Abstract
Industrial robots are widely used for repetitive, humanly unmanageable, and hazardous tasks. Hence, an improvement in the production efficiency of industrial robot manipulators is of prime concern. This can be achieved through machine vision and path planning techniques with a focus on localization and shortest path calculation. In particular, this is important for manufacturing and bottle filling industries which extensively use robotic manipulators to place/displace bottles during production and post refill placements. This is even more challenging when soft, fragile, or opaque objects have to be detected, since it is significantly difficult for robot vision to focus on their indistinguishable features. To this end, we present an ensemble robot framework with a stereo vision system for tracking colored objects which are sensed using blob analysis. An ensemble robotic framework with neural networks is proposed for predicting and thereby overcoming the inbuilt geometric error present in stereo vision systems. Moreover, we have simplified 2-D correspondence problem to 1-D by using a non-rectified stereo camera model and object tracking by applying the triangulation technique in 3D stereo vision coordinate system (SVCS). Subsequently, the SVCS is transformed into robot stereo vision coordinate system for tracking the object centroid by using an RGB marker placed on the object. Finally, in the learning model we have combined color region tracking with machine learning to achieve high accuracy. The outcomes are in accordance with the designed model and successfully achieve path prediction with up to 91.8% accuracy.
Highlights
Industrial robots and robotic manipulators are getting more sophisticated, highly diversified, and designed to achieve a high level of autonomy
We set up a precise experiment in the laboratory with 6-degree of freedom (DOF) industrial manipulators equipped with robot stereo vision (RSV) to perform a tracking task for soft objects having red, green, and blue (RGB) color marker in an open environment
Since the transformation data of robot is high dimensional and non linear, we propose to use non-linear principle component analysis with a neural network and call this NLPCANN
Summary
Industrial robots and robotic manipulators are getting more sophisticated, highly diversified, and designed to achieve a high level of autonomy. This is in comparison to the earlier concepts where robots were considered as mechanical manipulators [1]. To make robots flexible, automated object detection has benefited towards a more generalized and adaptive behavior for different objects and positions [3]. This has increased the utility of robotics in. Neural networks and deep learning-based methods are increasing in popularity [6], but care must be taken in terms of explainability and generalization of such models
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