Journal
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
Volume 39, Issue 4, Pages 5329-5338Publisher
IOS PRESS
DOI: 10.3233/JIFS-189018
Keywords
Mobile robot; path planning; ant colony algorithm; heuristic information; global optimization
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Funding
- Chongqing Municipal Education Commission Science and Technology Fund Project [KJ1601032, KJQN201 901238, KJZD-K201901202]
- Intelligent Manufacturing Pilot Technology Chongqing University Engineering Research Center [2019yjzx0101]
- Chongqing Three Gorges University Science and Technology Fund Project [19QN06]
- Chongqing Three Gorges University Graduate Innovation Project [YJSKY1804]
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The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms' simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.
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