Optimizing cotton-picking robotic manipulator and inverse kinematics modeling using evolutionary algorithm-assisted artificial neural network

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 (R²) > 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.