Abstract Robotic technologies may improve productivity while reducing musculoskeletal disorders for employees in physically strenuous industrial activities, such as grinding. The automation of industrial processes still faces enduring challenges, yet contemporary manipulators can cope with and execute sophisticated tasks under different performing conditions. This research seeks to create a passive industrial robotic arm that will minimize muscle vibrations caused by repetitive movements during a grinding procedure. This work encompasses the complete forward and inverse kinematics of a 5-degree-of-freedom (DOF) robotic arm constructed with a particular trajectory in mind, emphasizing workspace evaluation and trajectory planning.In robotic motion analysis, kinematics, which characterizes motion without taking into account the forces causing it, is essential. In order to determine the end-effector's position and orientation and enable multifunctional robotic applications, both forward and inverse kinematics are necessary. A mathematical model for forward kinematics was developed using the conventional Denavit-Hartenberg (D-H) convention, which predicted and simulated the position and orientation of the end-effector. To identify the necessary joint variables, inverse kinematics solutions were obtained by combining algebraic and geometric methods.The kinematics were validated and analyzed through MATLAB with the Peter Corke Robotics Toolbox, while the robotic arm was designed and simulated on SOLIDWORKS. As part of the robotic arm system performance assessment, several simulations and experiments were conducted, including but not limited to workspace analysis, joint trajectory planning, tool-path generation, and end-effector tracking. It was shown with results that random sampling approaches to high-dimensional kinematic problem spaces, visual representations of the workspaces for the manipulators, are intuitive. This proves the robotic arm's ability to reach designated points with sufficient precision, flexibility, and spatial coverage, confirming its operational feasibility for complex tasks in an industrial environment.
Thilakarathna et al. (Tue,) studied this question.