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Electronics
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Cognitive Robotics
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Cognitive Robotics

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 22539

Special Issue Editor

Dr. Janos Botzheim

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Faculty of Informatics, Eötvös Loránd University, Pázmány Péter Stny. 1/C, 1117 Budapest, Hungary
Interests: computational intelligence; cognitive robotics

Special Issue Information

Dear Colleagues,

Recently, various types of intelligent robots have been developed for the society of the next generation. In particular, intelligent robots should continue to perform tasks in real environments such as houses, commercial facilities, and public facilities. The growing need to automate daily tasks combined with new robot technologies are driving the development of human-friendly robots, i.e., safe and dependable machines, operating in close vicinity to humans or directly interacting with persons in a wide range of domains. The technology shift from classical industrial robots which are safely kept away from humans in cages to robots which will be used in close collaboration with humans requires major technological challenges that need to be overcome. Computational intelligence is very important to provide human-friendly services by robots. A robot should have human-like intelligence and cognitive capabilities to co-exist with people. The study on the intelligence, cognition, and self of robots has a long history. The concepts on adaptation, learning, and cognitive development should be introduced more intensively in the next-generation robotics from the theoretical point of view. Fuzzy, neural, and evolutionary computation play an important role in realizing the cognitive development of robots from a methodological point of view. Furthermore, the synthesis of information technology, network technology, and robot technology may bring the brand-new emerging intelligence to robots from a technical point of view. This Special Issue focuses on the intelligence of robots emerging from the adaptation, learning, and cognitive development through the interaction with people and dynamic environments from the conceptual, theoretical, methodological, and/or technical points of view.

Prof. Dr. Janos Botzheim
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. Electronics 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.

Keywords

  • Robot intelligence
  • Learning, adaptation, and evolution in robotics
  • Human–robot interaction
  • Embodied cognitive science
  • Perception and action
  • Intelligent robots
  • Fuzzy, neural, and evolutionary computation for robotics
  • Evolutionary robotics
  • Soft computing for vision and learning

Published Papers (8 papers)

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Editorial

Jump to: Research, Review

2 pages, 153 KiB  
Editorial
Cognitive Robotics
by János Botzheim
Electronics 2021, 10(13), 1510; https://doi.org/10.3390/electronics10131510 - 22 Jun 2021
Cited by 2 | Viewed by 1229
Abstract
Recently, various types of intelligent robots have been developed for the society of the next generation [...] Full article
(This article belongs to the Special Issue Cognitive Robotics)

