Abstract:
The trajectory tracking and obstacle avoidance problems of autonomous mobile robots are typically solved through the layered planning and tracking method. However, this a...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Industrial Electronics ( Early Access )