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Advanced Cognitive Robotics
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Advanced Cognitive Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 12680

Special Issue Editor

Dr. Antonio Bandera

E-Mail Website
Guest Editor
Department of Electronic Technology, University of Málaga, 29071 Málaga, Spain
Interests: assistive robotics; embedded vision; machine learning; image processing; pattern recognition; computer vision; architecture robotics; algorithms; artificial intelligence; mobile robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is a growing desire to develop robots that are capable of helping humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn not only from experience but also through interaction. In particular, cognitive robotics aims to endow robots with the capacity to plan solutions for complex goals and to enact those plans while being reactive to unexpected changes in their environments. Among the limiting factors for their application in real-life scenarios, there are clearly ethical, technological, and economic challenges.

Cognitive robotics includes studies on advanced mechatronics, artificial intelligence, and machine learning, as well as cognitive psychology and brain science in the frame of cognitive science. The aim of this Special Issue is to gather scientific papers addressing any of the challenges of cognitive robotics. The topics of this Special Issue include, but are not limited to, the following:

  • Active perception;
  • Architectures and frameworks for cognition;
  • Cognitive human–robot interaction;
  • Cognitive modeling and development;
  • Knowledge discovery and representation in robots;
  • Learning for action and interaction;
  • Cognitive architectures for interactive robots;
  • Neurorobotics;
  • Social and assistive robots.

Dr. Antonio Bandera
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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

Published Papers (4 papers)

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Research

16 pages, 1616 KiB  
Article
Analysis of a Human Meta-Strategy for Agents with Active and Passive Strategies
by Kensuke Miyamoto, Norifumi Watanabe, Osamu Nakamura and Yoshiyasu Takefuji
Appl. Sci. 2022, 12(17), 8720; https://doi.org/10.3390/app12178720 - 31 Aug 2022
Viewed by 1266
Abstract
Human cooperative behavior includes passive action strategies based on others and active action strategies that prioritize one’s own objective. Therefore, for cooperation with humans, it is necessary to realize a robot that uses these strategies to communicate as a human would. In this [...] Read more.
Human cooperative behavior includes passive action strategies based on others and active action strategies that prioritize one’s own objective. Therefore, for cooperation with humans, it is necessary to realize a robot that uses these strategies to communicate as a human would. In this research, we aim to realize robots that evaluate the actions of their opponents in comparison with their own action strategies. In our previous work, we obtained a Meta-Strategy with two action strategies through the simulation of learning between agents. However, humans’ Meta-Strategies may have different characteristics depending on the individual in question. In this study, we conducted a collision avoidance experiment in a grid space with agents with active and passive strategies for giving way. In addition, we analyzed whether a subject’s action changes when the agent’s strategy changes. The results showed that some subjects changed their actions in response to changes in the agent’s strategy, as well as subjects who behaved in a certain way regardless of the agent’s strategy and subjects who did not divide their actions. We considered that these types could be expressed in terms of differences in Meta-Strategies, such as active or passive Meta-Strategies for estimating an opponent’s strategy. Assuming a human Meta-Strategy, we discuss the action strategies of agents who can switch between active and passive strategies. Full article
(This article belongs to the Special Issue Advanced Cognitive Robotics)
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18 pages, 4286 KiB  
Article
On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model
by Adrián Romero-Garcés, Alejandro Hidalgo-Paniagua, Martín González-García and Antonio Bandera
Appl. Sci. 2022, 12(17), 8583; https://doi.org/10.3390/app12178583 - 27 Aug 2022
Cited by 3 | Viewed by 2204
Abstract
Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define [...] Read more.
Service robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse. Full article
(This article belongs to the Special Issue Advanced Cognitive Robotics)
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19 pages, 1914 KiB  
Article
A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
by Nizar Rokbani, Bilel Neji, Mohamed Slim, Seyedali Mirjalili and Raymond Ghandour
Appl. Sci. 2022, 12(14), 7091; https://doi.org/10.3390/app12147091 - 14 Jul 2022
Cited by 20 | Viewed by 2665
Abstract
In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their [...] Read more.
In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model. Full article
(This article belongs to the Special Issue Advanced Cognitive Robotics)
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36 pages, 10790 KiB  
Article
Telepresence Social Robotics towards Co-Presence: A Review
by Luis Almeida, Paulo Menezes and Jorge Dias
Appl. Sci. 2022, 12(11), 5557; https://doi.org/10.3390/app12115557 - 30 May 2022
Cited by 15 | Viewed by 5772
Abstract
Telepresence robots are becoming popular in social interactions involving health care, elderly assistance, guidance, or office meetings. There are two types of human psychological experiences to consider in robot-mediated interactions: (1) telepresence, in which a user develops a sense of being present near [...] Read more.
Telepresence robots are becoming popular in social interactions involving health care, elderly assistance, guidance, or office meetings. There are two types of human psychological experiences to consider in robot-mediated interactions: (1) telepresence, in which a user develops a sense of being present near the remote interlocutor, and (2) co-presence, in which a user perceives the other person as being present locally with him or her. This work presents a literature review on developments supporting robotic social interactions, contributing to improving the sense of presence and co-presence via robot mediation. This survey aims to define social presence, co-presence, identify autonomous “user-adaptive systems” for social robots, and propose a taxonomy for “co-presence” mechanisms. It presents an overview of social robotics systems, applications areas, and technical methods and provides directions for telepresence and co-presence robot design given the actual and future challenges. Finally, we suggest evaluation guidelines for these systems, having as reference face-to-face interaction. Full article
(This article belongs to the Special Issue Advanced Cognitive Robotics)
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