Decentralized multi-agent active search for sparse signals

Ramina Ghods, Arundhati Banerjee, Jeff Schneider
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:696-706, 2021.

Abstract

Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions. In this paper, we are focusing on multiple aerial robots (agents) detecting targets such as gas leaks, radiation sources or human survivors of disasters. One of the main challenges of active search with multiple agents in unknown environments is impracticality of central coordination due to the difficulties of connectivity maintenance. In this paper, we propose two distinct active search algorithms that allow for multiple robots to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common sensing algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at a time, we make a realistic assumption that each agent can only sense a contiguous region of space at each time step. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-ghods21a, title = {Decentralized multi-agent active search for sparse signals}, author = {Ghods, Ramina and Banerjee, Arundhati and Schneider, Jeff}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {696--706}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/ghods21a/ghods21a.pdf}, url = {https://proceedings.mlr.press/v161/ghods21a.html}, abstract = {Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions. In this paper, we are focusing on multiple aerial robots (agents) detecting targets such as gas leaks, radiation sources or human survivors of disasters. One of the main challenges of active search with multiple agents in unknown environments is impracticality of central coordination due to the difficulties of connectivity maintenance. In this paper, we propose two distinct active search algorithms that allow for multiple robots to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common sensing algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at a time, we make a realistic assumption that each agent can only sense a contiguous region of space at each time step. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.} }
Endnote
%0 Conference Paper %T Decentralized multi-agent active search for sparse signals %A Ramina Ghods %A Arundhati Banerjee %A Jeff Schneider %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-ghods21a %I PMLR %P 696--706 %U https://proceedings.mlr.press/v161/ghods21a.html %V 161 %X Active search refers to the problem of efficiently locating targets in an unknown environment by actively making data-collection decisions. In this paper, we are focusing on multiple aerial robots (agents) detecting targets such as gas leaks, radiation sources or human survivors of disasters. One of the main challenges of active search with multiple agents in unknown environments is impracticality of central coordination due to the difficulties of connectivity maintenance. In this paper, we propose two distinct active search algorithms that allow for multiple robots to independently make data-collection decisions without a central coordinator. Throughout we consider that targets are sparsely located around the environment in keeping with compressive sensing assumptions and its applicability in real world scenarios. Additionally, while most common sensing algorithms assume that agents can sense the entire environment (e.g. compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at a time, we make a realistic assumption that each agent can only sense a contiguous region of space at each time step. We provide simulation results as well as theoretical analysis to demonstrate the efficacy of our proposed algorithms.
APA
Ghods, R., Banerjee, A. & Schneider, J.. (2021). Decentralized multi-agent active search for sparse signals. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:696-706 Available from https://proceedings.mlr.press/v161/ghods21a.html.

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