Managing Energy Consumption of Linear Delta Robots Using Neural Network Models

A new approach to managing linear Delta robots is developed and two problems of their energy-efficient operation are solved in this work based on neural network models. The first solution concentrates on the minimization of the power consumed by the robot at various tool positions as a function of joint configurations. This problem is actually faced in industrial processes, in which the steady-state placing and holding phases of the pick-and-place cycle continue for much more time than picking, such as quality control, welding, packaging, and wrapping. The second solution relates to searching for the shortest path through all targets, considering all possible robot joint configurations, so that total energy consumption is minimal. This problem is essential to processes that require the fastest picking and placing cycles, such as assembling, loading, or painting. The outlined power monitoring procedure aligns with detailed power estimation at different joint configurations, with joint route optimization used to reduce energy demand. The feasibility and applicability of the proposed neural network-based methodology are confirmed via experimental testing on the Festo EXPT-45-E1 robot.