An improved ant colony algorithm for path planning of manipulator

In order to solve the path randomness of delta manipulator in sorting corresponding objects, this paper proposes a path optimization algorithm ant colony algorithm based on ordinary path sorting. The random path of delta manipulator not only affects the sorting efficiency, but also increases the operation cost. After the object coordinates are recognized by computer vision, they will be converted with the coordinates of the manipulator itself, so as to get the object coordinates under the delta manipulator. The coordinates are optimized by path optimization algorithm in the system code, and the optimal path of the suction head of the delta manipulator can be obtained. Therefore, a good path optimization scheme directly determines the operation state of delta manipulator. In view of the fact that ant colony algorithm is easy to fall into the local optimal solution, this paper optimizes the ant colony algorithm, realizes the optimized algorithm with MATLAB language, and carries out simulation test. In MATLAB 2018 environment, the running time of different cases is compared, which shows the effectiveness of the algorithm.