and cooperative trajectory planning for multiple autonomous forklifts in a
warehouse is a widely applied research topic. The conventional methods in this domain …

Robot swarm is a hot spot in robotic research community. In this paper, we propose a
decentralized framework for car-like robotic swarm which is capable of real-time planning in …

Multi-robot teams have attracted attention from industry and academia for their ability to
perform collaborative tasks in unstructured environments, such as wilderness rescue and …

This letter focuses on the multiple nonholonomic robots motion planning (MRMP) problem in
congested and complex environments, where the complexity escalates dramatically with the …

This paper presents an efficient algorithm, naming Centralized Searching and Decentralized
Optimization (CSDO), to find feasible solution for large-scale Multi-Vehicle Trajectory …

Path planning for multiple non-holonomic robots in continuous domains constitutes a difficult
robotics challenge with many applications. Despite significant recent progress on the topic …

This work presents a distributed velocity planning strategy for multi-vehicle cooperation
along pre-defined paths. Specifically, we consider a class of tasks where multiple vehicles …

针对动态环境下移动机器人对轨迹高效规划的需求, 提出一种基于增量式候选路径集的轨迹规划
方法. 首先, 提出一种图搜索与凸优化相结合的初始候选路径集生成方法, 优化了机器人的移动 …

To address the demandforeffective mobile robot time-optimal trajectory planning in dynamic
environments, a trajectory planning approachbasedon incremental candidate path set was …