Research On Robot Path Planning Based On Ant Colony Algorithm

Robotics rise since the1960s, with innovative technology, application scope expands unceasingly. In recent years, with the continuous expansion of human activities in space, robotics in the defense, medical, rescue, disaster relief, counter-terrorism, manufacturing and everyday life have a very wide range of applications. Development of robot technology, mainly related to sensing technique, speech and image recognition technology path planning and other issues. Among them, two-dimensional, three-dimensional path planning is an important way to improve the robot safety, reliability, efficiency and level of intelligence.This paper introduces the background and significance of mobile robot path planning problems; research home and abroad mobile robot path planning technology development as well as the research direction and current hot issues. The establishment of environmental models, and path planning method search strategy design should be solved. A briefly introduces the traditional method and the emerging algorithm applied in path planning.Ant Colony Algorithm (ACA) is extracted from natural foraging behavior of ants’ activities. It has a good applicability for solving the problem of mobile robot path planning. This paper analyzes the behavior and characteristics of the system and the application of ACA on a typical TSP problem. Analysis of the advantages and disadvantages of the ant colony algorithm, improve strategies such as using a parallel strategy, meeting strategy, fallback strategy and integration genetic algorithms and ACA, to avoid the traditional ACA easy to fall into local optimum.Finally, based on improved ACA, the two-dimensional mobile robot path planning problem for the system modeling, the improvement strategy analysis and simulation experiment, the experimental results show that the algorithm is effective. Improved ant colony algorithm is robust:a potential parallelism; greatly improved operating efficiency. In this paper, the improved algorithm is further extended to three-dimensional environment of space, it is further validated the applicability of the model in three-dimensional space.