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A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (15 November 2021) | Viewed by 27290

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Special Issue Editors

Prof. Dr. Wataru Takano

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Guest Editor

Center for Mathematical Modeling and Data Science, Osaka University, Osaka, Japan
Interests: robot learning; humanoid robot; robot motion; control theory

Dr. Stefan Gheorghe Pentiuc

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Guest Editor

Faculty of Electrical Engineering and Computer Science, Universitatea Stefan cel Mare Suceava, 720229 Suceava, Romania
Interests: computer science; pattern recognition; distribute artificial intelligence; high performance computing

Special Issue Information

Dear Colleagues,

Artificial intelligence technologies have undergone dramatic development in cyberspace, leading to the creation of new services and the resolution of various social challenges. However, much work remains in order to embody artificial intelligence in physical space. Robots have a mechanical system as their body, and robotics is the domain of science and technology that handles interactions between the body and the physical world. In robotics, domain-specific knowledge that includes sensing/perception, computation of kinematic/dynamic-based actions, and control theory is accumulated. Robotics is expected to be combined with artificial intelligence, building a bridge between cyberspace and physical space and revolutionizing society. This Special Issue solicits papers that present methodologies for integrating artificial intelligence with robotics and that create new research directions with the potential for tremendous academic and social impacts.

Prof. Dr. Wataru Takano
Dr. Stefan Gheorghe Pentiuc
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. Robotics 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 1800 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

learning perception, action, and environment, and their interactions
modeling multiple levels of representations
learning human-robot or robot-robot interaction/communication
practical algorithm and real-time computation for robot intelligence
big data on robot perception and action
deep learning specific to robotics

Published Papers (6 papers)

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Research

13 pages, 6364 KiB

Open AccessArticle

Reduced Simulation: Real-to-Sim Approach toward Collision Detection in Narrowly Confined Environments

by Yusuke Takayama, Photchara Ratsamee and Tomohiro Mashita

Robotics 2021, 10(4), 131; https://doi.org/10.3390/robotics10040131 - 08 Dec 2021

Cited by 1 | Viewed by 2619

Abstract

Recently, several deep-learning based navigation methods have been achieved because of a high quality dataset collected from high-quality simulated environments. However, the cost of creating high-quality simulated environments is high. In this paper, we present a concept of the reduced simulation, which can serve as a simplified version of a simulated environment yet be efficient enough for training deep-learning based UAV collision avoidance approaches. Our approach deals with the reality gap between a reduced simulation dataset and real world dataset and can provide a clear guideline for reduced simulation design. Our experimental result confirmed that the reduction in visual features provided by textures and lighting does not affect operating performance with the user study. Moreover, by conducting collision detection experiments, we verified that our reduced simulation outperforms the conventional cost-effective simulations in adaptation capability with respect to realistic simulation and real-world scenario. Full article

(This article belongs to the Special Issue Robotics and AI)

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20 pages, 5223 KiB

Open AccessArticle

Multi-Objective Swarm Intelligence Trajectory Generation for a 7 Degree of Freedom Robotic Manipulator

by Aryslan Malik, Troy Henderson and Richard Prazenica

Robotics 2021, 10(4), 127; https://doi.org/10.3390/robotics10040127 - 27 Nov 2021

Cited by 8 | Viewed by 3685

Abstract

This work is aimed to demonstrate a multi-objective joint trajectory generation algorithm for a 7 degree of freedom (DoF) robotic manipulator using swarm intelligence (SI)—product of exponentials (PoE) combination. Given a priori knowledge of the end-effector Cartesian trajectory and obstacles in the workspace, the inverse kinematics problem is tackled by SI-PoE subject to multiple constraints. The algorithm is designed to satisfy finite jerk constraint on end-effector, avoid obstacles, and minimize control effort while tracking the Cartesian trajectory. The SI-PoE algorithm is compared with conventional inverse kinematics algorithms and standard particle swarm optimization (PSO). The joint trajectories produced by SI-PoE are experimentally tested on Sawyer 7 DoF robotic arm, and the resulting torque trajectories are compared. Full article

(This article belongs to the Special Issue Robotics and AI)

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17 pages, 2789 KiB

Open AccessArticle

Unsupervised Online Grounding for Social Robots

by Oliver Roesler and Elahe Bagheri

Robotics 2021, 10(2), 66; https://doi.org/10.3390/robotics10020066 - 29 Apr 2021

Viewed by 3851

Abstract

Robots that incorporate social norms in their behaviors are seen as more supportive, friendly, and understanding. Since it is impossible to manually specify the most appropriate behavior for all possible situations, robots need to be able to learn it through trial and error, by observing interactions between humans, or by utilizing theoretical knowledge available in natural language. In contrast to the former two approaches, the latter has not received much attention because understanding natural language is non-trivial and requires proper grounding mechanisms to link words to corresponding perceptual information. Previous grounding studies have mostly focused on grounding of concepts relevant to object manipulation, while grounding of more abstract concepts relevant to the learning of social norms has so far not been investigated. Therefore, this paper presents an unsupervised cross-situational learning based online grounding framework to ground emotion types, emotion intensities and genders. The proposed framework is evaluated through a simulated human–agent interaction scenario and compared to an existing unsupervised Bayesian grounding framework. The obtained results show that the proposed framework is able to ground words, including synonyms, through their corresponding perceptual features in an unsupervised and open-ended manner, while outperfoming the baseline in terms of grounding accuracy, transparency, and deployability. Full article

