# Salp Swarm Algorithm Optimized A* Algorithm and Improved B-Spline Interpolation in Path Planning
## 1 Introduction  
Path planning [1] is a fundamental technology for robotic navigation and autonomous driving, with its performance directly determining the efficiency and safety of mobile agents. The algorithm must generate optimal and smooth collision-free paths to reduce collision risks. Path planning [2] focuses on discovering an optimal trajectory that avoids obstacles between a starting point and a destination. An ideal path planning algorithm needs to meet two key requirements: first, it must be computationally efficient, capable of generating feasible paths in real time; second, the generated path must be smooth and safe for practical execution. Path planning algorithms can be broadly classified into two types: global path planning and local path planning. Currently, common global path planning algorithms include graph search-based algorithms, sampling-based algorithms, intelligent optimization algorithms, mathematical model-based algorithms, and hybrid algorithms. Graphsearch-based algorithms include Dijkstra's Algorithm [3], A* algorithms [4], and hybrid A* algorithms [5]. Sampling-based algorithms include the Rapidly Exploring Random Tree (RRT) algorithm [6] and RRT* algorithm [7]. Intelligent optimization algorithms include the Genetic Algorithm [8], Ant Colony Optimization (ACO) [9], and Particle Swarm Optimization (PSO) [10]. Mathematical model-based algorithms include the Dynamic Programming (DP) algorithm [11]. Hybrid algorithms [12] combine the advantages of multiple algorithms to improve path quality and search efficiency. The Intelligent Bug algorithm [13] and its variants are currently the most commonly used methods for solving local path planning problems. Among these path planning algorithms, the A* algorithm is recognized as one of the most efficient direct search methods for solving the shortest path in static road networks. Due to its balance of optimality and efficiency [14], it has been widely used. The A* algorithm combines [PERSON]'s comprehensive search with heuristic guidance, greatly reducing unnecessary node expansion and improving search efficiency. However, as more scholars have delved into the A* algorithm, several drawbacks have been identified, such as the generation of non-smooth paths and a decrease in search efficiency when the map size increases.Over the years, both domestic and international scholars have made various improvements to the A* algorithm. Common optimization strategies include improving the heuristic function, enhancing the search neighborhood, and modifying the search strategy. [PERSON] et al. [15] proposed a heuristic function mechanism with directional information, which enhances the algorithm's directional intelligence, helping to reduce computational complexity and time. [PERSON] et al. [16] introduced the DPIO method, which optimizes the heuristic function to enhance the efficiency and quality of path planning. [PERSON] et al. [17] optimized the heuristic function and introduced the Jump Point Search strategy, reducing the number of nodes that need to be expanded during the path search process, thus improving the algorithm's efficiency and reducing computational time and memory consumption. [PERSON] et al. [18] proposed a new 5-neighborhood search method, dynamically adjusting the search neighborhood (5-neighborhood, 4-neighborhood, or 3-neighborhood) based on obstacle constraints, thereby improving path smoothness. Among the aforementioned algorithms, the A* algorithm has shown improvements in both search efficiency and path smoothness. However, these improvements, due to the need for dynamic adaptation, can consume considerable computer resources. Additionally, the Jump Point Search strategy may not effectively handle dense obstacle environments in certain cases.