CAD-Informed Uncertainty-Aware Sequence and Motion Planning for Robotic Assembly

Kurzfassung

This study addresses a multi-objective optimization problem in the planning of an uncertainty-aware sequence and motion for mechanical products with intricate structures and numerous contact areas. To generate an optimized sequence and motion that satisfies multiple conditions under mandatory requirements, we use a multi-objective optimization algorithm inspired by Non-Dominated Sorting Genetic Algorithm III, along with contact-rich robotic assembly-oriented constraints and objective functions. The proposed pipeline takes as input the CAD models of robot hardware, workspace, and assembled parts, conducts 3D geometrical and physical simulations of assembly motions, and then optimizes the assembly plan, including parts order, object placement pose, state transition, grasp, and trajectory for the real robot to execute. To obtain the uncertainty-aware sequence and motion, we incorporated a Contingent Contact-Exploring Rapidly-exploring Random Trees (ConCERRT)-based state transition planner and an objective function to evaluate the uncertainty in the multi-objective optimization algorithm. Our experiments on assembly planning for a chainsaw product demonstrated that the proposed method can generate constraint-satisfied assembly plans with a success rate of 99.2% while lowering the uncertainty in the simulations.