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Path planning of mobile robot based on improved ant colony algorithm for logistics.

Tian Xue1, Liu Li2, Liu Shuang3

  • 1Logistics School, Beijing Wuzi University, Beijing 101149, China.

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|July 2, 2021
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Summary

This study enhances robot path planning for logistics using an improved ant colony algorithm. The optimized approach significantly reduces path length by 9.21% in warehouse environments.

Keywords:
logisticsmulti step searchoptimal pathrobotroute planning

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Area of Science:

  • Robotics and Automation
  • Logistics and Supply Chain Management
  • Artificial Intelligence and Optimization Algorithms

Background:

  • Efficient robot path planning is crucial for optimizing logistics operations, including warehousing, sorting, and distribution.
  • Traditional path planning algorithms may face limitations in complex and dynamic logistics environments.
  • The ant colony algorithm offers a bio-inspired approach to solving pathfinding problems.

Purpose of the Study:

  • To improve the performance of the ant colony algorithm for robot path planning in logistics.
  • To develop a more efficient and effective path planning strategy for warehouse environments.
  • To reduce the path length and enhance the overall efficiency of robotic logistics operations.

Main Methods:

  • Modification of the ant colony algorithm by incorporating a multi-step search strategy.
  • Redesigning the pheromone update mechanism to enhance convergence and solution quality.
  • Implementing a path smoothing technique to optimize the planned routes.
  • Experimental validation on a 16x16 grid logistics storage site.

Main Results:

  • The improved ant colony algorithm successfully planned shorter optimal paths compared to standard methods.
  • A significant reduction in path length by 9.21% was achieved on the test logistics site.
  • The enhanced algorithm demonstrated improved performance in navigating the grid-based warehouse environment.
  • The multi-step search and redesigned pheromone updates contributed to better pathfinding.

Conclusions:

  • The proposed enhanced ant colony algorithm is effective for robot path planning in logistics.
  • The improvements lead to substantial savings in path length, boosting operational efficiency.
  • This optimized algorithm offers a practical solution for real-world warehouse automation challenges.