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|a Englot, Brendan J.
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|a Massachusetts Institute of Technology. Department of Mechanical Engineering
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|a Englot, Brendan J.
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|a Hover, Franz S.
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|a Hover, Franz S.
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|a Multi-Goal Feasible Path Planning Using Ant Colony Optimization
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|b Institute of Electrical and Electronics Engineers (IEEE),
|c 2013-04-25T14:17:19Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/78597
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|a A new algorithm for solving multi-goal planning problems in the presence of obstacles is introduced. We extend ant colony optimization (ACO) from its well-known application, the traveling salesman problem (TSP), to that of multi-goal feasible path planning for inspection and surveillance applications. Specifically, the ant colony framework is combined with a sampling-based point-to-point planning algorithm; this is compared with two successful sampling-based multi-goal planning algorithms in an obstacle-filled two-dimensional environment. Total mission time, a function of computational cost and the duration of the planned mission, is used as a basis for comparison. In our application of interest, autonomous underwater inspections, the ACO algorithm is found to be the best-equipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality.
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|a United States. Office of Naval Research (Grant N00014-06-10043)
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|a en_US
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|a Article
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|t 2011 IEEE International Conference on Robotics and Automation (ICRA)
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