Risk-Aware Motion Planning in Partially Known Environments

TL;DR

This paper introduces a risk-aware motion planning framework for robots operating in partially known hazardous environments, utilizing Gaussian processes and risk metrics to ensure safety and adaptability.

Contribution

It presents a novel integration of Gaussian process regression with risk metrics for safe motion planning in unknown environments, including two adaptive planning algorithms.

Findings

Algorithms effectively avoid high-risk areas in simulations

The approach enables quick adaptation to environmental changes

Demonstrates improved safety in hazardous scenarios

Abstract

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.