Intuitive Constraint-Based Robot Programming for Robotic Assembly Tasks

Halt, Lorenz; Nägele, Frank; Tenbrock, Philipp; Pott, Andreas

doi:10.1109/icra.2018.8462882

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…The proposed system has a high potential for flexibility, but requires a complex framework to model the interactions between skills, controllers, and movement constraints. Halt et al [6] identify several primitive actions that can be composed into larger assembly tasks. Each primitive action has feature coordinates that are mapped to task space coordinates, which impose constraints on motion and how primitives may be combined.…”

Section: Previous Workmentioning

confidence: 99%

“…By breaking up tasks into common actions, we are able to reuse actions for multiple tasks. Halt et al [6] identify 6 primitive actions in the execution of assembly tasks. We have designed the actions for this system to be robust to small misalignment and displacement, so that single-purpose jigging can be avoided as much as possible.…”

Section: Task Modularizationmentioning

confidence: 99%

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Autonomous industrial assembly using force, torque, and RGB-D sensing

Watson

Miller²,

Correll

2020

Advanced Robotics

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We present algorithms and results for a robotic manipulation system that was designed to be easily programmable and adaptable to various tasks common to industrial setting, which is inspired by the Industrial Assembly Challenge at the 2018 World Robotics Summit in Tokyo. This challenge included assembly of standard, commercially available industrial parts into 2D and 3D assemblies. We demonstrate three tasks that can be classified into "peg-in-hole" and "hole-on-peg" tasks and identify two canonical algorithms: spiral-based search and tilting insertion. Both algorithms use hand-coded thresholds in the force and torque domains to detect critical points in the assembly. After briefly summarizing the state of the art in research, we describe the strategy and approach utilized by the tested system, how it's design bears on its performance, statistics on 20 experimental trials for each task, lessons learned during the development of the system, and open research challenges that still remain.

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Section: Previous Workmentioning

confidence: 99%

Section: Task Modularizationmentioning

confidence: 99%

Autonomous industrial assembly using force, torque, and RGB-D sensing

Watson

Miller²,

Correll

2020

Advanced Robotics

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“…In order to join an electrical component with the DIN-Rail (step 3), precise force-controlled and position-controlled robot movements are required. To simplify learning such movements, the Joining Function Block (JFB) uses already existing joining assembly skills such as in [45]. Conventionally, such skills require users to define several parameters, such as positions and the respective contact forces, in each step of execution.…”

Section: Joiningmentioning

confidence: 99%

Simulation-driven machine learning for robotics and automation

El-Shamouty

Kleeberger

Lämmle

et al. 2019

Tm - Technisches Messen

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Mass personalization—a megatrend in industrial manufacturing and production—requires fast adaptations of robotics and automation solutions to continually decreasing lot sizes. In this paper, the challenges of applying robot-based automation in a highly individualized production are highlighted. To face these challenges, a framework is proposed that combines latest machine learning (ML) techniques, like deep learning, with high-end physics simulation environments. ML is used for programming and parameterizing machines for a given production task with minimal human intervention. If the simulation environment realistically captures physical properties like forces or elasticity of the real world, it provides a high-quality data source for ML. In doing so, new tasks are mastered in simulation faster than in real-time, while at the same time existing tasks are executed. The functionality of the simulation-driven ML framework is demonstrated on an industrial use case.

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“…Although effective, these works tackle the assembly problem from a control perspective, rather than from a programming point of view and the integration within a skillbased programming framework remains an open problem only recently started to be addressed, e.g. in [41,42].…”

Section: Related Workmentioning

confidence: 99%