Nonlinear Optimization-based Real-time Trajectory Planning for Cooperative Transport of Heterogeneous Mobile Manipulators

This paper proposes a real-time trajectory planning method using nonlinear optimization and whole-body control for cooperative transport with heterogeneous mobile manipulators. Unlike conventional approaches that rely on passive path following, the proposed method enables the follower robot to actively generate its own trajectory by laterally offsetting the leader’s path and refining it through nonlinear optimization. The optimization process simultaneously considers inter-robot distance maintenance, geometric consistency, and obstacle avoidance in cluttered environments. The optimized trajectory is executed through a generalized hierarchical control framework that integrates impedance-based coordination between the mobile base and manipulator. Experimental validation with two mobile manipulators—, featuring different locomotion and control characteristics—, demonstrates that the system maintains a consistent end-effector distance while adapting to environmental changes. The proposed method achieves high-fidelity trajectory tracking (average error: 0.037 m, standard deviation: 0.039 m) and maintains inter-robot spacing within a 1.05–1.65 m range for 96.09% of the task duration. The results demonstrate the framework’s relevance to real-world cooperative transport situations, emphasizing the need for adaptability, precision, and inter-robot coordination.