4.6 Article

Intelligent Warehouse Robot Path Planning Based on Improved Ant Colony Algorithm

期刊

IEEE ACCESS
卷 11, 期 -, 页码 12360-12367

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3241960

关键词

Ant colony optimization; Path planning; Convergence; Safety; Optimization; Heuristic algorithms; Robot kinematics; Warehouses; Mobile robots; Poisson equations; Intelligent warehouse robot; ant colony algorithm; path planning; poisson distribution; three-color grid map

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In order to enhance the safety and accuracy of path planning for intelligent warehouse robots, this study proposes a storage shelf model and utilizes Poisson Distribution to simulate the impact of unknown factors, establishing a three-color raster map. By optimizing the pheromone update mechanism considering various factors, including path safety, path length, and turning elements influenced by unknown factors, two separate models based on three-dimensional shelves are simulated, and the planned paths are smoothed and pruned. The simulation results demonstrate that the improved algorithm is capable of designing optimal routes safely and effectively in storage environments affected by unknown factors. The proposed algorithm not only resolves the issues of blind search and deadlock but also outperforms other algorithms, with only 4 iterations compared to 22 and 30 iterations, 3 turns compared to 9 and 7 turns, and a reduced running time of 8.468s compared to 16.974s and 13.754s.
To improve the safety and accuracy of path planning of intelligent warehouse robots, this paper establishes a storage shelf model, incorporates Poisson Distribution to simulate the influence of unknown factors, and establishes a three-color raster map. The pheromone update mechanism is optimized by considering the path safety, path length, and turning elements under the influence of unknown factors. The two models based on the three-dimensional shelves are simulated separately, and the planned paths are de-pointed and smoothed. The simulation results show that the improved algorithm can design the optimal route safely and effectively in the storage environment under the influence of unknown factors. The proposed algorithm not only solves the blind search and deadlock problems, but also has better performances than other algorithms, i.e., 4 iterations compared to 22 and 30 iterations, 3 turns compared to 9 and 7 turns, 8.468s running time compared to 16.974s and 13.754s.

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