The online cooperative path planning problem is discussed for multi-quadrotor maneuvering in an unknown dynamic environment. Based on the related basic concepts, typical three-dimensional obstacle models, such as spherical and cubic, and their collision checking criteria are presented in this article. An improved rapidly exploring random tree (RRT) algorithm with goal bias and greed property is proposed based on the heuristic search strategy to overcome the shortcomings of the classical RRT algorithm. Not only are the kinematic constraints of the quadrotor established but the time and space coordination strategy matching with the improved RRT algorithm is also presented in this article. Furthermore, a novel online collision avoidance strategy according to the partial information of the surrounding environment is proposed. On the basis of the above work, a distributed online path planning strategy is proposed to obtain the feasible path for each quadrotor. Numerical simulation results show that the improved RRT algorithm has better search efficiency than the classical RRT algorithm. And the satisfactory path planning and path tracking results prove that the above model and related planning strategies are reasonable and effective.

讨论了在未知动态环境下进行多quadrotor机动的在线合作路径规划问题。基于相关的基本概念，本文介绍了典型的三维障碍物模型，例如球面和立方体，以及它们的碰撞检查标准。基于启发式搜索策略，提出了一种改进的具有目标偏差和贪婪属性的快速探索随机树算法，克服了传统RRT算法的缺点。本文不仅提出了四旋翼的运动学约束，而且提出了与改进的RRT算法相匹配的时空协调策略。此外，根据周围环境的局部信息，提出了一种新颖的在线防撞策略。在以上工作的基础上，提出了一种分布式在线路径规划策略，以获取每个四旋翼的可行路径。数值仿真结果表明，改进后的RRT算法比经典的RRT算法具有更好的搜索效率。令人满意的路径规划和路径跟踪结果证明了上述模型和相关的规划策略是合理有效的。