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In order to improve the efficiency in path planning of security robot, this paper addresses to construct three kinds of grid obstacle models from simple to complicated, and use the integrated ant colony-particle algorithm to make global path planning. For further explaining the effectiveness of the combined algorithm implementation, this paper compares the different effects in path planning of the traditional ant colony algorithm and the combined ant colony-particle swarm optimization from three aspects, which are the iteration number of searching the optimal solution, the shortest path length and the running time. It is found that the integrated ant colony-particle algorithm enhances the search efficiency of the optimal solution and reduces the number of iterations of the search, so as to complete the optimal path of the security robot from the start point to the end point. Introduction Nowadays, intelligent security robots are capable of continuous, autonomous, real-time motion and the acquisition of live environments. For mobile robots, how to make robots to complete a predetermined job autonomously in a complex environment is the key and difficult point of research [1]. In the monitoring of environmental information, mobile robots need to accurately grasp their own location and the location of obstacles in the environment, and make real-time path planning in order to safely achieve the goal-oriented movement. Path planning is to find a collision-free path from the initial state to the target state in the environment with obstacles according to certain evaluation criteria (such as minimum working cost, shortest walking route, shortest walking time, etc.)[2].The current path planning algorithms are roughly divided into two kinds which are traditional algorithms and heuristic algorithms. The traditional algorithms are roadmap method and grid method. Artificial intelligence algorithms are also widely used in mobile robot path planning, such as ant colony algorithm, genetic algorithm, neural network algorithm and so on. Ant colony algorithm is widely used in path planning of mobile robots because of its parallelism, strong robustness and global optimality. However, in practical application, we find that the ant colony algorithm is computationally intensive and solves for a long time and is prone to stagnation [3]. The particle algorithm can make up for the lack of ant colony algorithm in the stability and search time, so this article combines the ant colony and the particle algorithm to form a new path planning algorithm, which is applied in the pasture monitoring robot.

Application of Ant Colony-Particle Swarm Optimization Algorithms in Path Planning of Security Robots