Model-based vision for robotic manipulation of twisted tubular parts: using affine transforms and heuristic search

Wang, S.; Cromwell, R. L.; Kak, Avinash C.; Kimura, Ichirô; Osada, Masakazu

doi:10.1109/robot.1994.350986

Cited by 17 publications

(4 citation statements)

References 17 publications

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“…There are three main approaches: 1) heuristic-based, 2) sampling-based, and 3) data-driven. 1) Heuristic-based approaches such as finding the highest grasp points [24] are simple but powerful if the appropriate one is selected for the task. 2) Sampling-based approaches evaluate grasp candidates for collision by using analytical methods [25]- [29] or by using task-and-motion planning libraries like OpenRAVE for collision checking [30]- [35].…”

Section: Collision Avoidance In Graspingmentioning

confidence: 99%

Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Ummadisingu¹,

Takahashi²,

Fukaya³

2022

Preprint

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The food packaging industry handles an immense variety of food products with wide-ranging shapes and sizes, even within one kind of food. Menus are also diverse and change frequently, making automation of pick-and-place difficult. A popular approach to bin-picking is to first identify each piece of food in the tray by using an instance segmentation method. However, human annotations to train these methods are unreliable and error-prone since foods are packed close together with unclear boundaries and visual similarity making separation of pieces difficult. To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models. Another concern is that foods are easily damaged during grasping. We address this by introducing two additional methods-a novel adaptive finger mechanism to passively retract when a collision occurs, and a method to filter grasps that are likely to cause damage to neighbouring pieces of food during a grasp. We demonstrate the effectiveness of the proposed method on several kinds of real foods.

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Section: Collision Avoidance In Graspingmentioning

confidence: 99%

Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Ummadisingu¹,

Takahashi²,

Fukaya³

2022

Preprint

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“…Bolles and Horaud [2] use range data to group the surface features which is then used to generate and verify the hypotheses of object location. Wang et al [22] use 3D range data to compute shape of flexible industrial parts such as cables. However in the presence of specularities, range sensors fail to produce accurate depth maps and they are comparably more expensive than camera-based solutions.…”

Section: A Prior Workmentioning

confidence: 99%

Finding a needle in a specular haystack

Shroff

Taguchi

Tuzel

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Shroff, N.; Taguchi, R.; Tuzel, O.; Veeraraghavan, A.; Ramalingam, S.; Okuda, H. TR2011-082 May 2011Abstract Progress in machine vision algorithms has led to widespread adoption of these techniques to automate several industrial assembly tasks. Nevertheless, shiny or specular objects which are common in industrial environments still present a great challenge for vision systems. In this paper, we take a step towards this problem under the context of vision-aided robotic assembly. We show that when the illumination source moves, the specular highlights remain in a region whose radius is inversely proportional to the surface curvature. This allows us to extract regions of the object that have high surface curvature. These points of high curvature can be used as features for specular objects. Further, an inexpensive multi-flash camera (MFC) design can be used to reliably extract these features. We show that one can use multiple views of the object using the MFC in order to triangulate and obtain the 3D location and pose of the shiny objects. Finally, we show a system consisting of a robot arm with an MFC that can perform automated detection and pose estimation of shiny screws within a cluttered bin, achieving position and orientation errors less than 0.5 mm and 0.8 respectively. IEEE International Conference on Robotics and Automation (ICRA)This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Abstract-Progress in machine vision algorithms has led to widespread adoption of these techniques to automate several industrial assembly tasks. Nevertheless, shiny or specular objects which are common in industrial environments still present a great challenge for vision systems. In this paper, we take a step towards this problem under the context of vision-aided robotic assembly. We show that when the illumination source moves, the specular highlights remain in a region whose radius is inversely proportional to the surface curvature. This allows us to extract regions of the object that have high surface curvature. These points of high curvature can be used as features for specular objects. Further, an inexpensive multi-flash camera (MFC) design can be used to reliably extract these features. We show that one can use multiple views of the object using the MFC in order to triangulate and obtain the 3D location and pose of the shiny objects. Finally, we show a system consisting of a robot arm with an MFC that can...

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“…Several papers have been published on bin picking algorithms, of which very few have considered occluded objects. Most researches on bin picking use vision only for object recognition and pose determination (Krisnawan Rahardja & Akio Kosaka, 1996), (Ayako Takenouchi & et al, 1998), (Ezzet Al-Hujazi & Arun Sood, 1990), (Harry Wechsler & George Lee Zimmerman, 1989), (Kohtaro Ohba & Katsushi Ikeuchi,1996), while others use a model based approach which compares the object image with a model database for pose determination (Yoshikatsu Kimura & et al, 1995), (Martin Berger & et al, 2000), (Sarah Wang & et al, 1994). Some other approaches use a combination of sensors and model database to solve the bin picking problem (Martin Berger & et al, 2000) who use stereo and CAD models to determine pose of objects.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Location Computation of Bin Objects

Hema

Paulraj

Nagarajan

et al. 2007

International Journal of Advanced Robotic Systems

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In this paper we present a stereo vision based system for segmentation and location computation of partially occluded objects in bin picking environments. Algorithms to segment partially occluded objects and to find the object location [midpoint,x, y and z co-ordinates] with respect to the bin area are proposed. The z co-ordinate is computed using stereo images and neural networks. The proposed algorithms is tested using two neural network architectures namely the Radial Basis Function nets and Simple Feedforward nets. The training results fo feedforward nets are found to be more suitable for the current application. The proposed stereo vision system is interfaced with an Adept SCARA Robot to perform bin picking operations. The vision system is found to be effective for partially occluded objects, in the absence of albedo effects. The results are validated through real time bin picking experiments on the Adept Robot.

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Model-based vision for robotic manipulation of twisted tubular parts: using affine transforms and heuristic search

Cited by 17 publications

References 17 publications

Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection

Finding a needle in a specular haystack

Segmentation and Location Computation of Bin Objects

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