ACO-based ROS robot global path optimization method

Abstract

The invention discloses an ACO-based ROS robot global path optimization method. The method comprises the steps of setting ant colony algorithm initial parameters; modeling an environment map by utilizing a grid strategy, selecting a preferred area according to a starting point and a target point to increase a pheromone initial value, and performing differential increment setting on pheromones in combination with the sum of distances between nodes and the starting point as well as the target point and the distance proportion; searching an optimal path, initializing a tabu table, adding the tabutable into the starting point, searching a next reachable node by utilizing a state transition probability, and stopping searching until the node selected by the ant is a target point; judging whether the loop iteration frequency reaches a preset value or not, if so, storing information, and if not, continuing path search; and ending until the optimal path is found. According to the method, the convergence rate and the optimization effect of the algorithm are greatly improved, and the effectiveness and feasibility of the improved ant colony algorithm for solving the robot path planning problem are verified through MATLAB simulation experiments.

Description

technical field [0001] The invention relates to the technical field of robot global path optimization and ant colony algorithm, in particular to an ACO-based ROS robot global path optimization method. Background technique [0002] In the process of the development of intelligent robots, robot autonomous obstacle avoidance planning plays a pivotal role in the field of intelligent robot research. Path planning and autonomous obstacle avoidance capabilities are an important indicator to measure the intelligence of robots. Inferiority directly determines the level of intelligence of the robot. Having autonomous obstacle avoidance and path planning capabilities means that the robot has the ability to move freely, which will greatly improve the work efficiency and mobility of the robot, and is conducive to the application of the robot to more fields. To achieve better autonomous obstacle avoidance and path planning for robots, optimizing path planning and obstacle avoidance algori...

AI Technical Summary

Problems solved by technology

[0003] With the rapid development of science and technology and a large number of applications of robots, people's requirements for robots are getting higher and higher, especially in terms of intelligent robots, and robot autonomous path planning is an important step in realizing the intelligentization of robots. Path planning refers to planning an optimal path starting from the starting point without collision and safely reaching the designated target position in a known environment. So far, many scholars at home and abroad have carried out a lot of research on robot path planning, such as the traditional method There are Dijkstra algorithm, A algorithm, artificial potential field method, etc. With the deepening of research, traditional algorithms have been unable to solve the problem well, so many scholars have proposed a series of intelligent algorithms, such as genetic algorithm, flower Pollination algorithm, particle swarm algorithm, firefly algorithm, different algorithms have their unique advantages under different limited conditions, but there are also shortcomings

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