Mechanical Dual-arms’ Collision-free Path Planning Of Robot Based On Improved Ant Colony Algorithms

The application of mechanical dual-arms’ robot has enhanced the robot’s adaptability of complex tasks and improved the utilizable rate of robot’s working space. The research of mechanical dual-arms’ harmonious movement has become a chief orientation of robot in further developing.This paper has taken the AS-MRobot and ASR robot which were produced by GRANDAR limited company of Shanghai as study object, built experimental platform with Visual C++program software. In the experimental platform, two kinds of experimental could be realized, they are dual arms’ collision-free and dual arms’ grab objects. For the experiment of dual arms’ collision-free, two plans be designed, respectively, they are master-arm stay in the workplace while slave-arm action with collision-free, and dual arms real-time action with collision-free in the workplace. Through the experiment verify the algorithm effectiveness of ACA and ACA-PSO.Based on the introduction of basic principle of ant colony algorithm, summarize the ant colony algorithm improved scheme, including its improved ant colony algorithm with other intelligent algorithm fusion of the two, which improved intelligent algorithm based on summarizing the improved ant colony algorithm improvement strategies. According to the actual problem arm movement planning, choose and particle swarm of fusion (PSO), using the ACA-particle swarm algorithm of ant colony optimization function, optimization of a solution to the output for the global optimal solution.By studying the foundation of time-varying C-space and the collision detection based on false measure distance function, time-varying C-space of slave arm has been built. Ant colony algorithm has been used to plan the collision-free path for slave mechanical arm in C-space and primary effect has been achieved. Because of the limitation of the basic ant colony algorithm for this system, and in order to improve the search speed and accuracy, we have studied on the method of compartmentalizing result and making the system further searching the optimal path when there are multi-feasible paths or non-feasible path. The experimental results show that the improved ant colony algorithm is better than the basic one in the global searching performance and searching speed, and can achieve the function of collision-free movement between the two mechanical arms.