Towards AI-Driven Extension Control: Power Consumption Analysis in Robotic Manipulators with Extendable Joint Mechanisms

Robotic arm is widely utilized in various industries all over the world. In general, the purpose is to automate any possible process and to replace the human workforce with more reliable and functional technology. This work examines the robotic arm and the influence of the extra degrees of freedom in the form of extendable parts. This work will design and analyze a robotic arm with three joints that can rotate on three axes, including the end effector and three extendable parts. The focus of the study was shone on the influence of so-called extendable parts on power consumption. Three sets of results were attained. In total, 15 simulations were carried out with 5 simulations being devoted to each set. The first investigation with no extendable parts was carried out with varying the value of load on the end effector. The second set of results were gathered with the extendable parts being activated while the second joint was rotated. Finally, the third set of results were collected by activating the extendable parts with the first joint rotation. By analyzing the outcomes, it can be concluded that the extension would be more efficient if activated by the second joint. It can be concluded that it is important to understand when to activate the links to minimize the power consumption and other important values. Hence, the use of algorithmic advantages to calculate the best performing positions or paths will play a major role in the functioning of the robotic arm and in efficiency.