(This article belongs to the Special Issue Robotics and AI)

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20 pages, 2474 KiB

Open AccessArticle

Industrial Robot Trajectory Tracking Control Using Multi-Layer Neural Networks Trained by Iterative Learning Control

by Shuyang Chen and John T. Wen

Robotics 2021, 10(1), 50; https://doi.org/10.3390/robotics10010050 - 21 Mar 2021

Cited by 22 | Viewed by 6251

Abstract

Fast and precise robot motion is needed in many industrial applications. Most industrial robot motion controllers allow externally commanded motion profiles, but the trajectory tracking performance is affected by the robot dynamics and joint servo controllers, to which users have no direct access and about which they have little information. The performance is further compromised by time delays in transmitting the external command as a setpoint to the inner control loop. This paper presents an approach for combining neural networks and iterative learning controls to improve the trajectory tracking performance for a multi-axis articulated industrial robot. For a given desired trajectory, the external command is iteratively refined using a high-fidelity dynamical simulator to compensate for the robot inner-loop dynamics. These desired trajectories and the corresponding refined input trajectories are then used to train multi-layer neural networks to emulate the dynamical inverse of the nonlinear inner-loop dynamics. We show that with a sufficiently rich training set, the trained neural networks generalize well to trajectories beyond the training set as tested in the simulator. In applying the trained neural networks to a physical robot, the tracking performance still improves but not as much as in the simulator. We show that transfer learning effectively bridges the gap between simulation and the physical robot. Finally, we test the trained neural networks on other robot models in simulation and demonstrate the possibility of a general purpose network. Development and evaluation of this methodology are based on the ABB IRB6640-180 industrial robot and ABB RobotStudio software packages. Full article

(This article belongs to the Special Issue Robotics and AI)

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23 pages, 567 KiB

Open AccessArticle

The Heuristic of Directional Qualitative Semantic: A New Heuristic for Making Decisions about Spinning with Qualitative Reasoning

by Sergio Miguel-Tomé

Robotics 2021, 10(1), 17; https://doi.org/10.3390/robotics10010017 - 15 Jan 2021

Cited by 1 | Viewed by 3309

Abstract

Multifunctional Robot On Topological Notions (MROTN) is a research program that has as one of its goals to develop qualitative algorithms that make navigation decisions. This article presents new research from MROTN that extends previous results by allowing an agent to carry out qualitative reasoning about direction and spinning. The main result is a new heuristic, the Heuristic of Directional Qualitative Semantic (HDQS), which allows for selecting a spinning action to establish a directional relation between an agent and an object. The HDQS is based on the key idea of encoding directional information into topological relations. The new heuristic is important to the MROTN because it permits the continued development of qualitative navigation methods based on topological notions. We show this by presenting a new version of the Topological Qualitative Architecture of Navigation that uses the HDQS to address situations that require spinning. Full article

(This article belongs to the Special Issue Robotics and AI)

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16 pages, 3452 KiB

Open AccessArticle

Deep Learning-Based Object Classification and Position Estimation Pipeline for Potential Use in Robotized Pick-and-Place Operations

by Sergey Soltan, Artemiy Oleinikov, M. Fatih Demirci and Almas Shintemirov

Robotics 2020, 9(3), 63; https://doi.org/10.3390/robotics9030063 - 18 Aug 2020

Cited by 13 | Viewed by 5439

Abstract

Accurate object classification and position estimation is a crucial part of executing autonomous pick-and-place operations by a robot and can be realized using RGB-D sensors becoming increasingly available for use in industrial applications. In this paper, we present a novel unified framework for object detection and classification using a combination of point cloud processing and deep learning techniques. The proposed model uses two streams that recognize objects on RGB and depth data separately and combines the two in later stages to classify objects. Experimental evaluation of the proposed model including classification accuracy compared with previous works demonstrates its effectiveness and efficiency, making the model suitable for real-time applications. In particular, the experiments performed on the Washington RGB-D object dataset show that the proposed framework has 97.5% and 95% fewer parameters compared to the previous state-of-the-art multimodel neural networks Fus-CNN, CNN Features and VGG3D, respectively, with the cost of approximately 5% drop in classification accuracy. Moreover, the inference of the proposed framework takes 66.11%, 32.65%, and 28.77% less time on GPU and 86.91%, 51.12%, and 50.15% less time on CPU in comparison to VGG3D, Fus-CNN, and CNN Features. The potential applicability of the developed object classification and position estimation framework was then demonstrated on an experimental robot-manipulation setup realizing a simplified object pick-and-place scenario. In approximately 95% of test trials, the system was able to accurately position the robot over the detected objects of interest in an automatic mode, ensuring stable cyclic execution with no time delays. Full article

(This article belongs to the Special Issue Robotics and AI)

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