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Classification-Aware Path Planning of Network of Robots

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Part of the Springer Proceedings in Advanced Robotics book series (SPAR,volume 22)


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.


  • Network of robots
  • Distributed classification
  • Path planning
  • Machine learning

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  • DOI: 10.1007/978-3-030-92790-5_23
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This work was supported in parts by the AFOSR FA9550-19-1-0004, ONR N00014-19-1-2478.

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Correspondence to Guangyi Liu .

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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.

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