Autonomous mobile robots are increasingly performing tasks in our daily environments, e.g., cleaning offices or order picking in supermarkets.
In such human-populated scenarios, it is crucial that these robots always navigate safely when performing their task.
Existing state-of-the-art methods can ensure safety, but often require deterministic motions of humans and may lead to conservative behavior in dense human-populated environments. This study investigates the use of time-varying control barrier functions (CBFs) and time-based rapidly exploring random trees (RRTs) to combine local safety with global task performance.
The proposed new cost function improves the trade-off of safety and task progress in densely populated scenarios.
Our results show that time-varying CBFs can perform better in terms of both task performance and safety compared to normal CBFs. Furthermore, our real-world robot experiments validate our approach to a physical nonholonomic robot.