基于碰撞裕度与A-Informed RRT∗的采摘机械臂路径规划方法

Path Planning for Picking Robotic Arm Based on Collision Margin and A-Informed RRT∗

摘要:

针对蜜梨采摘机器人机械臂在非结构化果园环境中的运动路径规划与避障问题,提出了一种融合碰撞裕度约束的路径规划方法,旨在提升机械臂运动效率与作业安全性。通过建立枝条弹性偏转模型,采用三点弯曲试验测定不同直径枝条的破坏临界力,确定力碰撞阈值,并结合悬臂梁模型完成碰撞裕度的量化,将枝条最大偏转角度与垂直于轴线的最大线位移量化成碰撞裕度,构建基于八叉树与包络法的混合碰撞检测模型,并提出引入具有目标引力机制的A-Informed RRT?算法,实现允许非破坏性弹性碰撞的机械臂运动路径规划。路径规划试验表明,完全避障的A-Informed RRT?算法在三维环境中平均规划时间为1.175s,路径长度为88.463mm;结合碰撞裕度后,基于碰撞裕度的A-Informed RRT?算法平均规划时间进一步降至0.089s,路径长度缩短至85.036mm。果园采摘试验中,无遮挡场景机械臂采摘成功率达96%,细枝遮蔽场景成功率为76%,显著优于全避障场景的52%,验证了所提方法在提升机械臂运动效率与成功率方面的有效性。该方法能够在保障机械臂与植株安全的前提下,显著提高机械臂运动效率与采摘成功率,为非结构化果园下的机械臂采摘作业提供了新路径。

Abstract:

Aiming to address the motion path planning and obstacle avoidance issues of honey pear harvesting robot arms in unstructured orchard environments, a path planning method integrating collision margin constraints was proposed, aiming to improve the motion efficiency and operational safety of the robot arm. By establishing a branch elastic deflection model and using a three-point bending test to determine the critical force for destroying branches of different diameters, the force collision threshold was determined. Combined with the cantilever beam model, the collision margin was quantified by translating the maximum deflection angle of the branch and the maximum linear displacement perpendicular to the axis into a collision margin. A hybrid collision detection model based on octree and envelope methods was constructed, and an A-Informed RRT? algorithm with a target attraction mechanism was proposed, achieving robot arm motion path planning that allowed non-destructive elastic collisions. Path planning experiments showed that the fully obstacle-avoidance A-Informed RRT? algorithm had an average planning time of 1. 175 s and a path length of 88. 463 mm in a three-dimensional environment. After incorporating the collision margin, the collision-margin-based A-Informed RRT? algorithm reduced the average planning time to 0. 089 s and shortened the path length to 85. 036 mm. In orchard harvesting tests, the success rate of the robot arm in an unobstructed scenario reached 96% , while in scenarios with thin branch obstructions it was 76% , which was significantly higher than the 52% success rate in the full obstacle-avoidance scenario, validating the effectiveness of the proposed method in improving the motion efficiency and success rate of the robot arm. This method can significantly enhance the robot arm's motion efficiency and harvesting success rate while ensuring the safety of both the robot arm and the plants, providing an approach for robot arm harvesting operations in unstructured orchards.