Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes

Outón, Jose Luis; Villaverde, Iván; Herrero, Héctor; Esnaola, Urko; Sierra, Basilio

doi:10.3390/s19245414

Cited by 27 publications

(13 citation statements)

References 32 publications

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“…The robotic system used in this paper is shown in Figure 1. It is an innovative robot designed by the authors and presented in [15], where it was described extensively. As this system meets all the criteria established in [16], it can be regarded as an AIMM.…”

Section: Innovative Aimmmentioning

confidence: 99%

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A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

et al. 2021

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In modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment.

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Section: Innovative Aimmmentioning

confidence: 99%

“…A more detailed description of these systems and their implementation can be found in the previously mentioned report [15].…”

Section: Global Autonomous Navigationmentioning

confidence: 99%

A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

et al. 2021

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“…Furthermore, the degrees of freedom (DOFs) of the mobile robot typically provide the mobile manipulator with redundancy with respect to the tasks such as end-effector trajectory tracking. By utilizing these properties, a mobile manipulator can perform complex and diverse tasks such as painting [ 1 ], production [ 2 ], and manufacturing [ 3 ]. However, to perform these complex tasks in dynamic and unstructured environments, one of the most critical capabilities for a mobile manipulator is to detect self-collision by using proprioceptive or exteroceptive sensors and reactively generate sensor-based avoidance motion.…”

Section: Introductionmentioning

confidence: 99%

Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Jang

Kim

Park

2021

Sensors

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This paper introduces a reactive self-collision avoidance algorithm for differentially driven mobile manipulators. The proposed method mainly focuses on self-collision between a manipulator and the mobile robot. We introduce the concept of a distance buffer border (DBB), which is a 3D curved surface enclosing a buffer region of the mobile robot. The region has the thickness equal to buffer distance. When the distance between the manipulator and mobile robot is less than the buffer distance, which means the manipulator lies inside the buffer region of the mobile robot, the proposed strategy is to move the mobile robot away from the manipulator in order for the manipulator to be placed outside the border of the region, the DBB. The strategy is achieved by exerting force on the mobile robot. Therefore, the manipulator can avoid self-collision with the mobile robot without modifying the predefined motion of the manipulator in a world Cartesian coordinate frame. In particular, the direction of the force is determined by considering the non-holonomic constraint of the differentially driven mobile robot. Additionally, the reachability of the manipulator is considered to arrive at a configuration in which the manipulator can be more maneuverable. In this respect, the proposed algorithm has a distinct advantage over existing avoidance methods that do not consider the non-holonomic constraint of the mobile robot and push links away from each other without considering the workspace. To realize the desired force and resulting torque, an avoidance task is constructed by converting them into the accelerations of the mobile robot. The avoidance task is smoothly inserted with a top priority into the controller based on hierarchical quadratic programming. The proposed algorithm was implemented on a differentially driven mobile robot with a 7-DOFs robotic arm and its performance was demonstrated in various experimental scenarios.

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“…Due to the rapid development and wide implementation of advanced technologies, we are undergoing a revolution toward smart manufacturing, named Industry 4.0. Quick response to consumers' fast-shifting and individualized needs, is regarded as one of the core competitiveness for the success of the enterprises in many industry sectors [1][2][3]. In this context, as a hybrid production mode of made-to-measure and mass production, leveraging the personalized services and high productivity of the two modes, mass customization has been increasingly applied in the apparel industry recently [4,5].…”

Section: Introductionmentioning

confidence: 99%

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

Wang¹,

Zeng²,

Tao³

et al. 2021

IJCIS

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Garment pattern design is an extremely significant factor for the success of fashion company in mass customization and industry 4.0. In this paper, we proposed a new approach for constructing a garment pattern design knowledge base (GPDKB) using sensory analysis, ontology and support vector regression (SVR) modeling, aiming at systematically formalizing the complete knowledge on garment pattern design and realizing garment pattern associated adaptation. This approach has been described and validated in the scenario of personalized men's shirt design. The GPDKB consists of three components: conceptual knowledge base, relationship knowledge base and adaptation rules knowledge base. After selecting the optimal garment patterns using data twins-driven technique, the GPDKB has been built by learning from quantitative relationships between garment structure lines, controlling points and garment patterns and then simulated for pattern parameters prediction and pattern associate adaptation. Finally, the performance of the presented approach was compared with other classical data learning techniques, i.e., multiple linear regression and backpropagation-artificial neural network. The experimental results show that SVR-based approach outperform another two techniques with the lowest average of mean squared errors (0.1279) and average of standard deviation (0.1651). And the adaptation effect of GPDKB is equivalent to existing grading method. The general principle of the proposed approach can be adapted to creation of design knowledge bases for other type garments such as compression leggings. In fashion industry, the proposed GPDKB can effectively support designers by rapidly, accurately and automatically predicting relevant pattern adaptation parameters during garment pattern design.

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Innovative Mobile Manipulator Solution for Modern Flexible Manufacturing Processes

Cited by 27 publications

References 32 publications

A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context

Reactive Self-Collision Avoidance for a Differentially Driven Mobile Manipulator

Construction of Garment Pattern Design Knowledge Base Using Sensory Analysis, Ontology and Support Vector Regression Modeling

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