Coordinating the motion of multiple robots in cluttered environments remains a computationally challenging task. We study the problem of minimizing the execution time of a set of geometric paths by a team of robots with state-dependent actuation constraints. We propose a Time-Optimal Path Parameterization (TOPP) algorithm for multiple car-like agents, where the modulation of the timing of every robot along its assigned path is employed to ensure collision avoidance and dynamic feasibility. This is achieved through the use of a priority queue to determine the order of trajectory execution for each robot while taking into account all possible collisions with higher priority robots in a spatiotemporal graph. We show a 10-20% reduction in makespan against existing state-of-the-art methods and validate our approach through simulations and hardware experiments.

翻译：在杂乱环境中协调多个机器人的运动仍然是一个计算上具有挑战性的任务。我们研究了在具有状态相关驱动约束的机器人团队中，最小化一组几何路径执行时间的问题。我们提出了一种适用于多个类车智能体的时间最优路径参数化算法，该算法通过调制每个机器人沿其分配路径的时序来确保无碰撞和动态可行性。这是通过使用优先级队列来确定每个机器人的轨迹执行顺序，同时在时空图中考虑与所有更高优先级机器人可能发生的碰撞来实现的。实验表明，与现有最先进方法相比，我们的方法在总完工时间上实现了10-20%的缩减，并通过仿真和硬件实验验证了我们的方法。