Technical Report: Cooperative Multi-Target Localization With Noisy Sensors

TL;DR

This paper presents a decentralized multi-robot system for localizing multiple static targets in known environments using noisy sensors, leveraging PHD filters and information-sharing to improve detection accuracy and coordination.

Contribution

It introduces a novel decentralized control scheme that combines local exploration with global information sharing via a server, enhancing multi-target localization accuracy.

Findings

Effective localization of multiple static targets achieved.

Decentralized control improves coordination among robots.

Information sharing via server enhances detection reliability.

Abstract

This technical report is an extended version of the paper 'Cooperative Multi-Target Localization With Noisy Sensors' accepted to the 2013 IEEE International Conference on Robotics and Automation (ICRA). This paper addresses the task of searching for an unknown number of static targets within a known obstacle map using a team of mobile robots equipped with noisy, limited field-of-view sensors. Such sensors may fail to detect a subset of the visible targets or return false positive detections. These measurement sets are used to localize the targets using the Probability Hypothesis Density, or PHD, filter. Robots communicate with each other on a local peer-to-peer basis and with a server or the cloud via access points, exchanging measurements and poses to update their belief about the targets and plan future actions. The server provides a mechanism to collect and synthesize information from all robots and to share the global, albeit time-delayed, belief state to robots near access points. We design a decentralized control scheme that exploits this communication architecture and the PHD representation of the belief state. Specifically, robots move to maximize mutual information between the target set and measurements, both self-collected and those available by accessing the server, balancing local exploration with sharing knowledge across the team. Furthermore, robots coordinate their actions with other robots exploring the same local region of the environment.