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October 23, 2025Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science

Implementation of machine learning approach by the application of ant colony optimization with enhanced heuristic within the realm of robotic path planning

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Authors

SBShriganesh BollakpalliKOKashfull Orra

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Overview

Simulation reports a 72% faster runtime and acceptable path lengths in robotic navigation, suggesting improved efficiency.

Key Points

  • Ant colony optimization leads to notable improvements in convergence speeds for robotic path planning, enhancing navigation.
  • The approach recorded a runtime increase of 72%, alongside maintaining an acceptable range for path lengths evaluated.
  • Simulation conducted in MATLAB focusing on algorithm adjustments for optimized heuristics affecting navigation outcomes.
  • Results emphasize practical navigation paths for robots, indicating potential for real-time application in various environments.

Cite This Study

Bollakpalli et al. (2025) studied this question.

synapsesocial.com/papers/68fa1210f9f8b44535bfcd2ehttps://doi.org/10.1177/09544062251379271
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Hybrid heuristic ant colony optimization algorithm for mobile robot path planning2026
  2. 2Comparison of Conventional and Modified Ant Colony Approaches for Path Planning of Robot in an Indoor Environment2024 · 5 citations
  3. 3Application of improved ant colony algorithm fusing Bresenham and direction factor in mobile robot path planning2025
  4. 4Path Planning for Mobile Robots Based on Multi-strategy Enhanced Ant Colony Optimization Algorithm2026
  5. 5Research on mobile robot path planning based on multi-strategy improved ant colony algorithm2025