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

• Published in: 2024 IEEE International Conference on Robotics and Automation (ICRA) pp. 11661 - 11667
• Main Authors: Vahs, Matti, Tumova, Jana
• Format: Conference Proceeding
• Language: English
• Published: IEEE 13.05.2024
• Subjects: Aerospace electronics; Hardware; Particle filters; Probabilistic logic; Robot sensing systems; Sensors; Uncertainty
• DOI: 10.1109/ICRA57147.2024.10611412

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.