Search-based Planning for Active Sensing in Goal-Directed Coverage Tasks

Kusnur, Tushar; Saxena, Dhruv Mauria; Likhachev, Maxim

doi:10.48550/arxiv.2011.07383

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent advances in sensing and perception have enabled the deployment of mobile robots in unknown environments for information gathering missions such as environmental monitoring [1], [2], surveillance [3], coverage [4], [5], target tracking [6], [7] and active-SLAM [8], [9]. These tasks require spatio-temporal information collection, which can be achieved more efficiently by multi-robot systems, rather than relying on individual robots.…”

Section: Introductionmentioning

confidence: 99%

Technical Report: Distributed Sampling-based Planning for Non-Myopic Active Information Gathering

Tzes,

Kantaros,

Pappas

2021

Preprint

View full text Add to dashboard Cite

This paper addresses the problem of active information gathering for multi-robot systems. Specifically, we consider scenarios where robots are tasked with reducing uncertainty of dynamical hidden states evolving in complex environments. The majority of existing information gathering approaches are centralized and, therefore, they cannot be applied to distributed robot teams where communication to a central user is not available. To address this challenge, we propose a novel distributed sampling-based planning algorithm that can significantly increase robot and target scalability while decreasing computational cost. In our non-myopic approach, all robots build in parallel local trees exploring the information space and their corresponding motion space. As the robots construct their respective local trees, they communicate with their neighbors to exchange and aggregate their local beliefs about the hidden state through a distributed Kalman filter. We show that the proposed algorithm is probabilistically complete and asymptotically optimal. We provide extensive simulation results that demonstrate the scalability of the proposed algorithm and that it can address large-scale, multi-robot information gathering tasks, that are computationally challenging for centralized methods.

show abstract