確率的推論によるマルチロボット編隊の連続時間ガウス過程軌道生成
Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference
GPMP2をマルチロボット編隊に拡張し、疎なガウス過程と因子グラフで衝突回避・編隊維持を考慮した連続時間軌道を集中生成する手法を提案。適応的な編隊変化とオンライン再計画を実現した。
著者: Shuang Guo, Bo Liu, Shen Zhang, Jifeng Guo, Changhong Wang
分類: cs.RO
原文アブストラクト
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.
