In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods.

The trajectory tracking and obstacle avoidance problems of autonomous mobile robots are typically solved through the layered planning and tracking method. However, this asynchronous method introduces a temporal lag between the planning stage and the execution of the control command. To solve this problem, this article proposes a simultaneous planning and tracking framework, which directly translates system states and obstacle information into control signals. Specifically, based on a novel model predictive control method, the two stages are integrated into a single optimal control problem. The safety constraint is modified with an elliptical obstacle model, and the predicted relative distances in a finite horizon are penalized in the objective function. These improvements ensure the feasibility of the optimal control problem and achieve the nonconservative avoidance performance. Furthermore, a triggering condition is specially developed for dynamic obstacle avoidance, ensuring that the event-triggered mechanism remains applicable even when the motion intentions of obstacles are unpredictable. Experiments are carried out on a mobile platform that is integrated with an onboard processor to validate the reliability of the proposed framework. The results show superior real-time performance, a higher success rate, and smoother operation compared to conventional methods.