多旋翼无人机避障航迹规划算法

多旋翼无人机的自主避障能力是安全作业的重要保证。该研究针对多旋翼无人机自主避障问题，提出了一种改进的双向RRT快速随机树航迹规划算法，结合最小化位移四阶导数的动力学优化方法，生成更符合多旋翼无人机动力学性能的避障航迹，解决避障过程中重复搜索、航迹曲率波动性大等问题，实现平稳避障；提出了以随机采样算法规划难度（用时）为核心的场景复杂度评价方法，在不同复杂度场景下进行了仿真试验。结果表明：与改进前相比，避障航迹再规划用时最多减少23.69%；有效避障航迹规划用时低于0.33 s、平均避障航迹跟踪速度大于1.12 m/s、避障航迹延长率最多达20.82%。所提出的避障航迹规划方法，提升了避障航迹的规划效率与效果，可为多旋翼无人机自主作业过程中的避障航迹规划提供参考。

Obstacle avoidance path planning algorithm for multi-rotor UAVs

Unmanned Aerial Vehicles (UAVs) have beencommonly used for the plant protectionin modern agriculture. Autonomous operation was a heated issueof UAVsdevelopment, while the obstacle avoidance is one of essential abilities. If the obstacles are not effectively avoided during the automatic operating, the security of UAVs will be inevitably at risk. This studyproposeda novel method of collision-free trajectory planning for a multi-rotor UAVs,using the modified dynamic optimization, thereby to deal with autonomous obstacle detection and avoidance. A quad-rotor UAV,Carto F4, equipped with the workloadof 5kg, was selected as the flight platform. A LIDAR, Rplidar S1, and a PIXHAWKflight controller were used on the UAV. Meanwhile, a high-speed computing module, NVIDIA TX2, was used for the complex computation. The specific method of trajectory planning consisted of three procedures during optimisation. First, a probability grid map was establishedto serve as the environment mapusing thebinary Bayesian probability. Then, an optimalbi-directional rapidly-exploring random trees (RRT) was developed to searcha complete and low-cost path for UAVs. Specifically, a systematic optimisationincluded the application of both centroid bias sampling and online-rolling optimization. The centroid bias sampling was used for the mutual guidance in thenode growing, while theonline-rolling optimization was used for the avoidance of repeated growing of nodes. Amore efficientpathswasestablishedaccording to the two step. Third, a dynamic optimization of full trajectories was applied, where the dynamic optimization of minimizing the fourth derivative of displacement was utilised to make the path to be a trajectory that was more in line with dynamic performance, thereby to achieve stable avoidance of obstacles. A minimum snap was employed during optimisation,where three types of constraintswere added, containing planning constrains, continuity constrains, and dynamic constrains. Meanwhile, a probability grid map with high and low expansions was developed to ensure that the full trajectory did not interfere with obstacle areas. An in-depth simulation test results illustrated that the re-planningduration of obstacle avoidance canbe reduced by up to 23.69%, compared with the non-improvements,indicating thatthe dynamic optimisation made the trajectory more feasible and smoother. Moreover, the duration planning of effective trajectories for obstacle avoidance was less than 0.33s, and the average speed of trajectory tracking of obstacle avoidance was larger than 1.12 m/s. In addition, the extension rate of trajectories for obstacle avoidance was up to 20.82%, indicating thatthe improvedefficiency and effectiveness of trajectory planning. The proposed method of obstacle-free trajectory planning for multi-rotor UAVs can provide a sound theoretical scheme and technical reference for the autonomous operation and obstacle avoidance of multi-rotor aircrafts.