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Biological Goal Seeking


Kerr, Emmett; Vance, Philip; Kerr, Dermot; Coleman, Sonya A; Das, Gautam; McGinnity, Thomas Martin; Moeys, Diederik Paul; Delbruck, Tobi (2018). Biological Goal Seeking. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA, 17 October 2018 - 19 October 2018.

Abstract

Obstacle avoidance is a critical aspect of control for a mobile robot when navigating towards a goal in an unfamiliar environment. Where traditional methods for obstacle avoidance depend heavily on path planning to reach a specific goal location whilst avoiding obstacles, the method proposed uses an adaption of a potential fields algorithm to successfully avoid obstacles whilst the robot is being guided to a non-specific goal location. Details of a generalised finite state machine based control algorithm that enable the robot to pursue a dynamic goal location, whilst avoiding obstacles and without the need for specific path planning, are presented. We have developed a novel potential fields algorithm for obstacle avoidance for use within Robot Operating Software (ROS) and made it available for download within the open source community. We evaluated the control algorithm in a high-speed predator-prey scenario in which the predator could successfully catch the moving prey whilst avoiding collision with all obstacles within the environment.

Abstract

Obstacle avoidance is a critical aspect of control for a mobile robot when navigating towards a goal in an unfamiliar environment. Where traditional methods for obstacle avoidance depend heavily on path planning to reach a specific goal location whilst avoiding obstacles, the method proposed uses an adaption of a potential fields algorithm to successfully avoid obstacles whilst the robot is being guided to a non-specific goal location. Details of a generalised finite state machine based control algorithm that enable the robot to pursue a dynamic goal location, whilst avoiding obstacles and without the need for specific path planning, are presented. We have developed a novel potential fields algorithm for obstacle avoidance for use within Robot Operating Software (ROS) and made it available for download within the open source community. We evaluated the control algorithm in a high-speed predator-prey scenario in which the predator could successfully catch the moving prey whilst avoiding collision with all obstacles within the environment.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:19 October 2018
Deposited On:12 Mar 2019 13:43
Last Modified:12 Mar 2019 13:43
Publisher:IEEE
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/SSCI.2018.8628696

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