Abstract
A direct data-driven path planner for small autonomous robots is a desirable feature of robot swarms that would allow each agent of the system to directly produce control actions from sensor readings. This feature allows to bring the artificial system closer to its biological model, and facilitates the programming of tasks at the swarm system level. To develop this feature it is necessary to generate behavior models for different possible events during navigation. In this paper we propose to develop these models using deep regression. In accordance with the dependence of distance on obstacles in the environment along the sensor array, we propose the use of a recurrent neural network. The models are developed for different types of obstacles, free spaces and other robots. The scheme was successfully tested by simulation and on real robots for simple grouping tasks in unknown environments.
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Acknowledgments
This work was supported by the District University Francisco José de Caldas and the Scientific Research and Development Centre (CIDC). The views expressed in this paper are not necessarily endorsed by District University. The authors thank the research group ARMOS for the evaluation carried out on prototypes of ideas and strategies.
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Martínez, F., Penagos, C., Pacheco, L. (2018). Deep Regression Models for Local Interaction in Multi-agent Robot Tasks. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_7
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DOI: https://doi.org/10.1007/978-3-319-93818-9_7
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