粒子群优化
人工神经网络
遗传算法
反向动力学
运动学
算法
均方误差
适应度函数
混合算法(约束满足)
计算机科学
控制理论(社会学)
反向
工程类
机器人
人工智能
数学
机器学习
控制(管理)
约束逻辑程序设计
统计
物理
几何学
约束满足
经典力学
概率逻辑
作者
Naseeb Singh,V.K. Tewari,Prabir Kumar Biswas,L.K. Dhruw,Rakesh Ranjan,Abhishek Ranjan
摘要
Abstract This study presents a particle swarm optimization (PSO) algorithm‐assisted neural‐network‐based inverse kinematics solution for a 4‐DoF (degree‐of‐freedom) cotton harvesting robot. A novel setup was developed to measure the three‐dimensional locations of in‐field cotton bolls. Dimensional optimization of the manipulator was conducted using the PSO algorithm to minimize torque requirements at joints. With the optimized links’ lengths, the targeted end‐effector positions were achieved effectively (coefficient of determination ( R 2 ) > 99.88). The genetic algorithm optimized the neural network architecture to include three hidden layers with [64 64 32] neurons, identifying the Tanh activation function as the optimal configuration. A custom loss function was used during the training of artificial neural network (ANN). Using angles predicted by the trained ANN, the end‐effector reached targeted positions with positioning errors below 13.0 mm. A hybrid model consisting of an ANN and PSO algorithm was developed to further reduce the error. This trained hybrid model resulted in a positioning error below 1.0 mm with inference time of 6.07 s during simulation phase. As compared to the ANN and PSO algorithm, hybrid model reduced the positioning error and inference time (>40.0%), respectively. For hybrid model, the mean percentage errors of 0.25%, 0.39%, and 0.84% were observed along the x‐ , y‐ , and z ‐axis. A positioning error below 9.0 mm occurred during evaluation of the hybrid model with the fabricated manipulator. Hence, the developed hybrid model precisely determines the joint angles, allowing the end‐effector of the cotton harvesting robot to reach at targeted pose with minimum error.
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