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.
