A1 Refereed original research article in a scientific journal
LiDAR-Based Online Control Barrier Function Synthesis for Safe Navigation in Unknown Environments
Authors: Keyumarsi, Shaghayegh; Atman, Made Widhi Surya; Gusrialdi, Azwirman
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publication year: 2024
Journal:IEEE Robotics and Automation Letters
Journal name in sourceIEEE Robotics and Automation Letters
Volume: 9
Issue: 2
First page : 1043
Last page: 1050
ISSN: 2377-3766
eISSN: 2377-3774
DOI: https://doi.org/10.1109/LRA.2023.3339059
Web address : https://doi.org/10.1109/lra.2023.3339059
Abstract
This letter presents a novel extension of the control barrier function (CBF) as the low-level safety controller for autonomous mobile robots navigating in unknown environments. The main challenges of implementing CBF in real-world situations arise from the absence of a model or the lack of an exact one for the environment. Additionally, online learning is needed for the robot to maneuver in an unknown environment which leads to dealing with the sampled data set size, memory, and computational complexity. We address these challenges by designing an online non-parametric LiDAR-based safety function using the Gaussian process (GP). It is both efficient in data size and eliminates the requirement to store previous data. Then, a CBF is synthesized using the proposed safety function to rectify the safe control input. The effectiveness of the LiDAR-based CBF synthesis for navigation in unknown environments was validated by conducting experiments on unicycle-type robots.
This letter presents a novel extension of the control barrier function (CBF) as the low-level safety controller for autonomous mobile robots navigating in unknown environments. The main challenges of implementing CBF in real-world situations arise from the absence of a model or the lack of an exact one for the environment. Additionally, online learning is needed for the robot to maneuver in an unknown environment which leads to dealing with the sampled data set size, memory, and computational complexity. We address these challenges by designing an online non-parametric LiDAR-based safety function using the Gaussian process (GP). It is both efficient in data size and eliminates the requirement to store previous data. Then, a CBF is synthesized using the proposed safety function to rectify the safe control input. The effectiveness of the LiDAR-based CBF synthesis for navigation in unknown environments was validated by conducting experiments on unicycle-type robots.
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