An Intelligent Assistive Robotic Manipulator

Lai

Asia-Pacific World Congress on Computer Science and Engineering

et al. 2014

Controlling a Robotic arm for applications such as object sorting with the use of vision sensors would need a robust image processing algorithm to recognize and detect the target object. This paper is directed towards the development of the image processing algorithm which is a pre-requisite for the full operation of a pick and place Robotic arm intended for object sorting task. For this type of task, first the objects are detected, and this is accomplished by feature extraction algorithm. Next, the extracted image (parameters in compliance with the classifier) is sent to the classifier to recognize what object it is and once this is finalized, the output would be the type of the object along with it's coordinates to be ready for the Robotic Arm to execute the pick and place task. The major challenge faced in developing this image processing algorithm was that upon making the test subjects in compliance with the classifier parameters, resizing of the images conceded in the loss of pixel data. Therefore, a centered image approach was taken. The accuracy of the classifier developed in this paper was 99.33% and for the feature extraction algorithm, the accuracy was 83.6443%. Finally, the overall system performance of the image processing algorithm developed after experimentation was 82.7162%.

Section: Introductionmentioning

confidence: 99%

Object detection and recognition for a pick and place Robot

Lai

Asia-Pacific World Congress on Computer Science and Engineering

et al. 2014

“…More recently, a new artificial intelligence robotics research lab was opened by NVIDIA where the main focus is to teach a robotic arm to navigate an IKEA kitchen [4] and recognize different utensils. Stereo vision systems were been used for identifying and grasping objects [5,6,7], where the robots aimed to accurately localize parts of the object from images and determine the correct grasping points.…”

Section: Introductionmentioning

confidence: 99%

3D Object Recognition Based on Point Clouds in Underwater Environment with Global Descriptors: A Survey

Himri

Ridao

Gracias

2019

Sensors

This paper addresses the problem of object recognition from colorless 3D point clouds in underwater environments. It presents a performance comparison of state-of-the-art global descriptors, which are readily available as open source code. The studied methods are intended to assist Autonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwater Inspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as being representative of IMR applications whose shape is typically known a priori. As such, CAD models were used to create virtual views of the objects under realistic conditions of added noise and varying resolution. Extensive experiments were conducted from both virtual scans and from real data collected with an AUV equipped with a fast laser sensor developed in our research centre. The underwater testing was conducted from a moving platform, which can create deformations in the perceived shape of the objects. These effects are considerably more difficult to correct than in above-water counterparts, and therefore may affect the performance of the descriptor. Among other conclusions, the testing we conducted illustrated the importance of matching the resolution of the database scans and test scans, as this significantly impacted the performance of all descriptors except one. This paper contributes to the state-of-the-art as being the first work on the comparison and performance evaluation of methods for underwater object recognition. It is also the first effort using comparison of methods for data acquired with a free floating underwater platform.

“…The end effector and its effectiveness are the most important part of the robotic arm used in industries and multi fingered robots [1] [2]. Mostly, the objects are placed in a fixed position from where the manipulator picks it up and places at the desired location [3]. However, there are applications where the arm has to find the object on its own.…”

Section: Introductionmentioning

confidence: 99%

Robotic Pick and Place using Two-Finger Touch Sensing

Kondaveti¹,

Venugopal²,

Rai³

2019

IJITEE

Pick and place robots are gaining popularity in the current era due to their ability to handle objects of varying sizes and dimensions. Research to improve the efficiency and design of these manipulators is currently the need of the hour. In this, a new and novel mechanism has been introduced; it is called Blind man Mechanism that is to find the object and to find shape of it. Shape Recognition algorithm to improve the hold on the object. It involves analysis of working of the human thumb and shape of the object. The Blind man mechanism allows object to be found, even when it is not in its usual position. It includes details about the thumb analysis to find an object, which further includes shape recognition of different objects in multiple scenarios also results for whether the object has been found or not. If found so verify for a pick place robot using MATLAB results