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：本文提出一种去中心化多智能体轨迹规划算法，该算法可在有限通信范围内保证生成安全无死锁的轨迹，适用于障碍物密集环境。该算法利用基于网格的多智能体路径规划算法解决死锁问题，并引入子目标优化方法使智能体收敛至路径规划算法生成的航点，同时避免死锁。此外，该算法通过采用线性安全走廊确保了优化问题的可行性与碰撞避免能力。实验表明，该算法在随机森林和密集迷宫环境中均不会产生死锁，且不受通信范围影响，在飞行时间与距离指标上优于本团队前期工作。最后通过十架四旋翼的硬件实物验证了算法有效性。