DIMENSIONAL OPTIMIZATION OF 7-DOF AGRICULTURAL ROBOT ARM

Agricultural automation has emerged as a potential solution to meet the growing need in the quickly changing farming business. For example, the tasks in palm oil plantations require precision, efficiency, and adaptability to navigate the complex environment. Robotics solve these challenges by enhancing farming and executing tasks such as cutting fresh fruit bunches that surpass manual labor. By examining the optimal dimensions synthesis for the robotic arm and integrating the Denavit-Hartenberg (DH) parameters for kinematic modeling, this study aims to enhance the degree of freedom, enabling precise and flexible movements, which is crucial for navigating around palm oil trees. This research evaluates two optimization algorithms, artificial bee colony (ABC) and particle swarm optimization (PSO), specifically tailored for robotic arms in the agricultural sector and intended to improve performance. Kinematic modeling simulations are conducted using MATLAB software. This research emphasizes optimization methods to ensure the accurate and efficient execution of tasks. The results indicate that the PSO algorithm outperforms the ABC algorithm in terms of error minimization. Specifically, the mean square error for PSO is 5.0433 x 10-6, compared to 9.3904 x 10-6 for ABC. These results demonstrate that the PSO algorithm provides more accurate and efficient task execution for the robotic arm in agricultural applications. Key Words: Optimization; Topological; Dimensional; Agricultural; Forward Kinematics; Inverse Kinematics