Special Issue "Cognitive Robotics & Control"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editor

Prof. Dr. Cecilio Angulo
E-Mail Website
Guest Editor
Universitat Politècnica de Catalunya – BarcelonaTech, Barcelona 08028, Spain
Interests: Cognitive Systems, Cognitive Robotics, Control Engineering

Special Issue Information

Dear Colleagues,

Robotics and control are research and application domains that have been frequently engineered by the use of transdisciplinary approaches like cybernetics. In this sense, cognition is a particular concept of this approach, abstracted from the context of living organisms to that of artificial devices, about knowledge acquisition and understanding through thought, experience, and the senses. Hence, cognitive robotics and control refer to knowledge processing as much as knowledge generation from problem understanding, leading to special forms of architectures enabling systems to behave in an autonomous way.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging applications and address recent breakthroughs in the domain of cognitive robotics and control and related areas. Procedures, algorithms, architectures and implementations for reasoning, problem solving or decision making in the domain of robotics and control are elements under consideration. The topics of interest include, but are not limited to:

  • Cognitive social/service robots
  • Cognitive environments
  • Cognition in human-computer interaction
  • Knowledge and representation in cognitive architectures
  • Autonomy in robotics and control
  • Sensorimotor contingencies
  • Action and perception cognitive coupling

Prof. Dr. Cecilio Angulo
Guest Editor

Manuscript Submission Information

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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. Electronics 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 1400 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.

Published Papers (8 papers)

