Risk-aware motion planning in partially known environments

Conference item

Recent trends envisage robots being deployed in areas deemed dangerous to humans, such as buildings with gas and radiation leaks. In such situations, the model of the underlying hazardous process might be unknown to the agent a priori, giving rise to the problem of planning for safe behaviour in partially known environments. We employ Gaussian process regression to create a probabilistic model of the hazardous process from local noisy samples. The result of this regression is then used by a risk metric, such as the Conditional Value-at-Risk, to reason about the safety at a certain state. The outcome is a risk function that can be employed in optimal motion planning problems. We demonstrate the use of the proposed function in two approaches. First is a sampling-based motion planning algorithm with an event-based trigger for online replanning. Second is an adaptation to the incremental Gaussian Process motion planner (iGPMP2), allowing it to quickly react and adapt to the environment. Both algorithms are evaluated in representative simulation scenarios, where they demonstrate the ability of avoiding high-risk areas.

Authors: Hawes, N; Barbosa, FS; Lacerda, B; Duckworth, P; Tumova, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Host title: Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021)
• Pages: 5220-5226
• Publication date: 2022-02-01
• Acceptance date: 2021-12-16
• Event title: 60th IEEE Conference on Decision and Control (CDC 2021)
• Event location: Austin, Texas, USA
• Event website: https://2021.ieeecdc.org/
• Event start date: 2021-12-13
• Event end date: 2021-12-17
• DOI: 10.1109/CDC45484.2021.9683744
• EISSN: 2576-2370
• ISSN: 0743-1546
• EISBN: 978-1-6654-3659-5
• ISBN: 978-1-6654-3660-1
• Language: English
• Keywords: FFR