Multi-Goal Feasible Path Planning Using Ant Colony Optimization
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Hover_Multi-goal.pdf
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212.46 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Englot, Brendan J.
Hover, Franz S.
Date Issued
February 2011
Journal
2011 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Englot, Brendan J., and Franz S. Hover. “ Multi-goal feasible path planning using ant colony optimization.” 2011 IEEE International Conference on Robotics and Automation (ICRA) (2011). 2255 - 2260.
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5980555&tag=1
