Multi-Goal Feasible Path Planning Using Ant Colony Optimization
Author(s)Englot, Brendan J.; Hover, Franz S.
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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.
DepartmentMassachusetts Institute of Technology. Department of Mechanical Engineering
2011 IEEE International Conference on Robotics and Automation (ICRA)
Institute of Electrical and Electronics Engineers (IEEE)
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.
Author's final manuscript