论文标题
具有非线性动态和有限扰动的可扩展且安全的多代理运动计划
Scalable and Safe Multi-Agent Motion Planning with Nonlinear Dynamics and Bounded Disturbances
论文作者
论文摘要
我们提出了可扩展有效的多机构安全运动计划者，使一组代理能够转移到所需的位置，同时避免与障碍物和其他代理发生碰撞，并存在丰富的障碍，高维，非线性，非实体性动力学，非独立动态，驱动限制和干扰。我们通过找到每个代理的分段线性路径来解决此问题，以确保按照这些路径的实际轨迹满足触及和避免的要求。我们表明，可以预先计算任何因驱动限制而导致的每个路径段的最小持续时间的合格路径，因此可以预先计算受控药物的实际轨迹的空间跟踪误差。使用这些边界，我们通过使用基于优先级的搜索来求解混合的整数线性程序和协调代理，从而为每个代理找到无冲突的路径。我们通过分别使用地面车辆和四型尺寸在2D和3D方案中进行基准测试，与两个最先进的多代理运动计划者相比，在2D和3D方案中进行基准测试。
We present a scalable and effective multi-agent safe motion planner that enables a group of agents to move to their desired locations while avoiding collisions with obstacles and other agents, with the presence of rich obstacles, high-dimensional, nonlinear, nonholonomic dynamics, actuation limits, and disturbances. We address this problem by finding a piecewise linear path for each agent such that the actual trajectories following these paths are guaranteed to satisfy the reach-and-avoid requirement. We show that the spatial tracking error of the actual trajectories of the controlled agents can be pre-computed for any qualified path that considers the minimum duration of each path segment due to actuation limits. Using these bounds, we find a collision-free path for each agent by solving Mixed Integer-Linear Programs and coordinate agents by using the priority-based search. We demonstrate our method by benchmarking in 2D and 3D scenarios with ground vehicles and quadrotors, respectively, and show improvements over the solving time and the solution quality compared to two state-of-the-art multi-agent motion planners.
