Hough-space-based object recognition tightly coupled with path planning for robust and fast bin-picking

Abstract

The proposed bin-picking method combines object recognition with path planning. To avoid conflicts between the assumptions of the elemental techniques needed for bin-picking, object recognition i s combined with path planning by using environmental information. To achieve this combination, a Hough transform, which records the model-to-image matches in a Hough space, is used to estimate the pose. The matches represent the arrangement of the objects, so they can be regaded as environmental information for path planning. To reduce the number of recognition errors and the object-detection time, a pair of object features that reduces the number of invalid votes in the Hough space is used for the Hough transform. Simulated path planning showed that using a Hough space to represent the environmental information improves the ability to plan a safe path for the manipulator. Simulated object recognition showed that using a pair of features makes the process faster and reduces the number of invalid votes. The pose estimation and safe path planning ability were confirmed by an ezperiment on casting objects using a range finder and a robot.

Automated 3D vision guided bin picking process for randomly located industrial parts

“…Most of the work is focused on image processing to obtain the part location, since this is the first step for bin picking [4] [5] [6] [7]. In this paper, we present a robust, 3D random bin picking system that includes a vision system capable of identifying the location of a part, and a robot 2 system that validates parts and controls the robot to pick up a part and place it in the right location.…”

Segmentation and Location Computation of Bin Objects

“…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.…”

Stereo Vision System for A Bin Picking Adept Robot

“…In recent years, much work have been reported on recognition of objects for the bin picking problem. Some concepts are limited to recognition of the objects while others concentrate on picking an object and analyzing the object for pose determination and recognition using database of the object images [1], [2], [3]. Stereo based approaches have been reported by some researchers; Rahardja and Kosaka [4] have developed an effective stereo based algorithm to find simple visual clues of complex objects, however the system requires human intervention for grasping tasks.…”