Cycle Time Reduction Through Redundancy Optimization in Industrial Robotic Tasks

Task order and trajectory optimization can allow to increase the performance of industrial robotic applications. Moreover, robotic manipulators are often redundant, either intrinsically or functionally, and redundancy is usually not fully exploited. In literature, several works focus on the reduction of the cycle time by possibly exploiting redundant manipulators. However, the following aspect and opportunity is hardly or not considered: since a redundant manipulator can execute a task with $$\infty $$ possible configurations, the manipulator can start a task with a given configuration, then, while the task is under execution, it can reconfigure thanks to the $$\infty $$ possibilities so as to complete the task with a configuration as close as possible in the joint space to the one needed to execute the next task, thus further minimizing the cycle time. This work aims at reducing the cycle time of an operation considering these aspects by developing a redundancy optimization method. Numerical simulations are run to validate the approach showing promising results.