Ibrahim, Dalia Shouman El Shahat (2024) Scalar field guidance in swarms of simple robots. Doctoral (PhD) thesis, Memorial University of Newfoundland.
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Abstract
Developing swarms of robots as simple agents without any central controller is a challenging task. We focus on simple robots with limited capability which do not have a localization system to show them where they are. Termite construction activity inspires our work; although they have poor vision, they can construct a massive termite mound, build a royal chamber and communicate with each other by observing the changes in the environment, sensing vibrations and detecting chemical substances (pheromones). In our work, we used scalar fields as a global level of guidance for the robots. A scalar field associates a value with every point in the working region. We show how scalar fields can be used to guide a low-cost and limited-capability swarm of robots to execute a specific task. We present four examples of tasks using the scalar field, such as constructing shapes from ambient objects, finding the largest coverage-connected network among the robots, aggregating to a predefined area and foraging by finding and collecting ambient objects to the collection area. This work is divided into three parts; first, we show how the scalar field with different resolutions can help divide labour among the robots and guide them in their movements. Second, we investigate combining a scalar field with reinforcement learning to find the largest coverage network. Finally, we design a coloured scalar field and use it as a road network for the swarm to reduce spatial interference among the robots. We practically build our robots based on the Zumo robot kit to perform the aggregation task.
| Item Type: | Thesis (Doctoral (PhD)) |
|---|---|
| URI: | http://research.library.mun.ca/id/eprint/16473 |
| Item ID: | 16473 |
| Additional Information: | Includes bibliographical references (pages 137-153) -- Restricted until April 30, 2025 |
| Keywords: | scalar field, swarm robotics, reinforcement learning |
| Department(s): | Science, Faculty of > Computer Science |
| Date: | April 2024 |
| Date Type: | Submission |
| Library of Congress Subject Heading: | Scalar field theory; Robotics; Reinforcement learning; Robots--Design and construction |
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