Optimal trajectory planning for industrial robots: Minimizing time,jerk,and energy consumption using LSTM for energy profile modeling

Abstract

Due to the continuous rise in energy costs for industrial robots (IRs), energy conservation has become one of the primary concerns in modern industry. This article presents a new, efficient approach for optimal trajectory planning of industrial robots in terms of time, jerk, and energy, while taking into consideration the kinematic constraints of the robot. A fifth-order B-spline interpolation method is adopted for curve fitting the trajectory in joint space to ensure smooth and continuous jerk in the robot’s articulation movements. The adjustable parameters of the trajectory are then optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to minimize traveling time, jerk, and energy consumption (EC) throughout the trajectory. Unlike time and jerk, establishing a precise mathematical relationship between energy consumption and the dynamics of a robot across different trajectories is challenging and not easily applicable. This study uses the deep learning technique long short-term memory (LSTM) to accurately uncover the quantitative relationships between trajectory operational parameters and energy consumption. The main advantage of this approach, compared to other proposed optimizations, is that it can predict and optimize the robot’s energy consumption before the real-time execution of the task, and it does not require setting a priori the overall execution time of the trajectory. The results on a six degree of freedom industrial robot demonstrate that the suggested approach reduces energy consumption by 49.87% and average absolute jerk by 60.56% compared to chord length distribution method with the same trajectory execution time.