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A Convex Optimization Framework for Multi-Agent Motion Planning.

A Convex Optimization Framework for Multi-Agent Motion Planning.


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About the Book

Imagine a world where driving fatalities are drastically reduced because cars are able to safely coordinate and drive themselves. A world where a robot team is sent into a burning building to pull a child from the clutches of death or where robots can assist an elderly couple by mowing the lawn, finding lost belongings, bringing daily medications, or calling for assistance in an emergency. A fundamental requirement of such transformative technologies will be the ability to effectively collaborate in order to reach some goal or bring about some physical state in the environment. In general, this decision process is known as motion planning and it poses one of the most significant challenges facing the fledgling field of robotics. The focus of this dissertation is addressing the motion planning problem by developing a rich, real-time framework that affords optimality and provides substantial performance guarantees. Towards this end, this research exploits recent advances in convex optimization with an emphasis on applications to optimal team formation changes and optimal collaborative target tracking. Regarding the former, second-order cone programming (SOCP) techniques are utilized to develop highly efficient centralized, distributed, and decentralized solutions to the problem. Regarding the latter, semidefinite programming (SDP) is melded with seminal results from spectral graph theory to develop a discrete-time framework for optimal target tracking subject to k-coverage and network connectivity constraints. As the capstone of this research, these seemingly unrelated contributions are unified in a discrete-time optimization framework that also affords goal-directed team behavior and dynamic collision avoidance while minimizing an arbitrary convex objective. Simulation and experimental results are included to highlight the utility of this approach.


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Product Details
  • ISBN-13: 9781243613271
  • Publisher: Proquest, Umi Dissertation Publishing
  • Publisher Imprint: Proquest, Umi Dissertation Publishing
  • Height: 254 mm
  • Weight: 417 gr
  • ISBN-10: 1243613270
  • Publisher Date: 01 Sep 2011
  • Binding: Paperback
  • Spine Width: 14 mm
  • Width: 203 mm


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A Convex Optimization Framework for Multi-Agent Motion Planning.
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