Flocking is seen in nature as a means for self protection, more efficient foraging, and other search behaviors. Although much research has been done regarding the application of this principle to autonomous vehicles, the majority of the research has relied on GPS information, broadcast communication, an omniscient central controller, or some other form of "global" knowledge. This approach, while effective, has serious drawbacks, especially regarding stealth, reliability, and biological grounding. This research effort uses three Pioneer P2-AT8 robots to achieve flocking behavior without the use of global knowledge. The sensory inputs are limited to two cameras, offset such that the area of stereo vision is minimal, thus making stereo image analysis techniques effectively impossible, but allowing a much larger effective field of vision. The flocking algorithm analyzes these images and updates each robot's velocity vector according to the relative position, heading, and speed of its nearest neighbor. The result of this velocity update is an eventual stabilization of speed and heading, resulting in a coherent, stable flock, demonstrated in both software simulation and in hardware.
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Hardcover. Condition: new. Hardcover. Flocking is seen in nature as a means for self protection, more efficient foraging, and other search behaviors. Although much research has been done regarding the application of this principle to autonomous vehicles, the majority of the research has relied on GPS information, broadcast communication, an omniscient central controller, or some other form of "global" knowledge. This approach, while effective, has serious drawbacks, especially regarding stealth, reliability, and biological grounding. This research effort uses three Pioneer P2-AT8 robots to achieve flocking behavior without the use of global knowledge. The sensory inputs are limited to two cameras, offset such that the area of stereo vision is minimal, thus making stereo image analysis techniques effectively impossible, but allowing a much larger effective field of vision. The flocking algorithm analyzes these images and updates each robot's velocity vector according to the relative position, heading, and speed of its nearest neighbor. The result of this velocity update is an eventual stabilization of speed and heading, resulting in a coherent, stable flock, demonstrated in both software simulation and in hardware.This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781025083216
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