Analysis of the Development of Control Algorithms for Artificial Intelligence-Based Manipulator Robots on Industrial Environment
Keywords:
Robot Manipulator, Inverse, Power Consumption, Artificial Intelligence, DRL, Industry, ControlAbstract
Robot manipulators are one of the key components in industrial automation, especially in assembly, object removal, and precision operation tasks. However, the main challenge in its implementation is the ability to adapt to a dynamic and unstructured work environment. This study aims to analyze the development of an artificial intelligence-based (AI)-based control algorithm on robot manipulators, with an initial approach using the inverse kinematics (IK) method, Pixy2 visual sensor, and PID control. The system was tested in a controlled environment and showed an average position error of only 1–4 mm, with relatively low and stable power consumption. However, this approach still relies on static environmental conditions and system parameters that must be calculated manually. To improve flexibility and adaptability, the Deep Reinforcement Learning (DRL) method was identified as a more advanced AI solution. DRLs allow the system to learn from experience and respond to environmental changes autonomously, but with the consequent greater power requirements due to high computing loads and dynamic motor control. The results of the analysis show that the integration of DRLs can improve the intelligence and independence of manipulator robots, but requires more complex electrical system planning and energy management. In conclusion, the IK approach is suitable for systems with limited resources and structured environments, while DRLs are more suitable for dynamic and complex industrial scenarios with adequate power infrastructure support.
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