Stealthy Terrain-Aware Multi-Agent Active Search

Nikhil Angad Bakshi, Jeff Schneider

Keywords: Reconnaissance, Adversarial Search, Multi-robot, Active Learning

TL;DR: Leveraging a known terrain map and Thompson sampling based active learning to set new state of the art in search efficiency with minimal visibility risk in realistic search settings.

Abstract: Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. This problem is applicable to reconnaissance tasks wherein the safety of the search agents can be compromised as the targets may be adversarial. Prior work usually focuses either on adversarial search, where the risk of revealing the agents' location to the targets is ignored or evasion strategies where efficient search is ignored. We present the Stealthy Terrain-Aware Reconnaissance (STAR) algorithm, a multi-objective parallelized Thompson sampling-based algorithm that relies on a strong topographical prior to reason over changing visibility risk over the course of the search. The STAR algorithm outperforms existing state-of-the-art multi-agent active search methods on both rate of recovery of targets as well as minimising risk even when subject to noisy observations, communication failures and an unknown number of targets.

Student First Author: yes

Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)

Video: https://youtu.be/Fs1lv4y6Nq8

Code: https://github.com/bakshienator77/Stealthy-Terrain-Aware-Reconnaissance-and-Search