# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 1 Introduction  
Mobile robots have significant advantages in improving work efficiency, reducing labor intensity, and optimizing production processes. They are widely used in various fields such as home services, industrial manufacturing, medical care, mining and resource exploration, logistics and transportation, military reconnaissance, and space exploration. Examples include cleaning robots, warehouse robots, mining robots, and drones [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]. Path planning, as one of the core technologies for the autonomous navigation of mobile robots, plays a decisive role in the operational efficiency, safety, and motion control of robots. Therefore, the performance of path planning algorithms is an important indicator of the intelligence level of mobile robots and a hot topic in robotics research worldwide. Traditional path planning algorithms, such as [PERSON]'s algorithm [11], A* algorithm [12, 13, 14], Rapidly-exploring Random Tree (RRT) [15], artificial neural networks (ANNs) [16], Dynamic Window Approach (DWA) [13, 14], and Artificial Potential Field (APF) [17], have significant limitations in practical applications. For example, [PERSON]'s algorithm has high computational complexity and long search times; the A* algorithm often generates paths with many turns and low smoothness; the RRT has slow convergence and strong randomness in path generation; ANNs require long training times and are prone tofalling into local optima; the DWA is sensitive to parameters and has high computational complexity; and the APF is prone to local optima and target unreachability issues. These limitations make it difficult for traditional algorithms to meet the real-time and efficiency requirements of path planning in complex environments. Compared to the traditional algorithms, swarm intelligence algorithms, which simulate the collaborative behavior of biological groups, have shown significant advantages in path planning in complex environments. For example, the Ant Colony Optimization (ACO) algorithm guides path search through pheromones, demonstrating strong global search capabilities [18]; the Particle Swarm Optimization (PSO) algorithm achieves efficient optimization through information sharing among particles [19]; the Grey Wolf Optimizer (GWO) approximates the optimal solution through the social hierarchy of grey wolves [20]; the Artificial Bee Colony (ABC) algorithm enhances search efficiency through the division of labor among bees [21]; and the Dung Beetle Optimization (DBO) algorithm, inspired by the natural foraging and rolling behaviors of dung beetles, demonstrates strong optimization capabilities and fast convergence [22]. Among these, the ACO algorithm is widely adopted in combinatorial optimization problems, such as path planning and scheduling, due to its robust performance [23]. Nevertheless, in certain technical object optimal design problems, algorithms like PSO and GWO may outperform ACO. While ACO has demonstrated strong performance in combinatorial optimization, it is important to acknowledge its limitations. Traditional ACO algorithms still suffer from problems such as local optima and poor convergence. To address these issues, researchers [24; 25; 26; 27; 28; 29; 30; 31; 32] have proposed various improvement methods. Such research has significantly enhanced the path smoothness, convergence speed, and global search capabilities of ACO algorithms through strategies such as optimized pheromone initialization (e.g., non-uniform distribution, reinforcement of key nodes), improved heuristic functions (incorporating turning penalties and distance factors), dynamically adjusted evaporation factors, reward-punishment mechanisms, integration with elite/Max-Min models, and multi-objective optimization (balancing path length, safety, and energy consumption). However, further improvements are still needed in real-time performance, dynamic adaptability, and the avoidance of local optima in complex environments.