Priority-based human-swarm interaction applied to a foraging application

Said, Dalia (2016) Priority-based human-swarm interaction applied to a foraging application. Masters thesis, Memorial University of Newfoundland.

[English] PDF - Accepted Version Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Download (54MB)

Abstract

A robot swarm employs a large number of robots to facilitate sophisticated problem-solving as well as improved load-balancing and time efficiency compared to single robot systems. Swarm Intelligence is based on the local interactions between agents of the swarm that enables the emergence of a desired global behaviour, thus allowing the swarm to be autonomous. While autonomy is efficient for straightforward applications, in complex problems and environments human intervention may be more efficient. Human control of a swarm remains an open problem with multiple approaches proposed, each designed for a specific type of application. This work suggests a priority-based approach inspired by well known Human-Swarm Interaction techniques. The approach aims to serve as a high-level guide for the agents of a swarm, allowing them to use Swarm Intelligence on a low level. It also allows the division of the swarm into subswarms that can be easily controlled separately. Before experiments could be carried out to validate the proposed approach, the robots used in our experiments had to be put together, and their software needed to be designed to put to use their various components. A vision system upon which their sensing is dependent needed to be established, with defined visual markers and obstacle detection. This thesis tests the proposed priority-based Human-Swarm Interaction system by implementing a simple foraging application, using simulated and real robots, and studying the effects of introducing such a system to a group of robots that use simple Swarm Intelligence. Results show that the proposed approach does succeed in dividing the swarm into subswarms and increasing the efficiency of the foraging solution, however, some drawbacks manifested themselves throughout the process. We discuss these advantages and issues as well as future work.

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