Abstract
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.
Highlights
Bin Picking Robot requires information of the object to be picked and its exact location with respect to the bin area
Two neural nets namely radial basis function nets and feedforward nets are compared in computing the z co‐ordinate.The object features of the stereo images are used as the input data and the distance of the object from gripper is used as the output data to train the neural network
To compute the ‘z’ co‐ordinate, the singular value features are extracted from the added edge image using SVD. 10 singular values of an image are fed as input and the object distance is fed as output to the neural networks
Summary
Bin Picking Robot requires information of the object to be picked and its exact location with respect to the bin area. In this paper emphasis is given to solving the segmentation problem of occluded objects in the bin. The stereo vision system proposed considers two aspects, one is the segmentation of the bin image to identify the topmost object and the other is the location of the object midpoint (x, y and z co‐ordinates). 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
