Automated Assembly Picking System for Cluttered Environments with Live Collision-Free Trajectory Planning

Assembly-picking requires sequence planning, object detection, pose estimation, and trajectory planning. Given a general CAD model, the existing ASAP (Automated Sequence Planning for complex Robotic Assembly with physical Feasibility) method generates a physically feasible assembly sequence. We have further optimised ASAP to address common structural challenges in LEGO assemblies. Then, we address cluttered-bin detection challenges using a voting-based strategy that reduces false positives across varying point cloud scales. Our image processing method combines best-fit and iterative closest point (ICP) techniques, enabling robust millimetre-level 3D localisation. Gripping points are identified for collision-free trajectories using the k-dimensional Tree (K-d Tree) and rapidly-exploring random trees (RRT) algorithms, and verified via a digital twin before execution. The system is validated in a Lego assembly case and benchmarked against industrial tools.