Autonomous robot patrolling of a sparsely populated unknown environment

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Title: Autonomous robot patrolling of a sparsely populated unknown environment
Author: LaRocque, Patrick
Abstract: The increasing availability and affordability of autonomous robots has expanded their uses for many new applications, such as exploration, surveillance and threat containment. Most research considers a team of a large number of robots that contain global information. This work explores distributed and low overhead algorithms for patrolling and threat containment within a region sparsely populated with few robots. The robots patrol the area without the global knowledge of the region, but each is equipped with an omni-directional range finder and a positioning system for keeping track of its location and metering distance and directions of events. This study presents the extent of effectiveness and limitations of utilizing a limited number of robots patrolling an unknown wide-spread region. A set of three algorithms was developed. All algorithms assume the use of artificial potential fields (APFs) for collision avoidance with other robots and the walls as well as to approach the threat. The algorithms differ in two ways; whether or not the robots have a limited memory of past events and the way the robots maneuver from one patrol target location to another. The next patrol target location can be derived randomly or based on past events. The past events include previously sensed robot locations, target locations, and walls. The algorithms are analyzed in terms of the time it takes for the robots to detect and neutralize threats within the surveillance region. Simulations via MATLAB are conducted to investigate the tradeoffs due to factors such as the number of robots, the size of the region, and the frequency of threats. The results show that the three algorithms perform comparably on average, achieving reasonable effectiveness given the inherent limitations that are lacking in the global information about the environment.
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Date: 2010-11

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