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Robotics
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Intelligent Systems in Robotics
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Intelligent Systems in Robotics

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

Deadline for manuscript submissions: closed (30 April 2018) | Viewed by 43512

Special Issue Editor

Prof. Dr. Donghun Kim

E-Mail Website
Guest Editor
Department of Electrical Engineering, Kyungnam University, 449 Woryeong-dong, Masanhappo-gu, Changwon, Kyungnam 631-701, Korea
Interests: mobile robot control; swarm robotics; IT-based robot; path planning

Special Issue Information

Dear Colleagues,

Nowadays, a variety of autonomous robots are changing our daily life by working on factory assembly lines, entertainment areas, service sites, and even mobile transportation. More interestingly, the unmanned aerial, ground, surface and/or underwater systems provide more efficient ways to execute various challenging tasks. Moreover, by employing intelligent approaches, advanced control methodologies for robot systems have been rapidly developed. For the reason, more efforts should be focused on the learning methodology for robotic systems. As a result, intelligent features, such as the fast adaptation, self-learning and autonomous capability, may be necessary for controlling robots.

Intelligent systems in robotics describes a class of control techniques that use various artificial intelligence (AI) techniques such as neural network control, fuzzy logic control, neuro-fuzzy control, expert systems, genetic control, evolutionary algorithms, multi-sensor integration, failure diagnosis, reconfigurable control, and swarm algorithms. Intelligent control systems are very useful in a robot area when no mathematical model is available a priori and intelligent control itself develops a system to be controlled. This special issue will focus on various applications of intelligent control in robotics.

This Special Issue aims at exhibiting the latest research achievement, findings and ideas in the areas of intelligence for robot control. The issue will carry revised and substantially extended versions of selected papers presented at the 18th International Symposium on Advanced Intelligent Systems (ISIS2017), but we also strongly encourage researchers unable to participate in the conference to submit articles for this call. 

Suitable topics include, but are not limited, to the following:

  • Intelligent control of mobile robots
  • Intelligent of robot manipulators
  • Applications of intelligent control to autonomous systems
  • Unmanned surface/ underwater/ aerial vehicles
  • Control of Swarm robots
  • Reinforcement learning based control of robots
  • Composite Learning Based Intelligent Control
  • Intelligent Optimization and Applications
  • Pattern Recognition, Image Processing, and Machine Learning for Robot
  • Robot System Modelling and Parameter Estimation
  • Advanced Intelligent Control

Prof. Dr. Donghun Kim
Guest Editor

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

  • Robotics
  • Intelligent systems
  • Intelligent control
  • Robotic vision
  • Unmanned surface/ underwater/aerial vehicles
  • Swarm robots
  • Autonomous systems

Published Papers (5 papers)

