Feature Based, Collision Free Inspection Path Planning

Sampling-based path planning for robust feature-based visual servoing

2009 Canadian Conference on Electrical and Computer Engineering ◽

10.1109/ccece.2009.5090243 ◽

2009 ◽

Cited By ~ 1

Author(s):

Farid Arvani ◽

George K. I. Mann ◽

Andrew Fisher ◽

Raymond G. Gosine

Keyword(s):

Path Planning ◽

Visual Servoing ◽

Feature Based

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Autonomous multi-vehicle contact reacquisition using feature-based navigation and in-situ adaptive path planning for AUVs

OCEANS 2019 - Marseille ◽

10.1109/oceanse.2019.8867234 ◽

2019 ◽

Author(s):

Jeroen van de Sande ◽

Martijn van Riet ◽

Arno Duijster ◽

Robbert van Vossen

Keyword(s):

Path Planning ◽

Feature Based

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A Feature-Based Underwater Path Planning Approach using Multiple Perspective Prior Maps

2020 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra40945.2020.9196680 ◽

2020 ◽

Author(s):

Daniel Cagara ◽

Matthew Dunbabin ◽

Paul Rigby

Keyword(s):

Path Planning ◽

Multiple Perspective ◽

Planning Approach ◽

Feature Based

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Energy Management of Planetary Rovers Using a Fast Feature-Based Path Planning and Hardware-in-the-Loop Experiments

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2013.2244624 ◽

2013 ◽

Vol 62 (6) ◽

pp. 2389-2401 ◽

Cited By ~ 10

Author(s):

Saber Fallah ◽

Bonnie Yue ◽

Orang Vahid-Araghi ◽

Amir Khajepour

Keyword(s):

Path Planning ◽

Energy Management ◽

Hardware In The Loop ◽

Planetary Rovers ◽

Feature Based

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A Feature-Based Approach to Path Planning for in-Process Probing Operations in STEP-Compliant NC Manufacture

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.422.326 ◽

2011 ◽

Vol 422 ◽

pp. 326-330

Author(s):

Ri Liang Liu ◽

Xi Hui Liang ◽

Cheng Rui Zhang

Keyword(s):

Quality Control ◽

Path Planning ◽

Research And Development ◽

Prototype System ◽

Development Trends ◽

Seamless Integration ◽

Process Measurement ◽

Control Processes ◽

Part Program ◽

Feature Based

With STEP-NC providing a way for seamless integration of the design, manufacturing, and quality control processes, the implementation of in-process measurement in this context has become a necessity for STEP-compliant NC manufacture. In the light of current research and development trends in this field, this paper proposes an approach to on-machine planning for in-process measurement, focusing on touch probing operations. Issues about the measured elements and the probing path planning are mainly addressed, and a prototype system is designed and implemented, which generates probing paths based on the information contained in the STEP-NC part program. The feasibility of the proposed approach is demonstrated with an example STEP-NC file.

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Feature-based five-axis path planning method for robotic additive manufacturing

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405417752508 ◽

2018 ◽

Vol 233 (5) ◽

pp. 1412-1424 ◽

Cited By ~ 6

Author(s):

Gang Zhao ◽

Guocai Ma ◽

Wenlei Xiao ◽

Yu Tian

Keyword(s):

Additive Manufacturing ◽

Path Planning ◽

Planning Method ◽

Manufacturing Strategy ◽

Feature Extraction Method ◽

Manufacturing Method ◽

Feature Based ◽

Five Axis ◽

Subtractive Machining ◽

Manufacturing Features

Additive manufacturing has been developed for decades and attracts significant research interests in recent years. Usually, the stereolithography tessellation language format is employed in additive manufacturing to represent the geometric data. However, people gradually realize the inevitable drawbacks of the stereolithography tessellation language file format, such as redundancy, inaccuracy, missing of feature definitions, and lack of integrity. In addition, it is almost impossible to apply the simple polygonal facet representation to the five-axis manufacturing strategy. Hence, there are quite few researches and applications on the five-axis additive manufacturing, in spite of its common applications in the subtractive machining. This article proposes a feature-based five-axis additive manufacturing methodology to enhance and extend the additive manufacturing method. The additive manufacturing features are defined and categorized into two5D_AM_feature and freeform_AM_feature. A feature extraction method is proposed that can automatically recognize the additive manufacturing features from the input model. Specially for the freeform_AM_feature, a five-axis path planning method is proposed and split into three stages: (1) offset the reference surface, (2) spatially slice the freeform layers, and (3) generate the toolpaths for each freeform layer. Real additive manufacturing five-axis toolpaths can be obtained using the proposed algorithm that performs as a secondary developed plug-in in the CATIA® environment. A robotic additive manufacturing system is constructed for the implementation of the five-axis additive manufacturing tasks, which are generated by the proposed algorithms and post-processed after simulation and off-line programming. Some examples are printed to validate the feasibility and efficiency of the proposed method.

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