# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 1 Introduction  
With AI's rapid development, AGVs have been applied in areas like warehousing and logistics, intelligent manufacturing, and smart services, thanks to their autonomous navigation, efficient operation, and precise obstacle avoidance capabilities. The core task of PP is to generate a trajectory that enables the robot to reach the goal position in the most efficient and reasonable way based on its starting position and considering the distribution of various obstacles in the operating space through specific algorithms and strategies. Within the two-dimensional space, the commonly used PP algorithms include traditional ones such as [PERSON]'s algorithm [1, 2], the A* algorithm [3, 4, 5, 6, 7, 8], and the RRT algorithm [9, 10, 11, 12, 13], as well as intelligent bionic algorithms like the 'genetic algorithm' [14, 15, 16], the 'particle swarm algorithm' [17, 18, 19], and the ACA [20, 21, 22]. The ACA, as the representative of swarm intelligence optimization technology, has been widely applied in path optimization due to its good fault tolerance and environmental adaptability. However, this algorithm also has certain limitations, specifically manifested in low efficiency during the convergence process and a high tendency of local optimal solutions, and high difficulty of achieving the ideal global optimal result, which negatively affects the final application effect and performance of the algorithm. In response to these deficiencies, scholars have actively carried out research on algorithm optimization. [PERSON] et al. [23] improved the state transition rule by constructing an adaptive state transition strategy, avoiding falling into local optima. [PERSON] et al. [24] reduced the blindnessof the initial search by setting non-uniform initial pheromone values, accelerating the convergence speed. [PERSON]. [25] combined the local and global search mechanisms of the 'genetic algorithm' with the pheromone update of the ACA, improving the search space and efficiency by guiding the optimal solution. [PERSON] et al. [26] set a pheromone range to ensure it was not zero, a simple method that did not increase algorithm complexity and helped reduce the possibility of falling into local optima. [PERSON] et al. [27] performed local search operations such as exchange and mutation on the iterative results, enhancing the algorithm's optimization performance and reducing the local optima. [PERSON] et al. [28] adopted a multi-subgroup parallel computing architecture, enhancing the group's collaborative ability through pheromone interaction among superior individuals, attempting to balance the contradiction between diversity and convergence speed, and reducing the risk of falling into local optima. [PERSON] et al. [29] proposed a multi-ant colony pseudo-parallel optimization algorithm to improve the optimization efficiency. [PERSON] et al. [30] used an ant colony algorithm based on random walk to detect cluster structures in complex networks, which provided new ideas for algorithm improvement in related fields. However, these similar methods have certain limitations. For one thing, the coupling mechanism between environment perception and path guidance is imperfect, and the path smoothness is insufficient in the complex obstacle environment. For example, the traditional ant colony algorithm is not flexible enough to adjust the path in real time according to the complex environment due to the pheromone accumulation and evaporation mechanism when searching for the path, resulting in more twists and turns in the path, which affects the operational efficiency of the AGV. Secondly, the pheromone updating strategy has poor dynamic adaptability, which makes it difficult to balance the exploration and utilization needs in different iteration stages. The pheromone updating rules in most improvement methods are relatively fixed, which makes it impossible to fully explore new paths in the early stage of the algorithm and difficult to focus on the optimal paths in the later stage.