# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 1 Introduction  
With the rapid development of mobile robotics technology, path planning, as one of the core issues in mobile-robot autonomous navigation, has garnered widespread attention [1]. The goal of path planning is to find an optimal path from the starting point to the destination within a given environment while avoiding obstacles [2]. Its applications span across various domains, including material handling in industrial production, intelligent transportation in logistics, robot guidance in the service industry [3], and unmanned combat in military fields, demonstrating its broad potential for application [4]. Currently, mobile-robot path planning algorithms can be classified into traditional path planning algorithms, sampling-based path planning algorithms, and intelligent bionic algorithms [5]. Traditional path planning algorithms include the A* algorithm [6], the Dijkstra algorithm [7], the D* algorithm [8], and the artificial potential field method [9], while sampling-based path planning algorithms include the PRM algorithm [10] and the RRT algorithm [11]. Intelligent bionic path planning algorithms include neural network algorithms [12], particle swarm optimization [13], ant colony optimization [14], and genetic algorithms [15]. Genetic algorithms, as global optimization algorithms based on biological evolution principles, have gradually become effective tools for solving complex path planningproblems due to their strong global search capabilities and robustness [16]. However, the application of traditional genetic algorithms in robot path planning still has many shortcomings [17]. On one hand, the initial population quality is low, leading to slow convergence [18]; on the other hand, traditional selection strategies may allow exceptional individuals to dominate the population, causing the algorithm to fall into local optima and reducing population diversity and exploration ability [19]. Furthermore, the crossover operation lacks sufficient local search capabilities and may disrupt well-formed gene combinations, while the mutation operation lacks directionality [20], potentially damaging high-fitness individuals and making path quality difficult to control. To address these issues, various improvement methods have been proposed in the literature. For instance, in reference [21], a population initialization strategy based on linear ranking was proposed for the CBPRM algorithm. By assigning scores to environmental cells based on their distance to the target, linear ranking replaces the roulette selection method to determine cell selection probabilities. Starting from the initial point, the strategy excludes cells containing obstacles and sequentially selects available cells to construct a feasible path to the target point. If no available cells are found before reaching the target, path construction terminates. In reference [22], two special lists are used to select parents: one list contains high-fitness paths chosen by elite selection, and the other contains paths with different fitness values. This approach improves the quality of offspring while allowing low-fitness parents to explore the search space. During offspring generation, multiple potential offspring are created based on feasible crossover points, and the two paths with the highest fitness are selected after sorting. In traditional mutation operations, only one node typically has the chance to mutate. Reference [23] designs a dual AGV collaborative safety steering radius, integrating multiple factors into the global path planning algorithm to prevent steering collisions and balance the planning efficiency with map utilization. It introduces the starting- and ending-point posture information and initializes the path based on safety steering rules, thoroughly considering the potential for steering collisions between the dual AGVs at the start and end points, thus generating a safer initial path. Reference [24] introduces a Markov chain that can predict future states based on current ones.