Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference
This is an incremental improvement for multi-robot systems needing adaptive formation changes in varying environments.
The paper tackles multi-robot formation trajectory generation by extending GPMP2 to a centralized method using sparse Gaussian Processes for efficiency, and it demonstrates feasibility and scalability in simulations and real-world experiments.
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.