Easy robot programming for industrial manipulators by manual volume sweeping

Maeda, Yusuke; Ushioda, Tatsuya; Makita, Satoshi

doi:10.1109/robot.2008.4543546

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…Contrary to reducing human intervention, recent research on manual programming instead focuses on making online robot programming easier and faster. 3,[7][8][9][10][11] This decreases the need for high-wage engineering hours and allows the shop floor operators to channel valuable process knowledge and experience into the instruction.…”

Section: Related Researchmentioning

confidence: 99%

Using collaborative robots as a tool for easier programming of industrial robots

Sarivan¹,

Batuev

Ciontos³

et al. 2018

Robotics Transforming the Future

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Programming industrial robots using traditional jogging via teach pendant is a time-consuming task that requires extensive training. More intuitive and faster task programming is often possible using kinesthetic teaching. Although this feature is available on many commercial collaborative robots, it is rarely available on traditional industrial robots. In this paper we propose a framework for allowing tasks to be instructed using a collaborative robot via kinesthetic teaching, and afterwards deployed to a traditional industrial robot. The framework consists of a physical modular concept for robot exchange, and a online programming software tool called Universal Industrial Interface. To assess the framework, a feasibility study is conducted where an industrial relevant task is first programmed using a collaborative manipulator, and afterwards deployed on an industrial manipulator.

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Section: Related Researchmentioning

confidence: 99%

Using collaborative robots as a tool for easier programming of industrial robots

Sarivan¹,

Batuev

Ciontos³

et al. 2018

Robotics Transforming the Future

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“…Features from the curvature and velocity need to be ordered to select a proper feature set to describe the morphologically significant points in the shape profile. Feature points from the curvature and velocity are merged based on the orthogonal distance from the trajectory point to the relevant line segment of the polyline as in (5). For all feature of the curvature and velocity, the feature set F and its errors with respect to the original shape trajectory are decided through Algorithm 1 and plotted in Fig.9.…”

Section: Morphological Feature Pointsmentioning

confidence: 99%

“…The conventional method to teach the manipulator was with a complex computer and teaching-pendant [1,2]. Recently, some researchers proposed novel robot teaching methods [3][4][5][6][7] including the direct teaching method. Direct teaching (DT) is one of the intuitive teaching methods.…”

Section: Introductionmentioning

confidence: 99%

Post-processing of Direct Teaching Trajectory in Industrial Robots

Choi¹,

Park²,

Do³

et al. 2012

International Journal of Fuzzy Logic and Intelligent Systems

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Direct teaching of the industrial robot is a novel technique to easily teach manipulators. However, teaching data by human hand cannot help having large noise errors ranging from low to high frequency. To use teaching data, post-processing to correct the teaching trajectory is required. Here, a novel shape-based trajectory correction method to rebuild teaching data with the feature information of curvature and velocity is proposed. The proposed method is tested on square and circular objects.

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“…A volume sweeping approach with an octree representation was proposed in [11] to allow an operator to manually map out the collision-free space. Unfortunately, navigating around obstacles in this non-convex space is still difficult for visual servoing controllers.…”

Section: Introductionmentioning

confidence: 99%

Constrained manipulator visual servoing (CMVS): Rapid robot programming in cluttered workspaces

Chan¹,

Croft²,

Little³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a model-free optimization framework for the visual servoing of eye-in-hand manipulators in cluttered environments. Visual feedback is used to solve for a set of feasible trajectories that bring the robot end-effector to a target object at a previously untaught location under a number of challenging constraints (i.e., whole-arm collisions, object occlusions, robot's joint limits, camera's sensing limits). A novel controller is proposed, which exploits the natural by-products of the teach-by-showing process, to help the robot navigate this non-convex space. Examining the user-demonstrated trajectories that lead up to the reference image, we use a combination of stochastic optimization techniques and classical optimization techniques to extract the relevant cost functions and constraints for servoing. We hypothesize that we can leverage the user's sensory capabilities and knowledge of the workspace to alleviate the burden of modeling system constraints explicitly. We verify this hypothesis via realistic experiments on a Barrett WAM 7-DOF manipulator equipped with a Sony XC-HR70 camera to show the comparative efficacy of this approach.

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Easy robot programming for industrial manipulators by manual volume sweeping

Cited by 19 publications

References 10 publications

Using collaborative robots as a tool for easier programming of industrial robots

Using collaborative robots as a tool for easier programming of industrial robots

Post-processing of Direct Teaching Trajectory in Industrial Robots

Constrained manipulator visual servoing (CMVS): Rapid robot programming in cluttered workspaces

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