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Paper TuP209.1

Englot, Brendan (MIT), Hover, Franz (MIT)

Multi-Goal Feasible Path Planning Using Ant Colony Optimization

Scheduled for presentation during the Regular Sessions "Motion and Path Planning II" (TuP209), Tuesday, May 10, 2011, 15:25−15:40, Room 5D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on April 2, 2020

Keywords Evolutionary Robotics, Motion and Path Planning, Marine Robotics

Abstract

A new algorithm for solving multi-goal planning problems in the presence of obstacles is introduced. We extend the use of ant colony optimization (ACO) from its well-known application, the traveling salesman problem (TSP), to that of multi-goal feasible motion planning for inspection and surveillance applications. Specifically, the ant colony framework for solving the TSP 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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