Trajectory Tracking Motion Optimization for Industrial Robots with One Degree of Freedom

The need for advanced optimization techniques is growing increasingly important as robotic tasks are becoming more complex, especially in industrial environments. A key focus in robotic systems is the creation of efficient and reliable trajectories that robots can execute smoothly. This thesis aims to develop feasible trajectories, that enable smooth robotic movement along designated paths, while ensuring both efficiency and reliability. To achieve this, the research focuses on methods that prioritize energy minimization, defined as the reduction in the variation of joint angles of the robotic manipulator. By optimizing the robot’s movements, unnecessary joint actions are reduced, resulting in enhanced overall efficiency and performance. The approach incorporates rotation around the Tool Axis as an additional degree of freedom, allowing the robot to handle a wider range of trajectories. The proposed solutions include the Greedy Graph Connectivity-Based method and the Graph Energy Minimization method, both of which rely on graph construction to identify a trajectory that minimizes total energy consumption. These methods were evaluated on synthetic trajectories and applied to practical industrial scenarios such as glue application for shoe manufacturing and bicycle painting. Results indicate that the Energy Minimization approach significantly reduces unnecessary joint movements and delivers more consistent outcomes compared to the Greedy Graph method. This work contributes to improved flexibility and efficiency in robotic motion planning within industrial settings, especially in applications where trajectory optimization is essential.