Florian Stuhlenmiller

Trajectory Optimization Considering Energy Consumption and Service Life Towards Efficient Robotic Systems


Front	Back

ISBN:

978-3-8440-8506-8

Series:

Forschungsberichte Mechatronische Systeme im Maschinenbau
Herausgeber: Prof. Dr.-Ing. Stephan Rinderknecht
Darmstadt

Keywords:

Robotic Systems; Efficiency; Trajectory Optimization

Type of publication:

Thesis

Language:

English

Pages:

186 pages

Figures:

50 figures

Weight:

276 g

Format:

21 x 14,8 cm

Bindung:

Paperback

Price:

48,80 € / 61,10 SFr

Published:

March 2022

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Abstract:

Industrial manufacturing benefits from the automation capability and the flexibility of robotic systems. Accordingly, a robot is designed to perform various tasks programmed by the user. However, a user has almost unlimited possibilities to specify the location and timing of the motions. In order to find optimal trajectories, various optimization methods are presented in the literature, for example, to minimize energy consumption or maximize the service life. As interactions between both optimization criteria are not considered so far, this thesis's objective is to optimize trajectories considering energy consumption and service life.
To this end, both criteria are modeled based on the dynamic characteristics of a serial manipulator and the actuator properties. This approach enables the expression of the required electrical power depending on the trajectory. In addition, the reliability is modeled, generating a single criterion for the service life of the robot. The models are then used to formulate an optimal control problem to optimize trajectories. Optimization results indicate a distinct potential for improvements and a conflict of interest between energy consumption and service. Experimentally evaluated energy consumption and loads on the gearboxes show the general applicability of the proposed optimization approach.
Based on the optimization results, the associated economic and environmental impact is estimated. In addition, corrective maintenance is considered based on the modeled component reliability. Evaluated results confirm the potential of trajectory optimization considering energy consumption and service life combined with maintenance methods, life cycle costing, and life cycle assessment to support user decision-making.