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Research

10 pages, 2157 KiB  
Article
Validating Autofocus Algorithms with Automated Tests
by Tobias Werner and Javier Carrasco
Robotics 2018, 7(3), 33; https://doi.org/10.3390/robotics7030033 - 25 Jun 2018
Cited by 2 | Viewed by 5002
Abstract
For an automated camera focus, a fast and reliable algorithm is key to its success. It should work in a precisely defined way for as many cases as possible. However, there are many parameters which have to be fine-tuned for it to work [...] Read more.
For an automated camera focus, a fast and reliable algorithm is key to its success. It should work in a precisely defined way for as many cases as possible. However, there are many parameters which have to be fine-tuned for it to work exactly as intended. Most literature only focuses on the algorithm itself and tests it with simulations or renderings, but not in real settings. Trying to gather this data by manually placing objects in front of the camera is not feasible, as no human can perform one movement repeatedly in the same way, which makes an objective comparison impossible. We therefore used a small industrial robot with a set of over 250 combinations of movement, pattern, and zoom-states to conduct these tests. The benefit of this method was the objectivity of the data and the monitoring of the important thresholds. Our interest laid in the optimization of an existing algorithm, by showing its performance in as many benchmarks as possible. This included standard use cases and worst-case scenarios. To validate our method, we gathered data from a first run, adapted the algorithm, and conducted the tests again. The second run showed improved performance. Full article
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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21 pages, 1347 KiB  
Article
Deep Learning Systems for Estimating Visual Attention in Robot-Assisted Therapy of Children with Autism and Intellectual Disability
by Alessandro Di Nuovo, Daniela Conti, Grazia Trubia, Serafino Buono and Santo Di Nuovo
Robotics 2018, 7(2), 25; https://doi.org/10.3390/robotics7020025 - 04 Jun 2018
Cited by 60 | Viewed by 10005
Abstract
Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This [...] Read more.
Recent studies suggest that some children with autism prefer robots as tutors for improving their social interaction and communication abilities which are impaired due to their disorder. Indeed, research has focused on developing a very promising form of intervention named Robot-Assisted Therapy. This area of intervention poses many challenges, including the necessary flexibility and adaptability to real unconstrained therapeutic settings, which are different from the constrained lab settings where most of the technology is typically tested. Among the most common impairments of children with autism and intellectual disability is social attention, which includes difficulties in establishing the correct visual focus of attention. This article presents an investigation on the use of novel deep learning neural network architectures for automatically estimating if the child is focusing their visual attention on the robot during a therapy session, which is an indicator of their engagement. To study the application, the authors gathered data from a clinical experiment in an unconstrained setting, which provided low-resolution videos recorded by the robot camera during the child–robot interaction. Two deep learning approaches are implemented in several variants and compared with a standard algorithm for face detection to verify the feasibility of estimating the status of the child directly from the robot sensors without relying on bulky external settings, which can distress the child with autism. One of the proposed approaches demonstrated a very high accuracy and it can be used for off-line continuous assessment during the therapy or for autonomously adapting the intervention in future robots with better computational capabilities. Full article
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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19 pages, 2294 KiB  
Article
Motion Planning for a Chain of Mobile Robots Using A* and Potential Field
by Apoorva, Rahul Gautam and Rahul Kala
Robotics 2018, 7(2), 20; https://doi.org/10.3390/robotics7020020 - 18 May 2018
Cited by 12 | Viewed by 7962
Abstract
Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are [...] Read more.
Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment. Full article
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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18 pages, 3336 KiB  
Article
Robust Composite High-Order Super-Twisting Sliding Mode Control of Robot Manipulators
by Shahnaz Tayebi-Haghighi, Farzin Piltan and Jong-Myon Kim
Robotics 2018, 7(1), 13; https://doi.org/10.3390/robotics7010013 - 01 Mar 2018
Cited by 53 | Viewed by 8408
Abstract
This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple [...] Read more.
This paper describes the design of a robust composite high-order super-twisting sliding mode controller (HOSTSMC) for robot manipulators. Robot manipulators are extensively used in industrial manufacturing for many complex and specialized applications. These applications require robots with nonlinear mechanical architectures, resulting in multiple control challenges in various applications. To address this issue, this paper focuses on designing a robust composite high-order super-twisting sliding mode controller by combining a higher-order super-twisting sliding mode controller as the main controller with a super-twisting higher-order sliding mode observer as unknown state measurement and uncertainty estimator in the presence of uncertainty. The proposed method adaptively improves the traditional sliding mode controller (TSMC) and the estimated state sliding mode controller (ESMC) to attenuate the chattering. The effectiveness of a HOSTSMC is tested over six degrees of freedom (DOF) using a Programmable Universal Manipulation Arm (PUMA) robot manipulator. The proposed method outperforms the TSMC and ESMC, yielding 4.9% and 2% average performance improvements in the output position root-mean-square (RMS) error and average error, respectively. Full article
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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16222 KiB  
Article
Design of a Novel Leg-Wheel Hexapod Walking Robot
by Franco Tedeschi and Giuseppe Carbone
Robotics 2017, 6(4), 40; https://doi.org/10.3390/robotics6040040 - 14 Dec 2017
Cited by 19 | Viewed by 11194
Abstract
Hexapod walking robots have been widely addressed in the literature with a very large number of design and engineering solutions. However, specific design approaches and solutions are needed to cope with specific novel applications. This paper aims to address the design of a [...] Read more.
Hexapod walking robots have been widely addressed in the literature with a very large number of design and engineering solutions. However, specific design approaches and solutions are needed to cope with specific novel applications. This paper aims to address the design of a hexapod walking robot having exploration, architectonic survey, and maintenance of cultural heritage goods as its main application tasks. This specific case of study is addressed by carrying out a detailed design, which led to the construction of a novel hexapod walking robot, named Cassino Hexapod III. The proposed robot is composed of hybrid legs of a modular anthropomorphic architecture with omni-wheels as feet at its extremity. The proposed design and engineering solutions can overcome the limits of other existing prototypes and to fulfil the specific application requirements and constraints with a cost-effective and user-friendly solution. The proposed novel solutions have also originated an Italian patent No. 102014902238772. Full article
(This article belongs to the Special Issue Intelligent Systems in Robotics)
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