Optimal Motion Planning for Object Picking in Industrial Contexts with Optimal Control

Abstract

This work presents a hard-constrained optimal control based motion planner for robotic manipulators operating in cluttered industrial environments. The method targets object picking tasks where cycle time, accuracy of the final pose, and collision safety are critical. The planner formulates a time-optimal trajectory generation problem with explicit constraints on collision avoidance, robot kinematics, and the accuracy with which the end-effector reaches its grasp pose. Environments, grippers, and objects are modelled using capsules, cuboids, and planes - geometric primitives commonly available in industrial robotic software - allowing flexible reconfiguration of workcells without modifying the underlying problem transcription. Two complementary initialisation strategies are proposed to reduce the computational complexity of the non-linear, non-convex optimal control problem: a geometric best-guess initialisation and a near-optimal warm-starting approach that leverages previously computed trajectories during repeated task execution. Compared to a cuRobo, the proposed CPU-only planner exhibits higher computational times but produces trajectories with 0.7x lower execution time and guarantees constraint satisfaction due to its hard-constrained formulation.The near-optimal initialisation method is shown to reduce computation times by up to 2.3x relative to the best-guess approach while simultaneously improving the success rates.