Decentralized trajectory optimization for multi-agent exploration
Abstract
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this paper, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents' trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.
Cite
@article{arxiv.2107.01623,
title = {Decentralized trajectory optimization for multi-agent exploration},
author = {Dimitris Gkouletsos and Andrea Iannelli and Mathias Hudoba de Badyn and John Lygeros},
journal= {arXiv preprint arXiv:2107.01623},
year = {2021}
}
Comments
8 pages, 9 figures. Accepted to the Robotics and Automation Letters and the 2021 International Conference on Intelligent Robots and Systems