基于单目视觉的多种群粒子群机械臂路径规划算法

针对复杂静态背景下具有多约束条件的机械臂路径规划问题，提出一种新的基于单目视觉的多种群粒子群算法。首先，使用图像差分算法消除背景，再利用轮廓包围法找出目标物体区域，然后使用模型位姿估计法求出目标物体位置。其次，提出一种多种群粒子群算法，根据目标物体位置演化出机械臂最优角度。该算法将精英种群与子种群组成多种群粒子群，使用预选择与交互机制使算法跳出局部最优。仿真结果表明，与实际坐标对比，使用背景消除法后求出的物体位置坐标误差较小。将多种群粒子群优化算法与现有的一流演化算法对比，结果表明对不同位置的物体，该算法获得的路径平均自适应度与均方误差（MSE）最小。

Path planning algorithm of multi-population particle swarm manipulator based on monocular vision

Aiming at the path planning problem of manipulator with complex static background and multiple constraints, a new multi-population particle swarm optimization algorithm based on elite population and monocular vision was proposed. Firstly, the image difference algorithm was used to eliminate the background, then the contour surrounding method was used to find out the target area, and the model pose estimation method was used to locate the target position. Secondly, a multi-population particle swarm optimization based on elite population was proposed to obtain the optimal angles of the manipulator according to the target position. In this algorithm, the elite population and the sub-populations were combined to form the multi-population particle swarm, and the pre-selection and interaction mechanisms were used to make the algorithm jump out of local optimums. The simulation results show that compared with the real coordinates, the coordinates error of the object position obtained by background elimination method is small; compared with those of the state-of-the-art evolutionary algorithms, the average fitness values of the paths and the Mean Square Errors (MSE) obtained by the proposed algorithm are the smallest for the objects in different positions.