Task and Grasp Constrained 3D Robotic Work-Cell Layout Planning

Current robotic cellular layout planning predominantly focuses on 2D configurations, often neglecting the spatial constraints imposed by manipulated objects and robotic tasks, which can lead to impractical solutions. This research introduces a multi-objective optimization framework for 3D robotic work-cell layout planning, integrating task, grasp, and motion constraints to enhance feasibility and real-world applicability. The proposed approach formulates objectives by balancing spatial efficiency, task-aware motion planning, and grasp feasibility. To improve computational efficiency, the framework incorporates a hybrid NSGA-II and PSO optimization strategy alongside adaptive and parallel computing techniques. The methodology is validated through case studies, including randomized layout evaluations and a greenfield automotive robotic work-cell, demonstrating significant improvements in solution quality and computational efficiency. A multi-perspective evaluation, including a comparative analysis with learning-based algorithms and an assessment based on task-aware performance metrics, verifies the robustness and superiority of the proposed framework. Results confirm that incorporating task-aware constraints significantly influences layout design.