DocumentCode :
2704235
Title :
Multi-goal feasible path planning using ant colony optimization
Author :
Englot, Brendan ; Hover, Franz
Author_Institution :
Dept. of Mech. Eng. ing, Massachusetts Inst. of Technol., Cambridge, MA, USA
fYear :
2011
fDate :
9-13 May 2011
Firstpage :
2255
Lastpage :
2260
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.
Keywords :
collision avoidance; computational complexity; optimisation; sampling methods; travelling salesman problems; underwater vehicles; ant colony optimization; autonomous undewater inspections; computational complexity; computational optimality; multigoal feasible path planning; obstacle-filled two-dimensional environment; sampling-based point-to-point planning algorithm; surveillance applications; traveling salesman problem; Algorithm design and analysis; Complexity theory; Heuristic algorithms; Inspection; Planning; Robots; Upper bound;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2011 IEEE International Conference on
Conference_Location :
Shanghai
ISSN :
1050-4729
Print_ISBN :
978-1-61284-386-5
Type :
conf
DOI :
10.1109/ICRA.2011.5980555
Filename :
5980555
Link To Document :
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