Abstract
The paper studies the path planning and task assignment of robots in the low-efficiency distribution of express delivery in the community. The grid method is used to model the environment and a community is analyzed as an example. In the ant colony optimization (ACO), the heuristic function is reconstructed by the valuation function of the A* algorithm to improve the convergence speed of the ACO. The algorithm has enhanced global search ability in the early stage, and the convergence speed is fast in the later stage with the improvement of pheromone volatilization coefficient, and the experimental parameters simulation analysis is done in MATLAB software. The experimental results show that the improved ACO has faster convergence and higher efficiency than the basic ACO. The rationality of the path planning model and the effectiveness of the optimized ACO are verified.
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Zu, Q., Yang, S. (2019). Route Optimization of Robot Groups in Community Environment. In: Milošević, D., Tang, Y., Zu, Q. (eds) Human Centered Computing. HCC 2019. Lecture Notes in Computer Science(), vol 11956. Springer, Cham. https://doi.org/10.1007/978-3-030-37429-7_32
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DOI: https://doi.org/10.1007/978-3-030-37429-7_32
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