# Path Planning of Rail-Mounted Logistics Robots Based on the Improved Dijkstra Algorithm
## 1 Introduction
GA is based on natural selection and heredity, producing optimal solutions from the previous generation. Recently, [PERSON]'s team [13] used a genetic algorithm to find the high-performance position of a task with the goal of realizing the efficient collaborative operation of a human arm and a robotic arm. Although the genetic algorithm has been widely used to solve MTSP, most of the solutions involve transforming MTSP into TSP. The focus of this research is on how to express MTSP via chromosome coding, and how to improve the genetic algorithm by changing the chromosome coding method. Due to the limitation of its improvement method, the role that the genetic algorithm can play in solving this problem is limited. The A* and Dijkstra algorithms are graph search algorithms that directly find a path in a graph. The A* algorithm is a heuristic search algorithm, which needs to decompose the operating environment into raster data during the working process for computing the cost function. However, the A* algorithm has high requirements for the operating environment of a robot, and when the operating environment is complex, this decomposition takes a long time. Additionally, when the running environment changes, the decomposition needs to be done again, which affects the efficiency of the A* algorithm. Among these, [PERSON]'s method [14] is a traditional approach that was put forth in 1956 to address the problem of finding the shortest path between any two points on a map. [PERSON]'s algorithm, which has been used extensively to solve the optimal path issue [15; 16], has the advantages of a simple structure, good convergence, and high practicability compared to [PERSON]'s algorithm, the A* algorithm, and other global path planning algorithms. The efficiency of the technique is extremely low because it must explore every point in space, which requires a significant amount of computation and memory. Numerous complicated issues, including navigational lines, dynamic planning, logistics scheduling, etc., can be resolved by improving and optimizing [PERSON]'s algorithm. [PERSON] et al. [17] improved [PERSON]'s algorithm by using the traceable priority queue data structure to achieve dynamic and effectively reduce the running time and running memory, but the performance of the solution may be somewhat less than the best resource bounds known so far. [PERSON] et al. [18] enhanced [PERSON]'s algorithm by adding a time window conflict judgment model, which is appropriate for multi-robot path planning and may avoid conflicts. In order to achieve the shortest distance and shortest time path planning for AGV (Automated Guided Vehicle) under rectangular environment graphs, [PERSON] et al. [19] suggested an improved [PERSON] method with the addition of running time. In order to accelerate the path-planning process, [PERSON] et al. [20] integrated the ant colony method and [PERSON]'s algorithm for the airport AGV path-planning scenario. [PERSON] et al. [21] modified the Dijkstra method by including the Bezier curve to realize route optimization, made the path smoother, and could track the obstacles in the way in real time and update the path. They performed this while taking into account the continuity of the path. To address the fuzzy shortest path problem in graphics, [PERSON] et al. [22] suggested an adaptive Dijkstra method based on trapezoidal image blur numbers. The dynamic window algorithm (DWA) and [PERSON]'s algorithm were integrated by [PERSON] et al. [23] and applied to intelligent cars, which performed well in path-planning navigation and obstacle avoidance. Additionally, the traditional Dijkstra method can be used for network communication's shortest route selection, but not for paths with limited resources or continuity. In the context of optical networks, [PERSON] et al. [24] recently proposed a general Dijkstra algorithm that effectively finds the shortest path using a network of discrete resources with continuity and continuity requirements.