Hierarchical Optimization of Robotic Placement, Motion Planning, and Posture for Multi-Target Manipulation

Efficient operation of robotic manipulators in constrained workspaces requires careful coordination of robot placement, motion planning, and posture optimization. This paper presents a hierarchical optimization framework that addresses these challenges for robot placement, motion planning, and posture determination for multi-target manipulation tasks. Initially, the workspace layout is optimized using an adaptive genetic algorithm to determine robot placement that maximizes reachability and task efficiency. Subsequently, a surrogate-assisted rapidly-exploring random tree (SA-RRT) algorithm generates collision-free paths from the start to target configurations, leveraging machine learning models to reduce computational cost while preserving path quality. Finally, the robot’s posture is optimized via a second genetic algorithm to achieve a single configuration that allows rapid access to all target positions. The effectiveness of the proposed framework was evaluated through extensive simulations in a Unity-based environment, comparing SA-RRT against the conventional target-biased nearest neighbor (TNN-RRT) algorithm. In particular, the proposed SA-RRT algorithm reduces path planning computation time by 76.17% compared to TNN-RRT and also yields lower path cost, demonstrating both efficiency and path quality improvement. Overall, the framework provides a unified methodology for integrating layout planning, motion planning, and posture optimization, facilitating more effective deployment of robotic manipulators in industrial environments.