This paper presents a decentralized multi-agent trajectory planning (MATP) algorithm that guarantees to generate a safe, deadlock-free trajectory in an obstacle-rich environment under a limited communication range. The proposed algorithm utilizes a grid-based multi-agent path planning (MAPP) algorithm for deadlock resolution, and we introduce the subgoal optimization method to make the agent converge to the waypoint generated from the MAPP without deadlock. In addition, the proposed algorithm ensures the feasibility of the optimization problem and collision avoidance by adopting a linear safe corridor (LSC). We verify that the proposed algorithm does not cause a deadlock in both random forests and dense mazes regardless of communication range, and it outperforms our previous work in flight time and distance. We validate the proposed algorithm through a hardware demonstration with ten quadrotors.

翻译：本文提出一种分散式多智能体轨迹规划算法，能在有限通信范围和障碍丰富环境中保证生成安全无死锁的轨迹。该算法采用基于网格的多智能体路径规划算法进行死锁消解，并引入子目标优化方法使智能体能无死锁地收敛至多智能体路径规划生成的路径点。此外，通过采用线性安全走廊确保优化问题的可行性与碰撞避免。验证表明，该算法在随机森林和密集迷宫环境中无论通信范围如何均不会产生死锁，且在飞行时间与距离上优于先前工作。通过十架四旋翼的硬件演示验证了所提算法的有效性。