الفهرس 
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Abstract
Foraging is a benchmark problem for swarm robotics. It is inspired by swarms of insects cooperating to locate and/or transport food items that a single individual can not move. The challenge is to program a swarm of simple robots, with minimal communication and individual capability, to search the environment for a food target and return it to their base collectively. In robotics, foraging is important for several reasons. It is a metaphor for a broad class of problems integrating exploration, navigation and object identification, manipulation and transport. In multi-robot systems, foraging is a canonical problem for the study of robot-robot cooperation. Moreover, many actual or potential real-world applications for robotics are instances of foraging robots, for instance cleaning, harvesting, search and rescue, land-mine clearance or planetary exploration.
This thesis focuses on foraging as a multi-robot task. The robots start at a home location, explore the world in search of their target, and return from the target incrementally to the home. Such task is difficult in swarm robotic systems because of the lack of global localization, communication, and odometry, which make it impossible for the robots to acquire or build maps, for example. Once a robot loses contact with the other robots, it is effectively lost and has no way to return home. This work focuses on robots with very simple hardware capabilities. The robots are not assumed to have global position information, global communication, and their only
communication capabilities are simple local neighbor-to-neighbor communication. To overcome their hardware limitations and leverage their large numbers, the robots must work together. In this thesis, we first survey different distributed
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foraging algorithms for swarm robots and analyze them in simulation and then introduce two novel foraging algorithms: Tornado and Adaptive Tornado. Tornado algorithm is inspired by the spiral tornado motion only and Adaptive Tornado algorithm is inspired by the spiral tornado motion and chain motion. These algorithms can scan an area with high speed given a large swarm. However, they can adapt in case of failure of some robots and successfully finish the job at a slower speed. Simulation results show that these algorithms provide better coverage and robustness compared to previous foraging algorithms and also these results show that Adaptive Tornado algorithm has better performance compared to Tornado algorithm in reaching to target at smaller time.
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