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Enhancing Robotic Swarms With Fractal Behaviours to Explore Unknown Enclosed Areas

Enhancing Robotic Swarms With Fractal Behaviours to Explore Unknown Enclosed Areas

Eissa, Hazem, Melis, Wim J. C. ORCID: 0000-0003-3779-8629, Keates, Simeon ORCID: 0000-0002-2826-672X and Doncheva, Radostinka (2018) Enhancing Robotic Swarms With Fractal Behaviours to Explore Unknown Enclosed Areas. In: 3rd Medway Engineering Conference: Systems: Efficiency, Sustainability and Modelling, 20 June 2018, University of Greenwich, Pembroke Building Central Avenue, Chatham Maritime, United Kingdom.

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Abstract

Swarm robotics coordinates multiple interacting robots for which it takes inspiration from nature. It has been used in different engineering applications, such as: food searching, path planning, and communication between robots. Additionally, when robots are used to explore unknown areas this can safeguard humans from having to take unnecessary risks. Most existing exploration strategies aim to reduce the exploration time as well as the robot’s power consumption. However, during the exploration process, the robots tend to preserve their formation which could, depending on the area to be explored, be inefficient. Therefore, this paper proposes to dynamically change the swarm’s formation as information about the explored area is being obtained. The restructuring obviously aims to reduce overall time and power consumption and considers any obstacles faced and/or the area itself. Overall, this means that the robots, as a swarm, will need to be more coordinated, as well as prepared for challenges that relate to e.g. the distribution of decision making across the swarm.

Item Type: Conference or Conference Paper (Paper)
Uncontrolled Keywords: Fractal Formation, Swarm Robotics
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Engineering Science
Related URLs:
Last Modified: 11 Aug 2018 23:00
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/20987

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