Li Xin, Yang Jingwen, Wang Xin, Fu Leiyang, Li Shaowen
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
Sensors (Basel). 2024 Dec 4;24(23):7759. doi: 10.3390/s24237759.
The Adaptive Step RRT* (AS-RRT*) path planning algorithm for tea-picking robotic arms was proposed as a solution to the autonomy, safety, and efficiency problems inherent to tea-picking robots in tea plantations. The algorithm employs an accumulator-based sampling point selection strategy to enhance the efficiency of path planning and the quality of the resulting path. It combines fast connectivity and pruning optimization methods to identify collision-free paths in a shorter time and to reduce the computational burden. It also incorporates a dynamic step length adjustment mechanism following collision detection, ensuring that the robot arm can avoid obstacles in real time. Furthermore, the generated paths were optimized through the introduction of redundant node removal and curve smoothing techniques. In the robotic arm motion planning experiments, the depth vision sensor was employed to obtain three-dimensional information within the tea plantation as the data source. The experimental results demonstrate that the AS-RRT* algorithm reduces the path length by 14.18% and the path planning time is less than 1 s, indicating that the proposed method enhances the efficiency of path planning and obstacle avoidance performance of the tea-picking robot arm.
提出了一种用于采茶机器人手臂的自适应步长RRT*（AS-RRT*）路径规划算法，以解决茶园采茶机器人固有的自主性、安全性和效率问题。该算法采用基于累加器的采样点选择策略，以提高路径规划效率和所得路径的质量。它结合了快速连通性和剪枝优化方法，以便在更短的时间内识别无碰撞路径并减轻计算负担。它还在碰撞检测后纳入了动态步长调整机制，确保机器人手臂能够实时避开障碍物。此外，通过引入冗余节点去除和曲线平滑技术对生成的路径进行了优化。在机器人手臂运动规划实验中，采用深度视觉传感器获取茶园内的三维信息作为数据源。实验结果表明，AS-RRT*算法使路径长度减少了14.18%，路径规划时间小于1秒，这表明所提出的方法提高了采茶机器人手臂的路径规划效率和避障性能。
