Force-based Algorithm for Motion Planning of Large Agent Teams
This addresses efficient and scalable motion planning for autonomous agents in dense environments, though it appears incremental as it builds on force-based approaches.
The paper tackles motion planning for large teams of agents by proposing a distributed force-based algorithm that uses only relative position information to generate collision-free trajectories, achieving lower transition times and computational overhead compared to existing methods.
This paper presents a distributed, efficient, scalable and real-time motion planning algorithm for a large group of agents moving in 2 or 3-dimensional spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: collision avoidance and navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm is able to find collision-free motions with lower transition time, free from velocity state information of neighbouring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2D and 3D benchmark simulation scenarios, with results outperforming existing methods.