A Model Predictive Control Approach to Trajectory Tracking with Human-Robot Collision Avoidance
Santer P, Völz A, Graichen K (2025)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2025
Event location: San Diego
Abstract
Human-robot collision avoidance plays an essential role in facilitating the integration of robotic systems. However, many state-of-the-art approaches do not consider the future movements of dynamic obstacles or neglect the importance of allowing trajectory tracking behavior. To this end, a method based on model predictive control (MPC) and dynamic obstacle prediction is proposed that features trajectory tracking with minimal error in obstacle-free cases. Otherwise, collisions with dynamic obstacles are avoided by either only adapting the speed or by additional local deviations from the path while quickly recovering to a low tracking error afterwards. This MPC formulation is applied to an omnidirectional mobile robot within simulations and real-world experiments that demonstrate the effectiveness of the proposed approach with respect to tracking accuracy and human safety.
Authors with CRIS profile
Philipp Santer
Chair of Automatic Control (LRT/AC)
Andreas Völz
Chair of Automatic Control (LRT/AC)
Knut Graichen
Chair of Automatic Control (LRT/AC)
Related research project(s)
How to cite
APA:
Santer, P., Völz, A., & Graichen, K. (2025). A Model Predictive Control Approach to Trajectory Tracking with Human-Robot Collision Avoidance. In Proceedings of the 2025 IEEE Conference on Control Technology and Applications (CCTA). San Diego, US.
MLA:
Santer, Philipp, Andreas Völz, and Knut Graichen. "A Model Predictive Control Approach to Trajectory Tracking with Human-Robot Collision Avoidance." Proceedings of the 2025 IEEE Conference on Control Technology and Applications (CCTA), San Diego 2025.
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