  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.
