Risk-aware Control for Robots with Non-Gaussian Belief Spaces

This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from un-modeled dynamics and noisy sensors. To take into account these uncertainties, probabilistic state estimators are often deployed to obtain a belief over possible...

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Bibliographic Details
Published in2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 11661 - 11667
Main Authors Vahs, Matti, Tumova, Jana
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.05.2024
Subjects
Aerospace electronics
Hardware
Particle filters
Probabilistic logic
Robot sensing systems
Sensors
Uncertainty
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DOI10.1109/ICRA57147.2024.10611412

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Summary:This paper addresses the problem of safety-critical control of autonomous robots, considering the ubiquitous uncertainties arising from un-modeled dynamics and noisy sensors. To take into account these uncertainties, probabilistic state estimators are often deployed to obtain a belief over possible states. Namely, Particle Filters (PFs) can handle arbitrary non-Gaussian distributions in the robot's state. In this work, we define the belief state and belief dynamics for continuous-discrete PFs and construct safe sets in the underlying belief space. We design a controller that provably keeps the robot's belief state within this safe set. As a result, we ensure that the risk of the unknown robot's state violating a safety specification, such as avoiding a dangerous area, is bounded. We provide an open-source implementation as a ROS2 package and evaluate the solution in simulations and hardware experiments involving high-dimensional belief spaces.
DOI:10.1109/ICRA57147.2024.10611412