Abstract
In this article, we consider the problem of guaranteeing safety constraint satisfaction in human–robot collaboration (HRC) with uncertain human position. We pose this problem as a chance-constrained problem with safety (chance) constraints represented by uncertain control barrier functions, where the probability of safety constraint satisfaction under uncertainty is bounded by a tunable user-defined risk. We solve this stochastic optimization problem using a sampling-based approach and obtain a risk-tunable controller to safely accomplish HRC tasks. We demonstrate the safety and performance of this approach through both simulation and hardware experiments on a 7 degree-of-freedom Franka–Panda manipulator and characterize the tradeoff between the user-defined risk tolerance and task time efficiency in safety-critical applications.
Keywords
Safe Control, Human-Robot Collaboration, Uncertainty, Risk, Control Barrier Functions
Date of this Version
6-3-2025
Recommended Citation
Sharma, Vipul K.; Zhou, Pokuang; Xu, Zhengtong; She, Yu; and Sivaranjani, S., "Safe Human–Robot Collaboration With Risk Tunable Control Barrier Functions" (2025). School of Industrial Engineering Faculty Publications. Paper 23.
https://docs.lib.purdue.edu/iepubs/23
Comments
This is the author-accepted manuscript of V. K. Sharma, P. Zhou, Z. Xu, Y. She and S. Sivaranjani, "Safe Human–Robot Collaboration With Risk Tunable Control Barrier Functions," in IEEE/ASME Transactions on Mechatronics.
(c) 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. The version of record can be found at DOI: 10.1109/TMECH.2025.3572047.