Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

A flexible active safety motion (FASM) control approach is proposed for collision avoidance in robot manipulators. The key feature is the use of control barrier functions (CBFs) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, providing both flexibility and active safety in dynamic environments for robots. First, discrete-time CBFs are utilized to formulate the new flexible CBFSC with dynamic decay rates, which is then integrated into the model predictive control (MPC) framework. Notably, the decay rates of the CBFSC are incorporated as decision variables, allowing for dynamic adaptability during obstacle avoidance. In addition, a new cost function with an integrated penalty term is designed to dynamically adjust the safety margins. Finally, experiments in various scenarios using a Universal Robots 5 (UR5) manipulator validate the effectiveness of the proposed approach.

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Publication Details

Journal
IEEE Robotics and Automation Letters
Published
2025-01-27
DOI
https://doi.org/10.1109/lra.2025.3534519
Citations
17
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
21.69

Funders

Controls
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article

Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

Jianliang Mao, Jun Yang, Xiangyu Wang, Shihua Li et al.
17 citations
IEEE Robotics and Automation Letters
Robotic Path Planning Algorithms
21.69
article

Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach

Jianliang Mao, Jun Yang, Xiangyu Wang, Shihua Li, Yimeng Li, Jinhao Liu, Tianqi Zhu, Qihang Xie
article en
17 citations

Abstract

A flexible active safety motion (FASM) control approach is proposed for collision avoidance in robot manipulators. The key feature is the use of control barrier functions (CBFs) to design flexible CBF-guided safety criteria (CBFSC) with dynamically optimized decay rates, providing both flexibility and active safety in dynamic environments for robots. First, discrete-time CBFs are utilized to formulate the new flexible CBFSC with dynamic decay rates, which is then integrated into the model predictive control (MPC) framework. Notably, the decay rates of the CBFSC are incorporated as decision variables, allowing for dynamic adaptability during obstacle avoidance. In addition, a new cost function with an integrated penalty term is designed to dynamically adjust the safety margins. Finally, experiments in various scenarios using a Universal Robots 5 (UR5) manipulator validate the effectiveness of the proposed approach.

IEEE Robotics and Automation LettersVol. 10(3)
Loughborough University (GB), Shanghai University of Electric Power (CN), Southeast University (CN)
National Natural Science Foundation of China, China Scholarship Council
Openalex Percentile: Top 0%
Robotic Path Planning Algorithms
21.69
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Flexible Active Safety Motion Control for Robotic Obstacle Avoidance: A CBF-Guided MPC Approach — Jianliang Mao, Jun Yang, et al. · IEEE Robotics and Automation Letters (2025) | TGRS Research Map | TGRS