Energy Saving Planner Model via Differential Evolutionary Algorithm for Bionic Palletizing Robot

Abstract

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

Active suspension LQR control based on modified differential evolutionary algorithm optimization

“…In standard differential evolution optimization algorithm, the scaling factor 𝐹 and crossover probability 𝐶𝑅 take fixed values, however, it is more difficult to determine an appropriate parameter in the optimization process [20]. As the scaling factor 𝐹 increases, the degree of population differentiation decreases, and the phenomenon of local extremes in evolution occurs, causing the population to converge prematurely.…”

An Overview of Energies Problems in Control and Robotic Systems

“…Deng Y. et al [18] give a novel differential advanced algorithm to solve the instability of the initial path parameters and its bad effects on reducing energy consumption. They developed a simplified analytical way of the palletizing robot.…”

“…This power-saving method improves the initial parameters of the paths collected by the bionic demonstration system to minimize operating power consumption. Due to the actual experimental results and simulation, the optimized path parameters could effectively minimize the energy consumption by 16% [18]. Pellicciari et al [19] searched the methods to measure the power consumption in an application used in the automotive industry, which contains about 74% of the total amount of industrial robots in the industry.…”

An Overview of Energies Problems in Robotic Systems

“…Deng et al [12] proposed a new approach to address the instability of initial path parameters and their negative impact on energy consumption reduction. They developed a simplified analytical model for the palletizing robot and then combined it with a differential evolution algorithm to form a power-saving method.…”

“…This approach improved the initial path parameters gathered by the bionic demonstration system to minimize power consumption during operation. Through both actual experimental results and simulations, the optimized path parameters were found to effectively reduce energy consumption by 16% [12]. Moreover, there were methods developed based on the concept of saving wasted energy to reduce power consumption.…”