SYMAROSep 2, 2020

Efficient Multi-Robot Exploration with Energy Constraint based on Optimal Transport Theory

arXiv:2009.00862v11 citations
Originality Incremental advance
AI Analysis

This work addresses energy-efficient exploration for multi-robot systems, applicable to heterogeneous platforms, but it is incremental as it builds on existing optimal transport methods.

The paper tackles the problem of efficient multi-robot exploration under energy constraints by using optimal transport theory to prioritize areas of interest, and it develops centralized and decentralized algorithms with performance bounds validated through simulations.

This paper addresses an Optimal Transport (OT)-based efficient multi-robot exploration problem, considering the energy constraints of a multi-robot system. The efficiency in this problem implies how a team of robots (agents) covers a given domain, reflecting a priority of areas of interest represented by a density distribution, rather than simply following a preset of uniform patterns. To achieve an efficient multi-robot exploration, the optimal transport theory that quantifies a distance between two density distributions is employed as a tool, which also serves as a means of performance measure. The energy constraints for the multi-robot system is then incorporated into the OT-based multi-robot exploration scheme. The proposed scheme is decoupled from robot dynamics, broadening the applicability of the multi-robot exploration plan to heterogeneous robot platforms. Not only the centralized but also decentralized algorithms are provided to cope with more realistic scenarios such as communication range limits between agents. To measure the exploration efficiency, the upper bound of the performance is developed for both the centralized and decentralized cases based on the optimal transport theory, which is computationally tractable as well as efficient. The proposed multi-robot exploration scheme is also applicable to a time-varying distribution, where the spatio-temporal evolution of the given reference distribution is desired. To validate the proposed method, multiple simulation results are provided.

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