Unknown object grasping using statistical pressure models

Author(s):  
D. Perrin ◽  
O. Masoud ◽  
C.E. Smith ◽  
N.P. Papanikolopoulos
2018 ◽  
Vol 224 ◽  
pp. 01088 ◽  
Author(s):  
Yaroslav Kulkov ◽  
Arkady Zhiznyakov ◽  
Denis Privezentsev

The aim is an experimental research on the flat objects recognition using dimensionless marks of the contours of their binary images and determining the possibility of applying this method in computer vision systems of assembly robots. The main problem with the automation of assembly operations is the recognition of parts for the subsequent picking up of the robot arm. The basis for the formation of attribute vectors is the characteristics of the image contour. Recognition of a class of an unknown object consists in receipt of its contour, calculation of primary parameters and forming of a vector of dimensionless marks. Further mean square deviations of its vector of dimensionless marks from all reference are calculated. The minimum value of a deviation will specify probable belonging to the corresponding class.


2007 ◽  
Vol 5 ◽  
pp. 153-156 ◽  
Author(s):  
S. Schelkshorn ◽  
J. Detlefsen

Abstract. An increasing number of modern applications and services is based on the knowledge of the users actual position. Depending on the application a rough position estimate is sufficient, e. g. services in cellular networks that use the information about the users actual cell. Other applications, e. g. navigation systems use the GPS-System for accurate position finding. Beyond these outdoor applications a growing number of indoor applications requires position information. The previously mentioned methods for position finding (mobile cell, GPS) are not usable for these indoor applications. Within this paper we will present a system that relies on the simultaneous measurement of doppler signals at four different positions to obtain position and velocity of an unknown object. It is therefore suiteable for indoor usage, extendig already existing wireless infrastructure.


2019 ◽  
pp. 1372-1387
Author(s):  
Hiroyuki Masuta ◽  
Tatsuo Motoyoshi ◽  
Kei Sawai ◽  
Ken'ichi Koyanagi ◽  
Toru Oshima ◽  
...  

This paper discusses the direct perception of an unknown object and the action decision to grasp an unknown object using depth sensor for social robots. Conventional methods estimate the accurate physical parameters when a robot wants to grasp an unknown object. Therefore, we propose a perceptual system based on an invariant concept in ecological psychology, which perceives the information relevant to the action of the robot. Firstly, we proposed the plane detection based approach for perceiving an unknown object. In this paper, we propose the sensation of grasping which is expressed by using inertia tensor, and applied with fuzzy inference using the relation between principle moment of inertia. The sensation of grasping encourages the decision for the grasping action directly without inferring from physical value such as size, posture and shape. As experimental results, we show that the sensation of grasping expresses the relative position and posture between the robot and the object, and the embodiment of the robot arm by one parameter. And, we verify the validity of the action decision from the sensation of grasping.


Author(s):  
Hiroyuki Masuta ◽  
Tatsuo Motoyoshi ◽  
Ken’ichi Koyanagi ◽  
Toru Oshima ◽  
Hun-ok Lim

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