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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ben-Afia, A., et al.: Review and classification of vision-based localisation techniques in unknown environments. IET Radar Sonar Navig. 8(9), 1059–1072 (2014)
Bock, M., et al.: XV. Methods for creating functional soil databases and applying digital soil mapping with SAGA GIS. In: JRC Scientific and technical Reports, Office for Official Publications of the European Communities, Luxemburg (2007)
Cai, P., et al.: LeTS-drive: driving in a crowd by learning from tree search (2019). arXiv:1905.12197 [cs.RO]
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–80 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Islam, Md.J., Fulton, M., Sattar, J.: Toward a generic diverfollowing algorithm: balancing robustness and efficiency in deep visual detection. IEEE Robot. Autom. Lett. 4(1), 113–120 (2018)
Kavraki, L.E., et al.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). arXiv:1412.6980 [cs.LG]
Konar, A., et al.: A deterministic improved Q-learning for path planning of a mobile robot. IEEE Trans. Syst. Man Cybern. Syst. 43(5), 1141–1153 (2013)
LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Liu, G., et al.: Distributed map classification using local observations. arXiv preprint arXiv:2012.10480 (2021)
Manduchi, R., et al.: Obstacle detection and terrain classification for autonomous off-road navigation. Auton. Robots 18(1), 81–102 (2005). https://doi.org/10.1023/:AURO.0000047286.62481.1d. ISSN 1573-7527
Meyer, J.-A., Filliat, D.: Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies. Cogn. Syst. Res. 4(4), 283–317 (2003)
Mousavi, H.K., et al.: Multi-agent image classification via reinforcement learning. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5020–5027 (2019). https://doi.org/10.1109/IROS40897.2019.8968129
Mousavi, H.K., et al.: A layered architecture for active perception: image classification using deep reinforcement learning (2019). arXiv:1909.09705 [cs.LG]
Paszke, A., et al.: Automatic differentiation in pytorch (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Wulfmeier, M., Wang, D.Z., Posner, I.: Watch this: scalable cost-function learning for path planning in urban environments. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2089–2095. IEEE (2016)
Wulfmeier, M., et al.: Large-scale cost function learning for path planning using deep inverse reinforcement learning. Int. J. Robot. Res. 36(10), 1073–1087 (2017)
Acknowledgement
This work was supported in parts by the AFOSR FA9550-19-1-0004, ONR N00014-19-1-2478.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, G., Amini, A., Takáč, M., Motee, N. (2022). Classification-Aware Path Planning of Network of Robots. In: Matsuno, F., Azuma, Si., Yamamoto, M. (eds) Distributed Autonomous Robotic Systems. DARS 2021. Springer Proceedings in Advanced Robotics, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-92790-5_23
Download citation
DOI: https://doi.org/10.1007/978-3-030-92790-5_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92789-9
Online ISBN: 978-3-030-92790-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)