Obstacle-Aware and High-Reach Path Planning for Robotic Manipulators in Complex Factory Farming Environments

Abstract

This article presents a novel path planning approach for robotic manipulators operating in complex factory farming environments, where traditional methods struggle with strict obstacle avoidance constraints. The proposed method strikes a balance between minor permissible collisions and efficient obstacle avoidance. First, scene point clouds are downsampled using voxelization to generate a cost map. A greedy search is then employed to determine an initial obstacle-aware Cartesian path from this map. After postprocessing, the Cartesian path is converted into joint configuration trajectories using Ranged-IK, ensuring smooth, singularity-free transitions with controlled flexibility. The resulting validated trajectories are executed by the manipulator. Experiments were conducted on two robotic manipulators for pollination and harvesting tasks. The results indicate that the proposed method outperforms common alternatives, achieving higher operational efficiency, success rates, and adaptability, while permitting minor collisions.