Mental Models for Intelligent Systems: eRobotics Enables New Approaches to Simulation-Based AI

Roßmann, Jürgen; Kaigom, Eric Guiffo; Atorf, Linus; Rast, Malte; Grinshpun, Georgij; Schlette, Christian

doi:10.1007/s13218-014-0298-z

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…5. As a central part of the "eRobotics" methodology, Virtual Testbeds [VTBs] previously have been applied in space and field robotics to support and accelerate the development of complex technical systems [67]. In a VTB, the target system is modelled and simulated in a comprehensive 3D simulation environment which -via plugins -provides components for, e.g., programming and control of kinematics, rigid-body and sensor simulation, as well as a variety of means to connect and exchange data with other systems.…”

Section: Virtual Testbed Support For System Development and Optimmentioning

confidence: 99%

Teaching a Robot the Semantics of Assembly Tasks

Savarimuthu

Buch

Schlette

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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We present a three-level cognitive system in a Learning by Demonstration (LbD) context. The system allows for learning and transfer on the sensorimotor level as well as the planning level. The fundamentally different data structures associated to these two levels are connected by an efficient mid-level representation based on so called "Semantic Event Chains". We describe details of the representations and quantify the effect of the associated learning procedures for each level under different amounts of noise. Moreover, we demonstrate the performance of the overall system by three demonstrations that have been performed at a project review. The described system has a Technical Readiness Level (TRL) of 4, which in an ongoing follow-up project will be raised to TRL 6.

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Section: Virtual Testbed Support For System Development and Optimmentioning

confidence: 99%

Teaching a Robot the Semantics of Assembly Tasks

Savarimuthu

Buch

Schlette

et al. 2018

IEEE Trans. Syst. Man Cybern, Syst.

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“…Although these methods were successfully tested and able to reduce the energy consumption of IR significantly, the approaches developed are only valid for a specific IR robot model. A simulation environment for analyzing the effect of IR motion planning with respect to the energy consumption of IR has been developed by Roßmann et al [24,25]. The developed simulation environment is based on calculating the energy consumption of every motor of the robot axis.…”

Section: Developing Energy-efficient Motion Planningmentioning

confidence: 99%

Reducing the energy consumption of industrial robots in manufacturing systems

Paryanto

Brossog

Bornschlegl

et al. 2015

Int J Adv Manuf Technol

160

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Reducing the energy consumption of industrial robots (IR) that are used in manufacturing systems has become a main focus in the development of green production systems. This is due to the reality that almost all automated manufacturing processes are using IR as the main component. Thus, reducing the energy consumption of IR will automatically reduce operating costs and CO 2 emissions. Therefore, a method for reducing the energy consumption of IR in manufacturing systems is desired. Firstly, this paper presents a literature survey of the research in energy consumption analysis of IR that is used in manufacturing processes. The survey found that current research in this field is focused on the development of simulation models of IR that are able to be used to predict its energy consumption. Secondly, a modular model to analyze power consumption and dynamic behavior of IR is developed. Afterward, an experimental investigation is carried out to validate and estimate the accuracy of the model developed. The investigation shows that the developed modular model can be conveniently used to optimize the industrial robot's operating parameters, which are commonly needed for production planning and at the process optimization stage. In addition, the investigation shows that the process constraints, environment layout, productivity requirement, Paryanto ( ) · M. Brossog · J. Franke as well as the robot payload and operating speed are the key factors that must be considered for optimizing the productivity and efficiency of IR.

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“…Their experiments showed that energy consumption was reduced by 45 %. Also, Roßmann et al [24] generated a path movement method by using artificial intelligence. Their simulation proved that energy consumption decreased from 24.5 J to 3.5 J. Rassõlkin et al [20] discovered that the energy consumed by the Industrial robot in the vertical motion was lower than in the horizontal motion.…”

Section: Fig 2 Energy Consumption (Colour Map) and Motion Time (Isoli...mentioning

confidence: 99%

Influential Aspects to Robotic Cell Energetic Efficiency: Overview

Bocák

Holubek

2022

Research Papers Faculty of Materials Science and Technology Slovak University of Technology

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This paper focuses on the input parameters that affect the resulting efficiency of a robotic workplace for industrial use. The aim of the investigation was to find which parameters most significantly affect cycle time and electricity consumption, and which way of movement is the most suitable for robotic operation. By finding the most suitable parameters, we obtain the information on how to build the most efficient robotic workplace. The investigation focused mainly on the energy consumption and layout of the robotic work cells components that significantly affect the cycle time and potentially save electric energy.

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Mental Models for Intelligent Systems: eRobotics Enables New Approaches to Simulation-Based AI

Cited by 17 publications

References 5 publications

Teaching a Robot the Semantics of Assembly Tasks

Teaching a Robot the Semantics of Assembly Tasks

Reducing the energy consumption of industrial robots in manufacturing systems

Influential Aspects to Robotic Cell Energetic Efficiency: Overview

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