Intelligent Control of Robots with Minimal Power Consumption in Pick-and-Place Operations

In many industries, such as assembling, welding, packaging, quality control, loading, and wrapping, a specific operation is requested, which is to pick processed objects in a given area of the workspace and hold them there for a rather long time compared with picking. The current study aims to minimize the power consumed by robots in pick-and-place applications with long-term placing and short-term picking operations. The main contribution of the paper is in the development of an approach that ensures the low power required by the robot by selecting the best robot joint configuration for object placement and providing intelligent control of robot joints for object-picking. The proposed and tested methodology is based on the mutual solution of the forward kinematics, inverse kinematics, inverse statics, and reinforcement learning problems in robotics. An appropriate neural-network-based controller is designed. In this work, model development, simulation, and experimental stages are described. As a result, several MATLAB/Simulink™ models and simulation methods are designed for efficient robot control and an appropriate neural-network-based controller is developed. The experiment conducted on the IRB1600 robot demonstrates that up to 18% of the consumed power may be saved thanks to an optimally chosen joint configuration.