Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.0
2.1
Journals
Machines
Special Issues
Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping...
share announcement

Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping Concepts

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 3470

Special Issue Editors

Dr. Iñaki Maurtua

E-Mail Website
Guest Editor
Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain
Interests: safety; human factors; perception; manufacturing; human robot; interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Ander Ansuategui

E-Mail Website
Guest Editor
Smart and Autonomous System Unit, Tekniker, Member of Basque Research & Technology Alliance, 20600 Eibar, Spain
Interests: robot manipulation; navigation; software architecture; AI; safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Manipulation is a key ability for both industrial and service robots. Traditionally, handling tasks have been defined by robot operators who use their own criteria and knowledge to define the best way to pick up a product, taking into account the characteristics of the part, the environment, the available tool and the subsequent operations to be performed with the part.

With the advancement of AI, it will be possible to create solutions where the robot is able to decide the best way to pick up a product and control its handling in terms of picking, assembly and release tasks. In addition, new materials and gripping concepts based on unconventional physical or kinematic principles will lead to tools that are capable of handling many different parts, including those that require careful handling.

This Special Issue aims to collaborate with researchers to present recent advances and technologies in the field of robotic manipulation. The topics of interest include, but are not limited to:

  • AI technologies and applications for perception, manipulation and assembly;
  • Innovative grasping concepts, including soft grippers;
  • Learning from demonstrations;
  • Control architectures;
  • Flexible task planning and control;
  • Human factors in manipulation applications.

Dr. Iñaki Maurtua
Dr. Ander Ansuategui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • grasping
  • assembly
  • learning
  • perception
  • manipulation
  • gripper
  • control
  • 3D nesting for packaging

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 3903 KiB  
Article
Pick and Place Control of a 3-DOF Robot Manipulator Based on Image and Pattern Recognition
by Samuel Kariuki, Eric Wanjau, Ian Muchiri, Joseph Muguro, Waweru Njeri and Minoru Sasaki
Machines 2024, 12(9), 665; https://doi.org/10.3390/machines12090665 - 23 Sep 2024
Viewed by 889
Abstract
Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech [...] Read more.
Board games like chess serve as an excellent testbed for human–robot interactions, where advancements can lead to broader human–robot cooperation systems. This paper presents a chess-playing robotic system to demonstrate controlled pick and place operations using a 3-DoF manipulator with image and speech recognition. The system identifies chessboard square coordinates through image processing and centroid detection before mapping them onto the physical board. User voice input is processed and transcribed into a string from which the system extracts the current and destination locations of a chess piece with a word error rate of 8.64%. Using an inverse-kinematics algorithm, the system calculates the joint angles needed to position the end effector at the desired coordinates actuating the robot. The developed system was evaluated experimentally on the 3-DoF manipulator with a voice command used to direct the robot movement in grasping a chess piece. Consideration was made involving both the own pieces as well as capturing the opponent’s pieces and moving the captured piece outside the board workspace. Full article
(This article belongs to the Special Issue Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping Concepts)
►▼ Show Figures

Figure 1

18 pages, 5787 KiB  
Article
A Novel Grasp Detection Algorithm with Multi-Target Semantic Segmentation for a Robot to Manipulate Cluttered Objects
by Xungao Zhong, Yijun Chen, Jiaguo Luo, Chaoquan Shi and Huosheng Hu
Machines 2024, 12(8), 506; https://doi.org/10.3390/machines12080506 - 27 Jul 2024
Cited by 1 | Viewed by 981
Abstract
Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and [...] Read more.
Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and this loss of detail limits the grasp performance of robots in complex environments. This paper proposes a high-performance grasp detection algorithm with a multi-target semantic segmentation model, which can effectively improve a robot’s grasp success rate in cluttered environments. The algorithm consists of two cascades: Semantic Segmentation and Grasp Detection modules (SS-GD), in which the backbone network of the semantic segmentation module is developed by using the state-of-the-art Swin Transformer structure. It can extract the detailed features of objects in cluttered environments and enable a robot to understand the position and shape of the candidate object. To construct the grasp schema SS-GD focused on important vision features, a grasp detection module is designed based on the Squeeze-and-Excitation (SE) attention mechanism, to predict the corresponding grasp configuration accurately. The grasp detection experiments were conducted on an actual UR5 robot platform to verify the robustness and generalization of the proposed SS-GD method in cluttered environments. A best grasp success rate of 91.7% was achieved for cluttered multi-target workspaces. Full article
(This article belongs to the Special Issue Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping Concepts)
►▼ Show Figures

Figure 1

16 pages, 6915 KiB  
Article
Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS
by Mohammad Sheikhsamad, Raúl Suárez and Jan Rosell
Machines 2024, 12(6), 364; https://doi.org/10.3390/machines12060364 - 23 May 2024
Viewed by 913
Abstract
Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human [...] Read more.
Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper. Full article
(This article belongs to the Special Issue Advances in Robotic Manipulation through Artificial Intelligence and Innovative Gripping Concepts)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Physics-Based Self-Supervised Grasp Pose Detection
Authors: Jon Ander Ruiz; Ander Iriondo; Elena Lazkano; Ander Ansuategi; Iñaki Maurtua
Affiliation: Tekniker - BRTA, Basque Country, Spain
Abstract: Current industrial robotic manipulators have made evident their lack of flexibility. The systems must know beforehand the piece and it’s position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, an often sought tool is an extensive grasp dataset. This work introduces our Physics-Based Self-Supervised Grasp Pose Detection (PBSS-GPD) pipeline for model-based grasping point detection, useful to generate grasp pose datasets. Given a gripper-object pair, it samples grasping pose candidates using a modified version of GPD (implementing inner-grasps, CAD support...) and quantifies their quality using the MuJoCo physics engine and a grasp quality metric that takes into account the pose of the object over time. The system is optimised to run on CPU in headless-parallelised mode, with the option of running in a graphical interface or headless and storing videos of the process. The system has been validated obtaining grasping poses for a subset of Egad! objects using the Franka Panda two-finger gripper, compared with state of the art grasp generation pipelines and tested in a real scenario. While our system achieves similar accuracy compared to a contemporary approach, 84% on the real world validation, it has proven to be effective at generating grasps with good centering 18 times faster than the compared system.

Back to TopTop