Abstract
The Data-collection Problem (DCP) models robotic agents collecting digital data in a risky environment under energy constraints. A good solution for DCP needs a balance between safety and energy use. We develop an Ensemble Navigation Network (ENN) that consists of a Convolutional Neural Network and several heuristics to learn the priorities. Experiments show ENN has superior performance than heuristic algorithms in all environmental settings. In particular, ENN has better performance in environments with higher risks and when robots have low energy capacity.
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Notes
- 1.
Input is padded with 0 s so that the input and output are of the same size.
- 2.
Reward per energy is also an average over 10,000 runs.
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This research was supported in part through computational resources provided by Syracuse University and by NSF award ACI-1541396.
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Xing, Z., Oh, J.C. (2018). Energy-Conserving Risk-Aware Data Collection Using Ensemble Navigation Network. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_59
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