Probabilistic Area Coverage

Abstract

This work presents an overview of our research on accounting for robot pose uncertainty in area coverage applications. In the vast majority of existing literature on robotics area coverage, the location uncertainty of the robot is not considered. An uncertain robot pose results in an uncertain sensor swath, which in turn creates uncertainty about the achieved coverage. Here, we present a general framework where pose estimates are mapped through the coverage sensor model to obtain a probability of coverage over the discretized workspace. This probabilistic representation can then be used to adaptively plan paths for coverage based on an entropy reduction formulation.
This framework is particularly well-suited to autonomous underwater vehicles (AUVs) performing seabed surveying operations. The AUV position estimate diverges from the actual AUV position while submerged due to the lack of a global position reference. This discrepancy can result in parts of the seabed being missed, which is unacceptable in safety-critical missions
such as mine countermeasures. The proposed information-based path planning approach is able to guarantee area coverage even in the case of severe AUV position estimate drift. In-water experiments with an AUV show the effectiveness of the method.

Overview

There are three important paths to consider: 1) The path you wanted to take (top right), 2) The path you think you took (bottom left), and 3) The path you actually took (bottom right).

The path you wanted to take and the path you think you took can be different if there are external disturbances (such as water currents in this case) or dynamics constraints.
The path you think you took and the path you actually take can be different because of noisy sensor data being used for vehicle localization.

The undesirable outcome is that area can be missed. In this case the blue area in the bottom left panel of the figure.

We propose a method that accounts for both sources of error. First, a probabilistic model of the coverage is maintained that accounts for the uncertainty in the robot location. Second, an adaptive planning strategy is proposed based on the probabilistic model that adjusts paths based on achieved coverage and the uncertainty.

Video

Relevant Publications

[C12] Liam Paull, Mae Seto, Howard Li. "Area coverage that accounts for pose uncertainty with an AUV surveying application." International Conference on Robotics and Automation (ICRA). 2014. [pdf] [bibtex]