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.
翻译:在本文中,我们将著名的运动规划方法GPMP2扩大到多机器人案例,为多机器人形成产生一种新的中央轨道生成方法。一个稀有的高斯进程模型用于代表所有机器人作为少数国家的连续时间轨迹,这提高了计算效率,因为偏狭性提高了计算效率。我们增加了一些制约因素,以保证避免个人之间发生碰撞并维持形成,然后在要素图上提出所有制约因素和动脉学。通过引入一个全球规划师,我们提出的方法可以有效地为一组机器人产生轨迹,这些机器人必须通过适应性编队变化穿越宽宽幅区域。最后,我们提供了一种渐进式再规划算法,以展示我们拟议框架的在线操作潜力。模拟和现实世界的实验展示了我们方法的可行性、效率和可扩展性。