Robot Path Optimization Based on Improved Ant Colony Optimization

The basic Ant Colony Algorithm is a distributed intelligent simulation algorithm. However, existing Ant Colony Optimization (ACO) algorithms still have limitations such as slow convergence and ants getting into deadlocks. To solve these problems, this paper proposes an improved ACO-based robot path planning algorithm. It aims to overcome thecy of the traditional ACO algorof the traditional ACO algorithm and them and the tendenccaughtt caught in local optima in path planning. First, this paper presents a variable-based study - grid ant colony algorithm. Ant colony algorithms have strong optimization, search capabilities. Next, by optimizing the initial pheromone distribution and introducing a differentiation strategy, the algorithm guides the ants to focus their initial stage search toward the target direction. This shortens the convergence time. Finally, combining a reward and punishment mechanism with an adaptive evaporation coefficient, the algorithm dynamically adjusts pheromone updates. This strengthens the global search ability and effectively avoids the local optimum problem.