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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Joon-Woo Lee Jeong-Jung Kim Ju-Jang Lee |
| Copyright Year | 2009 |
| Description | Author affiliation: Division of Electrical Engineering, School of Electrical Engineering and Computer Science, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Korea (Jeong-Jung Kim; Ju-Jang Lee) || Robotics Program, KAIST, 335 Gwahangno, Yuseong-gu, Daejeon 305-701, Korea (Joon-Woo Lee) |
| Abstract | In this paper, an improved Ant Colony Optimization (ACO) algorithm is proposed to solve path planning problems. These problems are to find a collision-free and optimal path from a start point to a goal point in environment of known obstacles. There are many ACO algorithms for path planning. However, it take a lot of time to get the solution and it is not to easy to obtain the optimal path every time. It is also difficult to apply to the complex and big size maps. Therefore, we study to solve these problems using the ACO algorithm improved by the path crossover scheme. The path crossover scheme is two-point crossover paths found by ants. The best path is stored and is compared with new path every time. The path crossover scheme is used at this time. When the two parts compared and exchanged, the better part updates the best path. We also propose that the pheromone update rule is modified as compared with previous our paper. |
| Starting Page | 1996 |
| Ending Page | 2000 |
| File Size | 6229213 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424443475 |
| DOI | 10.1109/ISIE.2009.5217930 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-05 |
| Publisher Place | South Korea |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Ant colony optimization Path planning Mobile robots Fuzzy logic Industrial electronics Service robots Computer science Costs Optimal control Genetic algorithms |
| Content Type | Text |
| Resource Type | Article |
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