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.


    Access

    Download


    Export, share and cite



    Title :

    Crowded Environment Navigation with NEAT: Impact of Perception Resolution on Controller Optimization



    Publication date :

    2021-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Navigation in crowded and restricted waters

    Lyon, P. R. | Taylor & Francis Verlag | 1981


    Tracking Multiple Humans in Crowded Environment

    Zhao, T. / Nevatia, R. / IEEE Computer Society | British Library Conference Proceedings | 2004


    Tracking multiple humans in crowded environment

    Tao Zhao, / Nevatia, R. | IEEE | 2004