Collaborative Robotic Arm and Humanoid Interaction for Kitting Tasks in Simulated Factory Environment: A Simulation-Based Evaluation of Motion Planning Methods for Automated Kitting in Dynamic Industrial Environments

Abstract

Future industrial automation requires robotic systems that can operate in workspaces where objects, equipment, and humans may be present. This thesis presents the development and evaluation of a simulation-based system for motion planning in a kitting task. The system was built around a gantry-mounted UR10e robot arm with a Robotiq gripper in a simulated factory environment containing a flow rack, crates, static and dynamic obstacles. The work integrated ROS 2, Isaac Sim, MoveIt 2, and several motion-planning frameworks in a containerised software architecture. Sampling-based planning with OMPL, GPU-accelerated planning with cuMotion and cuRobo, and a hybrid planner based on cuRobo MotionGen and MPC were implemented and evaluated. The planners were tested in simple motion cases, complete pick-and-place workflows, and a dynamic obstacle benchmark where the robot had to react to a newly introduced obstacle during execution. The results showed that cuRobo provided the strongest overall balance between planning speed, success rate, and motion efficiency in the static benchmark cases. cuMotion also achieved high success rates, but generally required longer planning times. The OMPL planners were computationally cheap and could be fast in successful cases, but showed lower robustness in several scenarios. The hybrid planner was able to react to dynamic changes in the environment, but its success depended on how close the obstacle appeared to the robot. When enough clearance was available, the hybrid planner recovered reliably, while recovery became less likely when the obstacle was inserted very close to the robot. The thesis shows that simulation is a useful tool for evaluating motion-planning methods for constrained industrial tasks before real-world deployment. It also shows that GPU-accelerated and hybrid planning methods are promising for robotic operation in dynamic environments, but that further work is needed before the system can fully represent realistic human-robot collaboration.