Probabilistic cooperative mobile robot area coverage and its application to autonomous seabed mapping

Abstract

There are many applications that require mobile robots to autonomously cover an entire area with a sensor or end effector. The vast majority of the literature on this subject is focused on addressing path planning for area coverage under the assumption that the robot’s pose is known or that error is bounded. In this work, we remove this assumption and develop a completely probabilistic representation of coverage. We show that coverage is guaranteed as long as the robot pose estimates are consistent, a much milder assumption than zero or bounded error. After formally connecting robot sensor uncertainty with area coverage, we propose an adaptive sliding window filter pose estimator that provides a close approximation to the full maximum a posteriori estimate with a computation cost that is bounded over time. Subsequently, an adaptive planning strategy is presented that automatically exploits conditions of low vehicle uncertainty to more efficiently cover an area. We further extend this approach to the multi-robot case where robots can communicate through a (possibly faulty and low-bandwidth) channel and make relative measurements of one another. In this case, area coverage is achieved more quickly since the uncertainty over the robots’ trajectories is reduced. We apply the framework to the scenario of mapping an area of seabed with an autonomous underwater vehicle. Experimental results support the claim that our method achieves guaranteed complete coverage notwithstanding poor navigational sensors and that resulting path lengths required to cover the entire area are shortest using the proposed cooperative and adaptive approach.