Classification-Aware Path Planning of Network of Robots

Authors

Guangyi Liu, Lehigh University
Arash Amini, Lehigh UniversityFollow
Martin Takac, Mohamed bin Zayed University of Artificial IntelligenceFollow
Nader Motee, Lehigh University

Document Type

Article

Publication Title

International Symposium Distributed Autonomous Robotic Systems

Abstract

We propose a classification-aware path planning architecture for a team of robots in order to traverse along the most informative paths with the objective of completing map classification tasks using localized (partial) observations from the environment. In this method, the neural network layers with parallel structure utilize each agent’s memorized history and solve the path planning problem to achieve classification. The objective is to avoid visiting less informative regions and significantly reduce the total energy cost (e.g., battery life) when solving the classification problem. Moreover, the parallel design of the path planning structure reduces the training complexity drastically. The efficacy of our approach has been validated by a map classification problem in the simulation environment of satellite campus maps using quadcopters with onboard cameras.

First Page

294

Last Page

305

DOI

10.1007/978-3-030-92790-5_23

Publication Date

1-1-2022

Comments

IR deposit conditions: none described

Recommended Citation

G. Liu, A. Amini, M. Takáč, and N. Motee, “Classification-aware path planning of network of robots,” Springer Proceedings in Advanced Robotics, vol. 22 SPAR, pp. 294–305, 2022, doi: 10.1007/978-3-030-92790-5_23.