Kinematical Modelling and Its Analytical Inverse Kinematic Solution for the Handling Mechanism of an Agricultural Robot

Early detection of the crop diseases helps to prevent failure in the amount and the quality of the production. In agricultural robotics, the idea of a disease detection robot is a fresh and an innovative hot-button topic. The exclusion of the diseased parts from the strawberry plants for further analyses is one of the main tasks of a recently developed strawberry robot. To this purpose, the handling mechanism in the robot needs to achieve an accurate manipulation task to reach the target. Reaching, cutting and storing the diseased leaf are challenging and delicate processes during the procedure of the handling mechanism operation in the field. The manipulation task of the mechanism is succeeded when the inverse kinematic relations from workspace to joint space are defined properly. The inverse kinematic analysis is usually subjected to the restrictions due to the limitations in mechanical design of the mechanism, hardware components and operation environment of the robots as well as the morphology of the target. This study proposes a set of analytical algorithms to solve the inverse kinematics problem of the handling mechanism under certain constraints. First, proposed analytical approach is based on the calculation of the joint variables by solving only the 3D position information of the target since the output from image processing algorithms of vision subsystem in the ground robot is only the location of the diseased point. The position of target point is the only output from vision subsystem and this data will be given as an input to the proposed algorithms. Second, the mechanism has certain restrictions on its geometrical construction and the joint actuators\u27 capacity. Hence, these restrictions limit the range of joint variables to be solved. Due to sudden and unpredicted nature of field conditions, the quickness of handling mechanism inverse kinematics solution\u27s execution has a vital effect on the success of the picking task of the robot. Another essential factor is to use the battery life of the robot effectively, by minimizing energy consumption. Therefore, the effectiveness of the proposed algorithm is decided by comparing the developed performance indices of consumed energy and CPU time cost via numerical solution namely, a nonlinear constrained optimization method under same restrictions of inverse kinematics problem. Performance of both algorithms is observed by the simulations in MATLAB® and laboratory set-up experiments