# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 1 Introduction  
Navigation is a critical research domain in mobile robotics [1], facilitating robots to achieve optimal paths without collision from one location to another [2]. Path planning is widely applied in various fields, including the prevention of robot collision [3], obstacle avoidance [4], unmanned aerial vehicle (UAV) flight [5], urban road planning [6], and logistics vehicle routing [7]. At present, numerous scholars have explored various path-planning methods, such as the artificial potential field method [8, 9], [PERSON]'s algorithm [10, 11], A* algorithm [12], genetic algorithm [13, 14], particle swarm algorithm [15], artificial neural network [16], and ant colony optimization [17, 18], among others. The ant colony optimization algorithm (ACO), proposed by [PERSON] and colleagues [19], is a distributed heuristic method inspired by natural processes. As a prominent approach within the realm of swarm intelligence systems, ACO emulates the foraging process of ants to identifyoptimal solutions [20]. It is distinguished by its capacity for parallel processing, distributed computing, high robustness, and positive feedback mechanisms. Owing to these characteristics, ACO has been widely applied to a variety of combinatorial optimization problems, including quadratic assignment, scheduling, image edge detection, economic modeling, and mobile robot navigation [21]. In the field of mobile robotics, ACO has demonstrated significant versatility and effectiveness across diverse applications. For instance, in intelligent agricultural environments, path planning based on the ant colony algorithm has greatly advanced agricultural robotics technology, enabling autonomous agricultural vehicles to accurately navigate complex field terrains during operations, such as sowing, spraying, and harvesting, thereby effectively improving overall operational efficiency [22]. In the warehousing and logistics industry, ACO can assist Automated Guided Vehicles (AGVs) in global path planning, thereby helping to streamline production workflows and reduce operational costs [23]. In emergency tasks, such as disaster relief and search and rescue, robots can efficiently perform path planning and navigation in complex and unpredictable environments using the ACO algorithm, thereby better ensuring the safety of human life and property [24; 25]. Furthermore, in document management domains, like libraries and archives, the ACO algorithm provides an efficient path planning approach, significantly improving the efficiency of document retrieval and storage [26]. Additionally, in hazardous environments containing radioactive materials, such as nuclear power plants, mobile robots utilize ant colony algorithm-based path planning to develop efficient inspection routes, thereby effectively ensuring the safe and stable operation of critical equipment [27]. However, in practical application, the ACO exhibits several limitations, including stagnation, premature convergence, low search efficiency, difficulty in determining control parameters, susceptibility to local optima, and a significant number of lost ants [28; 29; 30]. These shortcomings become increasingly pronounced as the complexity of the environmental map increases. To enhance the performance of ACO, numerous researchers have proposed some improvements. To effectively mitigate the issue of search blindness, [PERSON] et al. [31] introduced an approach that involves an uneven distribution of initial pheromones along with directional heuristic information. Similarly, [PERSON] et al. [32] developed an improved ACO-based algorithm that enhances the initial pheromone concentration, dynamically regulates the pseudorandom transfer strategy, updates the pheromones of high-quality ants, and adaptively adjusts the volatility coefficient. These modifications have significantly improved global search ability, convergence rate, and computational efficiency. Additionally, [PERSON] et al.