Robust Environmental Mapping by Mobile Sensor Networks

TL;DR

This paper introduces a Bayesian decentralized mapping method for environmental parameters using mobile robots that maintains robustness against agent failures, ensuring reliable spatial mapping in hazardous scenarios.

Contribution

It proposes a novel Voronoi-based partitioning and deployment strategy for robust environmental mapping with failure-tolerant decentralized robot coordination.

Findings

Demonstrates high robustness in simulations despite robot failures

Outperforms existing methods in mapping accuracy and reliability

Effective in hazardous and failure-prone environments

Abstract

Constructing a spatial map of environmental parameters is a crucial step to preventing hazardous chemical leakages, forest fires, or while estimating a spatially distributed physical quantities such as terrain elevation. Although prior methods can do such mapping tasks efficiently via dispatching a group of autonomous agents, they are unable to ensure satisfactory convergence to the underlying ground truth distribution in a decentralized manner when any of the agents fail. Since the types of agents utilized to perform such mapping are typically inexpensive and prone to failure, this results in poor overall mapping performance in real-world applications, which can in certain cases endanger human safety. This paper presents a Bayesian approach for robust spatial mapping of environmental parameters by deploying a group of mobile robots capable of ad-hoc communication equipped with short-range sensors in the presence of hardware failures. Our approach first utilizes a variant of the Voronoi diagram to partition the region to be mapped into disjoint regions that are each associated with at least one robot. These robots are then deployed in a decentralized manner to maximize the likelihood that at least one robot detects every target in their associated region despite a non-zero probability of failure. A suite of simulation results is presented to demonstrate the effectiveness and robustness of the proposed method when compared to existing techniques.