# Bio-Inspired Reactive Approaches for Automated Guided Vehicle Path Planning: A Review

## TL;DR

This paper reviews recent advances in reactive path planning algorithms for automated guided vehicles, focusing on bio-inspired and artificial intelligence approaches.

## Contribution

A comprehensive review of reactive path planning methods for AGVs from 2019–2025, highlighting trends and performance in static and dynamic environments.

## Key findings

- Swarm intelligence algorithms are effective in static environments with low computational complexity.
- Artificial intelligence algorithms excel in dynamic environments but face robustness and sim-to-real challenges.
- 45.68% of reviewed papers achieve online implementations, and 33.33% address multi-AGV systems.

## Abstract

Automated guided vehicle (AGV) path planning aims to obtain an optimal path from the start point to the target point. Path planning methods are generally divided into classical algorithms and reactive algorithms, and this paper focuses on reactive algorithms. Reactive algorithms are classified into swarm intelligence algorithms and artificial intelligence algorithms, and this paper reviews relevant studies from the past six years (2019–2025). This review involves 123 papers: 81 papers are about reactive algorithms, 44 are based on the swarm intelligence algorithm, and 37 are based on artificial intelligence algorithms. The main categories of swarm intelligence algorithms include particle swarm optimization, ant colony optimization, and genetic algorithms. Neural networks, reinforcement learning, and fuzzy logic represent the main trends in artificial intelligence–based algorithms. Among the cited papers, 45.68% achieve online implementations, and 33.33% address multi-AGV systems. Swarm intelligence algorithms are suitable for static or simplified dynamic environments with a low computational complexity and fast convergence, as 79.55% of papers are based on a static environment and 22.73% achieve online path planning. Artificial intelligence algorithms are effective for dealing with dynamic environments, which contribute 72.97% to online implementation and 54.05% to dynamic environments, while they face the challenge of robustness and the sim-to-real problem.