论文标题
通过概率推断，连续时间高斯过程轨迹生成多机器人形成
Continuous-time Gaussian Process Trajectory Generation for Multi-robot Formation via Probabilistic Inference
论文作者
论文摘要
在本文中，我们将著名的运动计划方法GPMP2扩展到多机器人案例，从而为多机器人形成提供了一种新型的集中式轨迹生成方法。采用稀疏的高斯工艺模型来代表所有机器人的连续时间轨迹作为有限数量的状态，从而提高了由于稀疏性而引起的计算效率。我们增加了约束，以确保个人之间的避免碰撞以及造型维护，然后在因子图上制定了所有约束和运动学。通过引入全球规划师，我们提出的方法可以为机器人团队有效地生成轨迹，这些机器人必须通过自适应形成的变化来通过宽度变化的区域。最后，我们提供了一种增量重型算法的实现，以证明我们提出的框架的在线操作潜力。模拟和现实世界中的实验说明了我们方法的可行性，效率和可扩展性。
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.
