Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference

TL;DR

This paper presents a centralized, continuous-time trajectory generation method for multi-robot formations using sparse Gaussian Processes and probabilistic inference, enabling efficient, online adaptive planning in complex environments.

Contribution

It extends GPMP2 to multi-robot scenarios, introducing a global planner and incremental replanning for online, formation-aware trajectory generation.

Findings

Efficient trajectory generation for multi-robot formations demonstrated in simulations.

Scalable approach suitable for real-world multi-robot systems.

Online replanning enables adaptive formation changes in dynamic environments.

Abstract

In this paper, we extend a famous motion planning approach GPMP2 to multi-robot cases, yielding a novel centralized trajectory generation method for the multi-robot formation. A sparse Gaussian Process model is employed to represent the continuous-time trajectories of all robots as a limited number of states, which improves computational efficiency due to the sparsity. We add constraints to guarantee collision avoidance between individuals as well as formation maintenance, then all constraints and kinematics are formulated on a factor graph. By introducing a global planner, our proposed method can generate trajectories efficiently for a team of robots which have to get through a width-varying area by adaptive formation change. Finally, we provide the implementation of an incremental replanning algorithm to demonstrate the online operation potential of our proposed framework. The experiments in simulation and real world illustrate the feasibility, efficiency and scalability of our approach.