Research

Jump to: Editorial, Review

30 pages, 1677 KiB  
Article
Bacterial Evolutionary Algorithm-Trained Interpolative Fuzzy System for Mobile Robot Navigation
by Ferenc Ádám Szili, János Botzheim and Balázs Nagy
Electronics 2022, 11(11), 1734; https://doi.org/10.3390/electronics11111734 - 30 May 2022
Cited by 3 | Viewed by 1313
Abstract
This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy [...] Read more.
This paper describes the process of building a transport logic that enables a mobile robot to travel fast enough to reach a desired destination in time, but safe enough to prevent damage. This transport logic is based on fuzzy logic inference using fuzzy rule interpolation, which allows for accurate inferences even when using a smaller rule base. The construction of the fuzzy rule base can be conducted experimentally, but there are also solutions for automatic construction. One of them is the bacterial evolutionary algorithm, which is used in this application. This algorithm is based on the theory of bacterial evolution and is very well-suited to solving optimization problems. Successful transport is also facilitated by proper path planning, and for this purpose, the so-called neuro-activity-based path planning has been used. This path-planning algorithm is combined with interpolative fuzzy logic-based speed control of the mobile robot. By applying the described methods, an intelligent transport logic can be constructed. These methods are tested in a simulated environment and several results are investigated. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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14 pages, 1057 KiB  
Article
Improved Rapidly Exploring Random Tree with Bacterial Mutation and Node Deletion for Offline Path Planning of Mobile Robot
by Aphilak Lonklang and János Botzheim
Electronics 2022, 11(9), 1459; https://doi.org/10.3390/electronics11091459 - 03 May 2022
Cited by 11 | Viewed by 2848
Abstract
The path-planning algorithm aims to find the optimal path between the starting and goal points without collision. One of the most popular algorithms is the optimized Rapidly exploring Random Tree (RRT*). The strength of RRT* algorithm is the collision-free path. It is the [...] Read more.
The path-planning algorithm aims to find the optimal path between the starting and goal points without collision. One of the most popular algorithms is the optimized Rapidly exploring Random Tree (RRT*). The strength of RRT* algorithm is the collision-free path. It is the main reason why RRT-based algorithms are used in path planning for mobile robots. The RRT* algorithm generally creates the node for randomly making a tree branch to reach the goal point. The weakness of the RRT* algorithm is in the random process when the randomized nodes fall into the obstacle regions. The proposed algorithm generates a new random environment by removing the obstacle regions from the global environment. The objective is to minimize the number of unusable nodes from the randomizing process. The results show better performance in computational time and overall path-planning length. Bacterial mutation and local search algorithms are combined at post-processing to get a better path length and reduce the number of nodes. The proposed algorithm is tested in simulation. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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21 pages, 7766 KiB  
Article
Perceptual and Semantic Processing in Cognitive Robots
by Syed Tanweer Shah Bukhari and Wajahat Mahmood Qazi
Electronics 2021, 10(18), 2216; https://doi.org/10.3390/electronics10182216 - 10 Sep 2021
Cited by 2 | Viewed by 2378
Abstract
The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to [...] Read more.
The challenge in human–robot interaction is to build an agent that can act upon human implicit statements, where the agent is instructed to execute tasks without explicit utterance. Understanding what to do under such scenarios requires the agent to have the capability to process object grounding and affordance learning from acquired knowledge. Affordance has been the driving force for agents to construct relationships between objects, their effects, and actions, whereas grounding is effective in the understanding of spatial maps of objects present in the environment. The main contribution of this paper is to propose a methodology for the extension of object affordance and grounding, the Bloom-based cognitive cycle, and the formulation of perceptual semantics for the context-based human–robot interaction. In this study, we implemented YOLOv3 to formulate visual perception and LSTM to identify the level of the cognitive cycle, as cognitive processes synchronized in the cognitive cycle. In addition, we used semantic networks and conceptual graphs as a method to represent knowledge in various dimensions related to the cognitive cycle. The visual perception showed average precision of 0.78, an average recall of 0.87, and an average F1 score of 0.80, indicating an improvement in the generation of semantic networks and conceptual graphs. The similarity index used for the lingual and visual association showed promising results and improves the overall experience of human–robot interaction. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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17 pages, 2527 KiB  
Article
Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions
by Fei Yan, Abdullah M. Iliyasu and Kaoru Hirota
Electronics 2021, 10(2), 100; https://doi.org/10.3390/electronics10020100 - 06 Jan 2021
Cited by 7 | Viewed by 2532
Abstract
This study presents a modest attempt to interpret, formulate, and manipulate the emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes emotion information as a superposition state, whilst unitary operators are used to manipulate the transition of emotion states [...] Read more.
This study presents a modest attempt to interpret, formulate, and manipulate the emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes emotion information as a superposition state, whilst unitary operators are used to manipulate the transition of emotion states which are subsequently recovered via appropriate quantum measurement operations. The framework described provides essential steps towards exploiting the potency of quantum mechanics in a quantum affective computing paradigm. Further, the emotions of multi-robots in a specified communication scenario are fused using quantum entanglement, thereby reducing the number of qubits required to capture the emotion states of all the robots in the environment, and therefore fewer quantum gates are needed to transform the emotion of all or part of the robots from one state to another. In addition to the mathematical rigours expected of the proposed framework, we present a few simulation-based demonstrations to illustrate its feasibility and effectiveness. This exposition is an important step in the transition of formulations of emotional intelligence to the quantum era. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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15 pages, 3218 KiB  
Article
An RSSI-Based Localization, Path Planning and Computer Vision-Based Decision Making Robotic System
by Jatin Upadhyay, Abhishek Rawat, Dipankar Deb, Vlad Muresan and Mihaela-Ligia Unguresan
Electronics 2020, 9(8), 1326; https://doi.org/10.3390/electronics9081326 - 17 Aug 2020
Cited by 8 | Viewed by 4328
Abstract
A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small [...] Read more.
A robotic navigation system operates flawlessly under an adequate GPS signal range, whereas indoor navigation systems use the simultaneous localization and mapping system or other vision-based localization systems. The sensor used in indoor navigation systems is not suitable for low power and small scale robotic systems. The wireless area network transmitting devices have fixed transmission power, and the receivers get the different values of signal strength based on their surrounding environments. In the proposed method, the received signal strength index (RSSI) values of three fixed transmitter units are measured every 1.6 m in mesh format and analyzed by the classifiers, and robot position can be mapped in the indoor area. After navigation, the robot analyzes objects and detects and recognize human faces with the help of object recognition and facial recognition-based classification methods respectively. This robot detects the intruder with the current position in an indoor environment. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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19 pages, 2721 KiB  
Article
Intrinsic Motivation Based Hierarchical Exploration for Model and Skill Learning
by Lina Lu, Wanpeng Zhang, Xueqiang Gu and Jing Chen
Electronics 2020, 9(2), 312; https://doi.org/10.3390/electronics9020312 - 11 Feb 2020
Viewed by 2154
Abstract
Hierarchical skill learning is an important research direction in human intelligence. However, many real-world problems have sparse rewards and a long time horizon, which typically pose challenges in hierarchical skill learning and lead to the poor performance of naive exploration. In this work, [...] Read more.
Hierarchical skill learning is an important research direction in human intelligence. However, many real-world problems have sparse rewards and a long time horizon, which typically pose challenges in hierarchical skill learning and lead to the poor performance of naive exploration. In this work, we propose an algorithmic framework called surprise-based hierarchical exploration for model and skill learning (Surprise-HEL). The framework leverages the surprise-based intrinsic motivation for improving the efficiency of sampling and driving exploration. It also combines the surprise-based intrinsic motivation and the hierarchical exploration to speed up the model learning and skill learning. Moreover, the framework incorporates the reward independent incremental learning rules and the technique of alternating model learning and policy update to handle the changing intrinsic rewards and the changing models. These works enable the framework to implement the incremental and developmental learning of models and hierarchical skills. We tested Surprise-HEL on a common benchmark domain: Household Robot Pickup and Place. The evaluation results show that the Surprise-HEL framework can significantly improve the agent’s efficiency in model and skill learning in a typical complex domain. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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Review

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31 pages, 2463 KiB  
Review
On the Gap between Domestic Robotic Applications and Computational Intelligence
by Junpei Zhong, Chaofan Ling, Angelo Cangelosi, Ahmad Lotfi and Xiaofeng Liu
Electronics 2021, 10(7), 793; https://doi.org/10.3390/electronics10070793 - 27 Mar 2021
Cited by 11 | Viewed by 3938
Abstract
Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) [...] Read more.
Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots. Full article
(This article belongs to the Special Issue Cognitive Robotics)
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