Trajectory planning for multiple robots in shared environments is a challenging problem especially when there is limited communication available or no central entity. In this article, we present Real-time planning using Linear Spatial Separations, or RLSS: a real-time decentralized trajectory planning algorithm for cooperative multi-robot teams in static environments. The algorithm requires relatively few robot capabilities, namely sensing the positions of robots and obstacles without higher-order derivatives and the ability of distinguishing robots from obstacles. There is no communication requirement and the robots' dynamic limits are taken into account. RLSS generates and solves convex quadratic optimization problems that are kinematically feasible and guarantees collision avoidance if the resulting problems are feasible. We demonstrate the algorithm's performance in real-time in simulations and on physical robots. We compare RLSS to two state-of-the-art planners and show empirically that RLSS does avoid deadlocks and collisions in forest-like and maze-like environments, significantly improving prior work, which result in collisions and deadlocks in such environments.

翻译：在多机器人共享环境中的轨迹规划是一个极具挑战性的问题，尤其是在通信受限或无中央实体的情况下。本文提出了一种基于线性空间分离的实时规划方法（RLSS）：一种针对静态环境中协作多机器人团队的实时分布式轨迹规划算法。该算法对机器人能力要求较低，即仅需感知机器人及障碍物的位置（无需高阶导数），并能区分机器人与障碍物。算法无需通信支持，且考虑了机器人的动力学限制。RLSS生成并求解凸二次优化问题，保证运动学可行性，并在问题可解时确保无碰撞。我们通过仿真与实体机器人实验验证了该算法的实时性能。将RLSS与两种当前最优规划器进行对比，实验表明RLSS在类森林与类迷宫环境中能够有效避免死锁与碰撞，显著改进了在此类环境中易发生碰撞与死锁的既有方法。