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（MATP）算法，在有限通信距离下，能够确保在富含障碍物的环境中生成安全且无死锁的轨迹。该算法利用基于网格的多智能体路径规划（MAPP）算法进行死锁消解，并引入子目标优化方法，使智能体收敛至MAPP生成的航路点并避免死锁。此外，算法通过采用线性安全走廊（LSC）保证了优化问题的可行性以及碰撞规避能力。我们验证了所提算法在随机森林与密集迷宫环境中均不会引发死锁（与通信距离无关），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，验证了该算法的有效性。