Optimizing robotic configurations for efficient motion planning in industrial automation through Dijkstra's Algorithm

In the context of industrial automation, optimizing the performance and efficiency of robotic systems is crucial for enhancing productivity. This thesis addresses the challenge of selecting optimal robot configurations to minimize trajectory execution time and reduce program development time for industrial machines. The core of this research lies in the implementation of an optimization algorithm based on Dijkstra's algorithm, a graph theory method renowned for finding shortest paths. The developed algorithm models robot configurations as nodes in a graph, with transitions between configurations represented by edges weighted according to the time required by the robot’s joints to move between configurations. This approach allows for the systematic identification of the most time-efficient paths through the joint-space configurations. The research focuses on two anthropomorphic industrial robots, the ABB IRB1100 and IRB1200, chosen for their advanced kinematic characteristics and extensive application in industrial settings. By applying the optimization algorithm to these models, the study evaluates its effectiveness in minimizing trajectory times across different robotic platforms with varying physical and kinematic properties. Simulations were conducted in ABB’s RobotStudio and real-world test bench environments to validate the algorithm's performance.