Abstract—Autonomous docking of mobile robots presents significant control challenges due to nonholonomic constraints and physical limitations. This paper addresses these challenges with a dual-controller hybrid framework that achieves precise posture stabilization while respecting system constraints. The first component is an analytically derived time-invariant controller based on a polar coordinate transformation of the robot's kinematic model, enabling bidirectional motion that enhances maneuverability. The second component employs a Model Predictive Control (MPC) approach implemented as a Pointwise Min-Norm (PMN) controller that explicitly handles actuator constraints while tracking references generated by the first controller. This constrained optimization is formulated as a quadratic programming problem that maintains the stabilization properties of the reference signals while satisfying velocity and acceleration limits. Extensive validation through simulations and experiments demonstrates performance improvements over conventional methods, with particular emphasis on practical docking scenarios requiring specific pose constraints. The close alignment between theoretical predictions and experimental results confirms the framework’s robustness and applicability to real-world docking operations.

Keywords—autonomous docking, posture stabilization, time-invariant, autonomous mobile robots

Cite: Dinh-Quan Nguyen, "A Hybrid Posture Stabilization Approach for the Docking Problem of Autonomous Mobile Robots," International Journal of Mechanical Engineering and Robotics Research, Vol. 14, No. 6, pp. 582-593, 2025. doi: 10.18178/ijmerr.14.6.582-593

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