Abstract
The ant colony optimization algorithm is an effective way to solve the problem of unmanned vehicle path planning. First, establish the environment model of the unmanned vehicle path planning, process and describe the environmental information, and finally realize the division of the problem space. Next, the biomimetic behavior of the ant colony algorithm is described. The ant colony algorithm has been improved by adding a penalty strategy. This penalty strategy can enhance the utilization of resources and guide the ants to explore other unknown areas by using the worse value in the search history to enhance the volatility of the pheromone.
Highlights
Unmanned vehicle path planning explores a feasible path in the known or unknown environment by sensing the surrounding environment
Path planning problem expresses the search of a route from the start point to the end point and presents an optimal path among all reachable paths [1]
That can be said as a kind of positive feedback phenomenon of the ant group during the foraging process [5]. It is because of this positive feedback mechanism that the ant colony can search for food more quickly. This algorithm has strong global search ability, can perform parallel and distributed computing, and has fast convergence speed and strong adaptability [6], so it has been widely used in solving path planning problems
Summary
Unmanned vehicle path planning explores a feasible path in the known or unknown environment by sensing the surrounding environment. It is because of this positive feedback mechanism that the ant colony can search for food more quickly This algorithm has strong global search ability, can perform parallel and distributed computing, and has fast convergence speed and strong adaptability [6], so it has been widely used in solving path planning problems. In the literature [8], the author can avoid the blindness of initial planning by adjusting the transition probability based on the classical ant colony algorithm and introducing relevant strategies to solve the deadlock problem. The simulation experiment proves that the algorithm is superior to the classical ant colony algorithm, which can effectively guide the mobile robot to avoid dynamic obstacles in the environment, obtain the optimal or suboptimal path without collision, and better adapt to the changes of the environment. Since each step of the robot is from the center of one grid to the center of anpoffitffiher adjacent grid, the motion step of the robot is R or 1 2R
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