Application of Hybrid Nelder-Mead Bat Algorithm to Improve the Grasp Quality during the Automated Robotic Grasping

Abstract Robots are introduced in the manufacturing industry to automate the manufacturing process to meet the high productivity in the manufacturing industry as well as for manufacturing high quality and low-cost product for the customer. Robots are playing a crucial role in handling the object in pick and place operation, palletizing, welding, handling of materials and other operations in automated manufacturing industries. Robot-mounted with the gripper as the end effector need to identify the best position on the object for positioning the tip of the gripper for a successful grasp. The key to a successful and a stable grasp in automation is to obtain the most appropriate grasp planning and grasp synthesis for the desired task. In this work, a hybrid Meta heuristics method has been developed by combing an efficient local search method known as Nelder Mead method with the Bat algorithm and the new proposed hybrid algorithm is known as the hybrid Nelder-Mead Bat algorithm (HNMBA). The proposed algorithm was introduced for solving the automated grasp synthesis and grasp planning problem in the robotic grasping in an automated manufacturing line. The fitness function for the considered robotic grasp synthesis problem has been formulated using the largest ball grasp quality criteria. The proposed methodology is validated for the tessellated 3D objects, and the obtained results are compared with the previously proposed methods.