A Deep Recurrent Neural Network Framework for Swarm Motion Speed Prediction

Abstract
Controlling and maintaining swarm robotic systems executing daily collective actions and accomplishing tasks more successfully in groups requires a timely and accurate forecast of swarm motion speed, which becomes a challenging task owing to swarm motion’s high dynamic feature. In this work, six potent forecasting recurrent deep neural networks, including RNN, LSTM, GRU, ConvLSTM, Bidirectional LSTM (BiLSTM), and BiGRU, are explored and compared in forecasting the motion speed of miniature swarm mobile robots engaged in a simple aggregation formation task. Essentially, the introduced forecasting models take advantage of the viscoelastic control model in flexibly controlling swarm robots and the capabilities of DL models to capture patterns in time series data. To this end, sensor measurements from a simulated swarm of foot bots conducting a circle formation task through the viscoelastic controller are recorded every 0.1 s and used as input vectors for forecasting purposes. The results show the promising performance of DL for swarm motion forecasting. Moreover, obtained results report that BiGRU reaches the highest swarm motion speed forecasting performance with the no/with obstacles scenarios considered in this study.

Citation
Khaldi, B., Harrou, F., Dairi, A., & Sun, Y. (2023). A Deep Recurrent Neural Network Framework for Swarm Motion Speed Prediction. Journal of Electrical Engineering & Technology. https://doi.org/10.1007/s42835-023-01446-7

Acknowledgements
This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Research Funding (KRF) under Award No. ORA-2022-5339. It belongs also to the PRFU research project C00L07ES220120220002 supported by General Direction of Scientific Research and Technological Development (DGRSDT).

Publisher
Springer Science and Business Media LLC

Journal
Journal of Electrical Engineering and Technology

DOI
10.1007/s42835-023-01446-7

Additional Links
https://link.springer.com/10.1007/s42835-023-01446-7

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