Robot Grid Map Shortest Path Planning Based on MATLAB Genetic Algorithm
In the field of robot path planning, genetic algorithm is a commonly used and effective optimization method. This article will introduce how to use MATLAB to write the shortest path planning algorithm based on genetic algorithm for robot grid map, and provide the corresponding source code.
1. Problem description and modeling
Suppose a robot needs to find the shortest path on a given grid map. A raster map consists of a series of discrete grid cells, each of which can be an obstacle (meaning impassable) or free space (meaning free movement). The robot can move up, down, left and right, but not diagonally.
Our goal is to find the shortest path from the starting point to the goal point, making the robot move on the path with the least cost. The movement cost of a path can be defined as the sum of the cumulative costs of each unit on the path, where the cost can be set according to the actual situation, such as the distance between units or the difficulty of passing obstacles.
To solve this problem, we can use genetic algorithm to find the optimal solution. The genetic algorithm is an optimization algorithm based on the principle of biological evolution, which gradually optimizes the quality of the solution by simulating the processes of natural selection, crossover and mutation.
2. Algorithm design and implementation
- Initializing the population
In the genetic algorithm, each individual represents a path solution. First, a batch of initial paths need to be randomly generated as a population. The initial path can be generated using a random number generator.
function population = initializePopulation(populationSize, gridMap