Energy-efficient trajectory planning for robot manipulators

• Energieeffiziente Trajektorienplanung für robotische Manipulatoren

Lorenz, Michael; Corves, Burkhard (Thesis advisor); Müller, Andreas (Thesis advisor)

1. Auflage. - Aachen : Apprimus Verlag (2021)
Book, Dissertation / PhD Thesis

In: Getriebetechnik, Maschinendynamik und Robotik
Page(s)/Article-Nr.: 1 Online-Ressource : Illustrationen

Dissertation, RWTH Aachen University, 2021

Abstract

Characterized by the growing awareness of anthropogenic global warming and its disastrous effects on the Earth’s climate system, society is establishing an increasing demand for effective environment and climate protection. Since human energy requirements represent a significant contribution to global warming, the industrial sector gradually focuses on the sustainable utilization and transformation of available raw materials and energy resources. In this respect, manufacturing industries provide considerable potential for improving the energy efficiency of future product development as well as production processes. Against this background, the present thesis introduces an innovative methodology for the efficient design and optimization of technical motion profiles performed by automatic machines and robots. In contrast to today’s industrial automation primarily based on time-optimized motion design, an ecological arrangement of future manipulation processes is thus to be realized by means of efficient trajectory planning. In particular, the proposed approach represents an extension of discrete optimal planning towards universal manipulation and transportation tasks as well as arbitrarily complex robot systems. Conceptually based on graph theory, different methods of quasi-random state space sampling and shortest path exploration are analysed and tested for generalized motion patterns. To this end, the proposed research approach involves a standard definition of spatial and temporal motion states as well as the heuristic cost evaluation of admissible state modifications. Subsequently, the directed set of discrete state transitions can systematically be searched for cost-efficient motion paths using suitable exploration algorithms. For validation purposes, discrete optimal planning is compared to node-based trajectory optimization generally based on the analytical formulation and numerical optimization of elementary motion profiles. In this context, the conducted simulations successfully confirm the reliable identification of cost-efficient trajectories indicating a significant savings potential due to the spatial and temporal modification of common reference motions. In addition, one central focus of this thesis is devoted to regenerative energy recuperation enabling supportive energy reductions through active drive management. Further investigations involve the redundant actuation of parallel kinematic mechanisms, thus providing the active and beneficial redistribution of actuator forces by internal preload control. In summary, the methods developed within this thesis create the framework for energy-efficient motion design for general robot applications in multi-dimensional space. In contrast to classical planning strategies, discrete state space sampling additionally ensures a consistent shortest path exploration at significantly reduced runtime complexities. Assuming large-scale and universal applicability of energy-efficient trajectory planning, the presented approach demonstrates high potential for the sustainable design of dynamic motion paths, finally supporting an ecological arrangement of general manufacturing processes.

Institutions

• Chair and Institute of Mechanism Theory, Machine Dynamics and Robotics [411910]