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This study introduces a novel path planning method for mobile robots using improved artificial potential fields (APF) and ant colony optimization (ACO). The hybrid approach enhances stability and adaptability in known environments.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • Mobile robot path planning is crucial for autonomous navigation in known environments.
  • Existing methods like artificial potential fields (APF) can suffer from local minima and oscillations.
  • Ant colony optimization (ACO) offers a robust approach but can be computationally intensive.

Purpose of the Study:

  • To propose a hybrid path planning method combining improved APF and ACO for mobile robots.
  • To enhance the stability and environmental adaptability of mobile robot navigation.
  • To overcome limitations of traditional APF methods in complex environments.

Main Methods:

  • An improved artificial potential field (APF) algorithm was developed, addressing attraction fields, resultant force direction, and infinite loop issues on a grid map.
  • A hybrid pheromone updating strategy for ant colony optimization (ACO) was designed, integrating global and local updates.
  • A two-phase ACO optimization process was implemented: initial directional movement guided by APF, followed by exploration based on pheromone updates.

Main Results:

  • Simulation experiments demonstrated the effectiveness of the proposed hybrid path planning method.
  • Real-world mobile robot experiments validated the approach's stability and adaptability.
  • The method successfully navigated known environments, showing improved performance over traditional techniques.

Conclusions:

  • The hybrid APF-ACO path planning method offers a robust solution for mobile robot navigation.
  • The integration of improved APF and a novel ACO updating strategy enhances pathfinding efficiency and reliability.
  • The proposed method exhibits superior stability and environmental adaptability for mobile robots.