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Topological Progress Potential-Enhanced Continuous-Space Ant Colony Algorithm for Robot Path Planning.

Guikun Dong1, Feixiong Zhao1, Jiaxiong Zhuo1

  • 1School of Mechanical Engineering, Chengdu University, Chengdu 610106, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

A new topological progress potential-enhanced continuous-space ant colony path planning algorithm (TPP-CSACO) improves path planning by enhancing global guidance and obstacle avoidance. This method achieves shorter paths, better smoothness, and higher safety compared to traditional algorithms.

Keywords:
ant colony optimization algorithmcontinuous spaceelastic step sizeglobal path planningsector-based perception

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Intelligence
  • Path Planning Algorithms

Background:

  • Traditional grid-based Ant Colony Optimization (ACO) path planning faces limitations in continuous spaces, including restricted direction freedom, inadequate global topological guidance, and challenges in balancing path smoothness with safety.
  • These limitations hinder efficient and safe navigation in complex environments.

Purpose of the Study:

  • To propose a novel algorithm, topological progress potential-enhanced continuous-space ant colony path planning (TPP-CSACO), to overcome the drawbacks of traditional grid-based ACO.
  • To enhance path planning by integrating global topological information with local pheromone guidance for improved efficiency, smoothness, and safety.

Main Methods:

  • TPP-CSACO abandons grid-based expansion, employing a perception circle and sector-based movement with probabilistic direction selection.
  • It utilizes band-shaped decaying pheromones and couples global topological progress potential (from simplified probabilistic roadmap) with pheromones for dual-field guidance.
  • An adaptive step size strategy, incorporating elastic step size and frustration-induced temperature rise, is used with signed distance field (SDF) for obstacle avoidance and stability.

Main Results:

  • Experiments on multiscale constrained maps demonstrate TPP-CSACO reduces path length by up to 50.6% compared to traditional ACO.
  • The algorithm exhibits faster convergence and maintains good search diversity.
  • While path length increased slightly (max 5.9%) versus heuristic methods, maximum turning angle was reduced by 75%-93%, achieving a 100% success rate and zero safety violations.

Conclusions:

  • TPP-CSACO effectively addresses limitations of traditional grid-based ACO in continuous space path planning.
  • The algorithm achieves a stable balance between path safety, smoothness, and global search capability.
  • TPP-CSACO offers a promising approach for complex navigation tasks requiring high safety and efficiency.