Multiple Sensor Integrated Robotic End-effectors for Assembly

Sahu, Omprakash; Biswal, Bibhuti Bhusan; Mukherjee, Saptarshi; Jha, Panchanand

doi:10.1016/j.protcy.2014.08.014

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…Ying and Pollard [8] described an approach that handles the specific challenges of applying shape matching to grasp synthesis. By processing images at various resolutions and object detection systems work using multiple sensors operation is addressed by Sahu et al [9]. Osada et al [10] proposed Algorithms which are used for Internet and An Integrated Approach of Sensors to Detect Grasping Point for Unstructured 3-D Parts…”

Section: Related Work and Reviewmentioning

confidence: 99%

An Integrated Approach of Sensors to Detect Grasping Point for Unstructured 3-D Parts

Sahu¹

2017

IJET

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Grasping of unstructured parts is a problem in automated assembly. In this paper, grasping of unstructured manufacturing parts is considered, where the parts are unambiguously seen first time via vision sensor. The problem lies on grasping these unknown parts, where the three dimensional view of parts is not available. For grasping efficiently, the end-effector is equipped with a vision and ultrasonic sensor. The demonstration is explained in details in the paper. The desired grasping point is found out by the bounding rectangle and image frame size contour to calculate the total number of the pixel on parts. Acceptable parts grasping point can be calculated in the presence of HSV noise. Experiments analysis is done on different unknown objects for evaluation.

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Section: Related Work and Reviewmentioning

confidence: 99%

An Integrated Approach of Sensors to Detect Grasping Point for Unstructured 3-D Parts

Sahu¹

2017

IJET

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“…The initial installation cost of RFA is slightly high when compared with conventional fixture setup. But finally, this RFA reduces around 20% of the total fixturing cost, 2 improves autonomy, 7 improves reconfigurability, 8 and ultimately increases the throughput. 9,10 The workspace of the robot arm can be increased further by mounting it on the mobile platform.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Veeramani

Muthuswamy

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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SwarmItFIX (self-reconfigurable intelligent swarm fixtures) is a multi-agent setup mainly used as a robotic fixture for large Sheet metal machining operations. A Constraint Satisfaction Problem (CSP) based planning model is utilized currently for computing the locomotion sequence of multiple agents of the SwarmItFIX. But the SwarmItFIX faces several challenges with the current planner as it fails on several occasions. Moreover, the current planner computes only the goal positions of the base agent, not the path. To overcome these issues, a novel hierarchical planner is proposed, which employs Monte Carlo and SARSA TD based model-free Reinforcement Learning (RL) algorithms for the computation of locomotion sequences of head and base agents, respectively. These methods hold two distinct features when compared with the existing methods (i) the transition model is not required for obtaining the locomotion sequence of the computational agent, and (ii) the state-space of the computational agent become scalable. The obtained results show that the proposed planner is capable of delivering optimal makespan for effective fixturing during the sheet metal milling process.

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“…Additionally, this approach allows for each part to be inspected after each critical manufacturing step, detecting defects early and ensuring constant quality. Typical approaches include machine integrated sensors [1][2][3] and robot-arm-based inspection using camera vision, laser scanning or 3D pattern projection [4][5][6][7][8][9][10] (also see industrial systems such as: X4 i-Robot, Creaform Cube-R, GOM Atos Scanbox, Mertolog X4), enhanced by multi-sensor and data fusion approaches [11][12][13][14][15][16][17][18]. Allowing for fast and flexible inspection of free-form surfaces, these systems commonly lack the ability to measure sub-millimeter features with high resolution, as the sensors required for this step are typically large, heavy and require laboratory conditions and are therefore used off-line.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of a Robot-Mounted Interferometer for an Industrial Environment

Bíró

Kinnell

2020

Sensors

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High value manufacturing requires production-integrated, fast, multi-sensor and multi-scale inspection. To meet this need, the robotic deployment of sensors within the factory environment is becoming increasingly popular. For microscale measurement applications, robot-mountable versions of high-resolution instruments, that are traditionally deployed in a laboratory environment, are now becoming available. However, standard methodologies for the evaluation of these instruments, particularly when mounted to a robot, have yet to be fully defined, and therefore, there is limited independent evaluation data to describe the potential performance of these systems. In this paper, a detailed evaluation approach is presented for light-weight robot mountable scanning interferometric sensors. Traditional evaluation approaches are considered and extended to account for robotic sensor deployment within industrial environments. The applicability and value of proposed evaluation is demonstrated through the comprehensive characterization of a Heliotis H6 interferometric sensors. The results indicate the performance of the sensor, in comparison to a traditional laboratory-based system, and demonstrate the limits of the sensor capability. Based-on the evaluation an effective strategy for robotic deployment of the sensor is demonstrated.

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Multiple Sensor Integrated Robotic End-effectors for Assembly

Cited by 9 publications

References 5 publications

An Integrated Approach of Sensors to Detect Grasping Point for Unstructured 3-D Parts

An Integrated Approach of Sensors to Detect Grasping Point for Unstructured 3-D Parts

Reinforcement learning based path planning of multiple agents of SwarmItFIX robot for fixturing operation in sheetmetal milling process

Performance Evaluation of a Robot-Mounted Interferometer for an Industrial Environment

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