This paper presents a new online multi-agent trajectory planning algorithm that guarantees to generate safe, dynamically feasible trajectories in a cluttered environment. The proposed algorithm utilizes a linear safe corridor (LSC) to formulate the distributed trajectory optimization problem with only feasible constraints, so it does not resort to slack variables or soft constraints to avoid optimization failure. We adopt a priority-based goal planning method to prevent the deadlock without an additional procedure to decide which robot to yield. The proposed algorithm can compute the trajectories for 60 agents on average 15.5 ms per agent with an Intel i7 laptop and shows a similar flight distance and distance compared to the baselines based on soft constraints. We verified that the proposed method can reach the goal without deadlock in both the random forest and the indoor space, and we validated the safety and operability of the proposed algorithm through a real flight test with ten quadrotors in a maze-like environment.
翻译:本文介绍了一个新的在线多试剂轨迹规划算法,该算法保证在混乱的环境中产生安全、动态可行的轨道。提议的算法使用线性安全走廊(LSC)来提出分布式轨迹优化问题,但只有可行的限制,因此它不会采用松懈的变数或软性限制来避免优化失败。我们采用了基于优先目标的目标规划法来防止僵局,而没有另外的程序来决定要交出哪个机器人。提议的算法可以计算60个试剂的轨迹,平均每个试剂15.5毫秒,使用Intel i7膝上型电脑,显示与基于软性限制的基线相近的飞行距离和距离。我们核实,拟议的方法可以在随机森林和室内空间不陷入僵局的情况下达到目标,我们通过在类似磁铁环境下与十个四重器进行实际飞行试验,验证了拟议算法的安全和可操作性。