Collision-Free Trajectory Planning in Cluttered Environments for Efficient Bin Picking

Abstract

In cluttered, reconfigurable environments with varied objects, materials, and sizes—where obstacles can be added, removed, or moved—planning robot arm actions between grasps is a key challenge for traditional bin picking. We propose a trajectory planner that quickly determines how to grasp and precisely assemble customized products in reconfigurable environment. Inspired by voting and global registration methods, our strategy reduces false positives and enhances object identification accuracy, even with vastly different point cloud scales. We present an image processing technique combining best-fit and iterative closest point methods to improve system robustness, adaptability, and stability for objects of varying shapes, sizes, and materials, achieving millimeter-level 3D localization with millions of scene points. An enhanced tree method manages uneven 3D space distribution, enabling fast collision-free planning. A space configuration algorithm minimizes computational load, supporting complex tasks like liquid handling. After digital twin verification of collision-free paths, the robotic arm executes assembly tasks. This approach is validated through Lego assembly and other industrial use cases.