Abstract
Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment.
Availability of data and material
The data that support the findings of this study are available from the corresponding author, S.S., upon reasonable request.
Code availability
The code used to generate the results presented in this study is available from the corresponding author, S.S., upon reasonable request.
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Funding
Open Access funding provided by Università degli Studi di Trieste. This work has been partially supported by the PRIN project779 SEDUCE n. 2017TWRCNB and by the University of Trieste - University funding for scientific research projects - FRA 2018.
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Conceptualization: L.M., S.S., E.M., P.G.; Methodology: L.M., S.S., E.M.; Software: L.M., S.S; Validation: S.S., M.C.; Formal analysis: S.S.; Investigation: S.S., L.M.; Resources: S.S.; Data Curation: S.S.; Writing - Original Draft: S.S., E.M., L.M., M.C.; Writing - Review & Editing: S.S., P.G., E.M.; Visualization: S.S., M.C.; Supervision: S.S., P.G.; Project administration: S.S., P.G., E.M.; Funding acquisition: S.S., P.G.
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Seriani, S., Marcini, L., Caruso, M. et al. Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization. J Intell Robot Syst 101, 36 (2021). https://doi.org/10.1007/s10846-020-01308-8
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DOI: https://doi.org/10.1007/s10846-020-01308-8
Keywords
- Artificial neural networks
- Evolutionary robotics
- NEAT
- Crowd navigation
- Robot controller