Skill based robot programming: Assembly, vision and Workspace Monitoring skill interaction

Herrero, Héctor; Moughlbay, Amine Abou; Outón, Jose Luis; Sallé, Damien; Ipiña, Karmele López de

doi:10.1016/j.neucom.2016.09.133

Cited by 34 publications

(18 citation statements)

References 15 publications

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“…On the one hand, the flexibility on this approach is demonstrated by the fact that the same skills can be used to perform different processes although they suffer certain variations, e.g., variations in the rivet models, variations in the drilled holes number or positions, etc. This assertion is supported by the work that the authors have made in different applications [51][52][53][54]: another deburring process was performed using very similar skills, antenna assembling skill was presented, workspace monitoring and vision operations for hole detection and 3D CAD Matching were integrated as skills, and finally the interaction between the skills and the state machine was presented. On the other hand, new applications can be generated graphically (Section 2.2.3), reducing the requiring expertise and increasing the ease of use.…”

Section: Discussionmentioning

confidence: 80%

Enhanced Flexibility and Reusability Through State Machine Based Architectures for Multisensor Intelligent Robotics

Herrero¹,

Outón²,

Puerto³

et al. 2017

Preprint

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This paper presents a state machine based architecture which enhances flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating presented approach versus traditional robot programming techniques.

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Section: Discussionmentioning

confidence: 80%

Enhanced Flexibility and Reusability Through State Machine Based Architectures for Multisensor Intelligent Robotics

Herrero¹,

Outón²,

Puerto³

et al. 2017

Preprint

Self Cite

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“…The LfD approach [ 76 , 78 ] could transfer the manipulation skill from human to robot, which enables a robot to manipulate objects with more flexibility. Skill-based programming [ 80 , 81 , 82 , 83 , 84 ] eases the robot program generation and enables the robot to complete varied object manipulation, such as grasping, picking, and assembly. This approach requires mastering in advance what the robot can accomplish.…”

Section: Discussionmentioning

confidence: 99%

“…These skills are acquired by machine learning and can be reused for different furniture sets. Herrero et al [ 84 ] combined skill-based programming with a state machine and improved the skills of interaction and communication with humans. The skill-based programming approach eases the robot program generation and is helpful for the robot to grasp the object and complete the task, particularly in a human–robot collaborative workspace.…”

Section: Physical-uncertain Objectsmentioning

confidence: 99%

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Wang

Zhang

Zang

et al. 2020

Sensors

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As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.

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“…Skill-based programming has been used in this robotic solution [52]. Each skill represents a singular operation or task (detect one part, move arm to a pose, grasp, etc.)…”

Section: Skill-based Programmingmentioning

confidence: 99%

Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes

Outón

Villaverde

Herrero

et al. 2019

Sensors

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There is a paradigm shift in current manufacturing needs that is causing a change from the current mass-production-based approach to a mass customization approach where production volumes are smaller and more variable. Current processes are very adapted to the previous paradigm and lack the required flexibility to adapt to the new production needs. To solve this problem, an innovative industrial mobile manipulator is presented. The robot is equipped with a variety of sensors that allow it to perceive its surroundings and perform complex tasks in dynamic environments. Following the current needs of the industry, the robot is capable of autonomous navigation, safely avoiding obstacles. It is flexible enough to be able to perform a wide variety of tasks, being the change between tasks done easily thanks to skills-based programming and the ability to change tools autonomously. In addition, its security systems allow it to share the workspace with human operators. This prototype has been developed as part of THOMAS European project, and it has been tested and demonstrated in real-world manufacturing use cases.

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Skill based robot programming: Assembly, vision and Workspace Monitoring skill interaction

Cited by 34 publications

References 15 publications

Enhanced Flexibility and Reusability Through State Machine Based Architectures for Multisensor Intelligent Robotics

Enhanced Flexibility and Reusability Through State Machine Based Architectures for Multisensor Intelligent Robotics

Feature Sensing and Robotic Grasping of Objects with Uncertain Information: A Review

Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes

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