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Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Operations Research

Background:

  • Automated guided vehicle (AGV) path planning is crucial for optimizing logistics and manufacturing operations.
  • Reactive algorithms offer dynamic pathfinding capabilities, contrasting with classical approaches.
  • A comprehensive review of recent advancements in reactive AGV path planning is needed.

Purpose of the Study:

  • To review and categorize reactive algorithms for AGV path planning published between 2019 and 2025.
  • To analyze the trends and applications of swarm intelligence and artificial intelligence-based methods.
  • To identify the strengths and limitations of different reactive approaches in various environments.

Main Methods:

  • Systematic literature review of 123 relevant papers.
  • Categorization of algorithms into swarm intelligence (e.g., PSO, ACO, GA) and artificial intelligence (e.g., NN, RL, Fuzzy Logic).
  • Analysis of implementation characteristics, including online capability and multi-AGV system applications.

Main Results:

  • Reactive algorithms are increasingly studied, with 81 papers focusing on this category.
  • Swarm intelligence algorithms (44 papers) are efficient for static environments with fast convergence.
  • Artificial intelligence algorithms (37 papers) demonstrate effectiveness in dynamic environments and online implementations.

Conclusions:

  • Swarm intelligence algorithms are well-suited for static or simplified dynamic environments due to low complexity and rapid convergence.
  • Artificial intelligence algorithms show promise for complex dynamic environments and real-time applications, despite robustness challenges.
  • Future research should address the sim-to-real gap and enhance the robustness of AI-based AGV path planning.