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Research

Open AccessFeature PaperArticle
On Inferring Intentions in Shared Tasks for Industrial Collaborative Robots
Electronics 2019, 8(11), 1306; https://doi.org/10.3390/electronics8111306 - 07 Nov 2019
Abstract
Inferring human operators’ actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a [...] Read more.
Inferring human operators’ actions in shared collaborative tasks plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space, but also forces and the execution of a task. In this article, we present a robotic system that is able to identify different human’s intentions and to adapt its behavior consequently, only employing force data. In order to accomplish this aim, three major contributions are presented: (a) a force based operator’s intention recognition system based on data from only two users; (b) a force based dataset of physical human–robot interaction; and (c) validation of the whole system with 15 people in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human–robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
RTPO: A Domain Knowledge Base for Robot Task Planning
Electronics 2019, 8(10), 1105; https://doi.org/10.3390/electronics8101105 - 01 Oct 2019
Abstract
Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and [...] Read more.
Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
Optimized Proportional-Integral-Derivative Controller for Upper Limb Rehabilitation Robot
Electronics 2019, 8(8), 826; https://doi.org/10.3390/electronics8080826 - 25 Jul 2019
Abstract
This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning [...] Read more.
This paper proposes a nature inspired, meta-heuristic optimization technique to tune a proportional-integral-derivative (PID) controller for a robotic arm exoskeleton RAX-1. The RAX-1 is a two-degrees-of-freedom (2-DOFs) upper limb rehabilitation robotic system comprising two joints to facilitate shoulder joint movements. The conventional tuning of PID controllers using Ziegler-Nichols produces large overshoots which is not desirable for rehabilitation applications. To address this issue, nature inspired algorithms have recently been proposed to improve the performance of PID controllers. In this study, a 2-DOF PID control system is optimized offline using particle swarm optimization (PSO) and artificial bee colony (ABC). To validate the effectiveness of the proposed ABC-PID method, several simulations were carried out comparing the ABC-PID controller with the PSO-PID and a classical PID controller tuned using the Zeigler-Nichols method. Various investigations, such as determining system performance with respect to maximum overshoot, rise and settling time and using maximum sensitivity function under disturbance, were carried out. The results of the investigations show that the ABC-PID is more robust and outperforms other tuning techniques, and demonstrate the effective response of the proposed technique for a robotic manipulator. Furthermore, the ABC-PID controller is implemented on the hardware setup of RAX-1 and the response during exercise showed minute overshoot with lower rise and settling times compared to PSO and Zeigler-Nichols-based controllers. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation
Electronics 2019, 8(7), 772; https://doi.org/10.3390/electronics8070772 - 10 Jul 2019
Cited by 1
Abstract
Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements [...] Read more.
Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time. During the last years, the use of learning from demonstration (LfD) has been proposed to perform interactive guidance based on simple tasks that are usually composed of a single stage. However, it would be desirable to improve this approach to solve complex tasks composed of several stages or gestures. This paper extends the LfD approach for object telemanipulation where the task to be solved is divided into a set of gestures that need to be detected. Thus, each gesture is previously trained and encoded within a Gaussian mixture model using LfD, and stored in a gesture library. During telemanipulation, depending on the sensory information, the gesture that is being carried out is recognized using the same LfD trained model for haptic guidance. The method was experimentally verified in a teleoperated peg-in-hole insertion task. A KUKA LWR4+ lightweight robot was remotely controlled with a Sigma.7 haptic device with LfD-based shared control. Finally, a comparison was carried out to evaluate the performance of Gaussian mixture models with a well-established gesture recognition method, continuous hidden Markov models, for the same task. Results show that the Gaussian mixture models (GMM)-based method slightly improves the success rate, with lower training and recognition processing times. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
A New Seven-Segment Profile Algorithm for an Open Source Architecture in a Hybrid Electronic Platform
Electronics 2019, 8(6), 652; https://doi.org/10.3390/electronics8060652 - 10 Jun 2019
Abstract
The velocity profiles are used in the design of trajectories in motion control systems. It is necessary to design smoother movements to avoid high stress in the motor. In this paper, the rate of change in acceleration value is used to develop an [...] Read more.
The velocity profiles are used in the design of trajectories in motion control systems. It is necessary to design smoother movements to avoid high stress in the motor. In this paper, the rate of change in acceleration value is used to develop an S-curve velocity profile which presents an acceleration and deceleration stage smoother than the trapezoidal velocity profile reducing the error at the end of the duty-cycle pre-established in one degree of freedom (DoF) application. Furthermore, a new methodology is developed to generate a seven-segment profile that works with negative velocity and displacement constraints applying an open source architecture in a hybrid electronic platform compounded by a system on a chip (SoC) Raspberry Pi 3 and a field programmable gate array (FPGA). The performance of the motion controller is measured through the comparison of the error obtained in real-time application with a trapezoidal velocity profile. As a result, a low-cost platform and an open architecture system are achieved. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
Transferring Know-How for an Autonomous Camera Robotic Assistant
Electronics 2019, 8(2), 224; https://doi.org/10.3390/electronics8020224 - 18 Feb 2019
Abstract
Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to [...] Read more.
Robotic platforms are taking their place in the operating room because they provide more stability and accuracy during surgery. Although most of these platforms are teleoperated, a lot of research is currently being carried out to design collaborative platforms. The objective is to reduce the surgeon workload through the automation of secondary or auxiliary tasks, which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time. One of the most important secondary tasks is the endoscopic camera guidance, whose automation would allow the surgeon to be concentrated on handling the surgical instruments. This paper proposes a novel autonomous camera guidance approach for laparoscopic surgery. It is based on learning from demonstration (LfD), which has demonstrated its feasibility to transfer knowledge from humans to robots by means of multiple expert showings. The proposed approach has been validated using an experimental surgical robotic platform to perform peg transferring, a typical task that is used to train human skills in laparoscopic surgery. The results show that camera guidance can be easily trained by a surgeon for a particular task. Later, it can be autonomously reproduced in a similar way to one carried out by a human. Therefore, the results demonstrate that the use of learning from demonstration is a suitable method to perform autonomous camera guidance in collaborative surgical robotic platforms. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
Control System in Open-Source FPGA for a Self-Balancing Robot
Electronics 2019, 8(2), 198; https://doi.org/10.3390/electronics8020198 - 09 Feb 2019
Cited by 1
Abstract
Computing in technological applications is typically performed with software running on general-purpose microprocessors, such as the Computer Processing Unit (CPU), or specific ones, like the Graphical Processing Unit (GPU). Application-Specific Integrated Circuits (ASICs) are an interesting option when speed and reliability are required, [...] Read more.
Computing in technological applications is typically performed with software running on general-purpose microprocessors, such as the Computer Processing Unit (CPU), or specific ones, like the Graphical Processing Unit (GPU). Application-Specific Integrated Circuits (ASICs) are an interesting option when speed and reliability are required, but development costs are usually high. Field-Programmable Gate Arrays (FPGA) combine the flexibility of software with the high-speed operation of hardware, and can keep costs low. The dominant FPGA infrastructure is proprietary, but open tools have greatly improved and are a growing trend, from which robotics can benefit. This paper presents a robotics application that was fully developed using open FPGA tools. An inverted pendulum robot was designed, built, and programmed using open FPGA tools, such as IceStudio and the IceZum Alhambra board, which integrates the iCE40HX4K-TQ144 from Lattice. The perception from an inertial sensor is used in a PD control algorithm that commands two DC motors. All the modules were synthesized in an FPGA as a proof of concept. Its experimental validation shows good behavior and performance. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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Open AccessArticle
Automatic Spray Trajectory Optimization on Bézier Surface
Electronics 2019, 8(2), 168; https://doi.org/10.3390/electronics8020168 - 01 Feb 2019
Abstract
The trajectory optimization of automatic spraying robot is still a challenging problem, which is very important in the whole spraying work. Spray trajectory optimization consists of two parts: spray space path and end-effector moving speed. A large number of spraying experiments have proved [...] Read more.
The trajectory optimization of automatic spraying robot is still a challenging problem, which is very important in the whole spraying work. Spray trajectory optimization consists of two parts: spray space path and end-effector moving speed. A large number of spraying experiments have proved that it is very important to find the best initial trajectory of spraying. This paper presents an automatic spray trajectory optimization that is based on the Bézier surface. Spray the workpiece for Bezier triangular surface modeling and find the best initial trajectory of the spraying robot, establish the appropriate spraying model, plan the appropriate space path, and finally plan the trajectory optimization along the specified painting path. The validity and practicability of the method presented in this paper are proved by an example. This method can also be extended to other applications. Full article
(This article belongs to the Special Issue Cognitive Robotics & Control)
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