Representation and Programming for a Robotic Assembly Task Using an Assembly Structure

Nagai, Tatsuichiro; Aramaki, Shigeto; Nagasawa, Isao

doi:10.1109/cit.2007.173

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…Having the Assembly Structure, it is possible to build a method to program the assembly robot adopting force and position control [6]. Note that also high-level robot programming is called task-level, but the term 'task' is used here with a different meaning.…”

Section: Figure 2 Hierarchical Model Of Assembly Job Tasks and Operationsmentioning

confidence: 99%

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Antonelli¹,

Zeng²,

Aliev³

et al. 2021

FME Transactions

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Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the human co-workers. The method is presented with the help of a benchmark case study.

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Section: Figure 2 Hierarchical Model Of Assembly Job Tasks and Operationsmentioning

confidence: 99%

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Antonelli¹,

Zeng²,

Aliev³

et al. 2021

FME Transactions

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“…The programming of assembly tasks by using skill levels is shown in [15]. The state space of a task is described by four levels: Control primitive level, control skill level, skill level and task level.…”

Section: State Of the Artmentioning

confidence: 99%

Classification, Modelling and Mapping of Skills in Automated Production Systems

Backhaus

Ulrich

Reinhart

2014

Enabling Manufacturing Competitiveness and Economic Sustainability

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The ability to enable a quick modification and system-change is one of the essential requirements of future production systems. The two basic steps to adapt an automated production system to a new task are the reconfiguration and the reprogramming. Against this background, the central objective of this paper is the discussion of a concept to simplify the programming. Assuming the use of standard industry devices, there is a lack in the ability to gather the necessary information about the available equipment automatically. Each device allocates its inherent skills through different functions, commands and interfaces. A classification and a method to map the devices' skills to the program of automation systems have been developed to handle and interpret these skills. The concept and its application are described in this paper.

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“…If no interference found, the assembly task is performed by the real robot. In our system, the robot controller implements the intelligent control hierarchy [6], managing the states of the position sensor and the force sensor of the robot by Finite State Automaton. The industrial robot used in experiments was Mitsubishi RV-1A.…”

Section: Acquisition Of Attribute Parameters For the Assembly Taskmentioning

confidence: 99%

The design object model for robotic assembly of mechanical components

Nagai

Aramaki

Moshnyaga

2011

2011 IEEE International Conference on Industrial Technology

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Industrial robots have been recently introduced into the assembly line of consumer products. To assemble a product, goal coordinates must be specified for the robot. Because this task is quite difficult and time-consuming, we propose computing the values of goal coordinates from an object model of target design. In this paper, we define a model capable of managing information necessary for assembling parts by a robot. The effectiveness of the model is verified by using an experimental system.

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Representation and Programming for a Robotic Assembly Task Using an Assembly Structure

Cited by 11 publications

References 7 publications

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Classification, Modelling and Mapping of Skills in Automated Production Systems

The design object model for robotic assembly of mechanical components

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