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

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