Fuzzy Logic Controller Design for Intelligent Robots

Chen, Ching-Han; Wang, Chien-Chun; Wang, Yi Tun; Wang, Po Tung

doi:10.1155/2017/8984713

Cited by 20 publications

(7 citation statements)

References 19 publications

(19 reference statements)

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“…Traditional robotic control systems have been recently implemented for self-reconfigurable robots. For example, [ 157 ] uses a robust adaptive fuzzy controller to adapt a dynamic self-reconfigurable robot to different configurations without adjusting control parameters. Table A9 in Appendix H compares the main computational approaches for four categories of robots.…”

Section: Snn Embodiment Into Cognitive Robotic Systemsmentioning

confidence: 99%

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham,

D’Angiulli,

Dehnavi

et al. 2023

Brain Sciences

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We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

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Section: Snn Embodiment Into Cognitive Robotic Systemsmentioning

confidence: 99%

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham,

D’Angiulli,

Dehnavi

et al. 2023

Brain Sciences

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“…The tests showed that the suggested fuzzy logic worked satisfactorily. The control allowed the robot could display intelligent human behavior under complicated conditions [50]. As known that the great performances of conventional controllers cannot be accomplished if high non-linearity or uncertainty characterized the mechanisms to be regulated.…”

Section: Fuzzy Logic Controllersmentioning

confidence: 99%

Review of Intelligent Control Systems with Robotics

Abbas

Mashhadany

Hameed

et al. 2022

IJEEI

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The interaction between humans and robots provides several important elements in improving the productivity of the tool in mechanical technology because the development of many complex tasks takes place through the application of multi-use robots technology that is self-adapted in these processes in various industrial or medical applications, which greatly contributed to the development of human life and achieving ways well-off Existing automated control frameworks have shaken up the construction business, making them highly adaptable and easy to use. This paper examines current and upcoming types of control frameworks and their implementation in mechanical technology, and the function of artificial intelligence in applying autonomy. In addition, he is expected to reveal insights into the various issues around control frameworks and the different approaches to fixing them. In addition, it proposes the basics of the application of autonomous control frameworks and various types of control frameworks in mechanical technology. Each type of control framework has its pros and cons which are discussed in this paper. Another type of robot control framework that upgrades the difficulty of the quest stage is man-made brainpower. Part of the speculations used in man-made reasoning, for example, artificial intelligence (AI) such as fuzzy logic, neural network, and genetic algorithm, are shown in this paper. Finally, a part of the joint effort between mechanical autonomy, personnel, and innovation is indicated. Coordinated human effort, for example, kinematic signal recognition used in games and versatile upper arm-based robots used in the clinical field for individuals with disabilities. Later on, it is only natural that the importance of different sensors is built up, thus expanding the knowledge and activity of the robot into a modern field. The data in this paper was collected based on many international references in publishing houses such as Springer, Elsevier, MDPI, IEEE, and others.

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“…One possible function on which adaptive control in mobile robot navigation tasks could be based is collision probability (CP) [35] since obstacle avoidance usually has the highest priority in the navigation stack hierarchy. The mediation can be implemented in several forms, and one of them is based on fuzzy set theory [36]. It is worth noting that several mediation engines can be used in the process, as well as several decision functions based on which the mediator infers the final decision and thus adapts the robot's behavior [18].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Fuzzy Mediation for Multimodal Control of Mobile Robots in Navigation-Based Tasks

Musić¹,

Kruzic²,

Stančić³

et al. 2019

IJCIS

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The paper proposes and analyses performance of a fuzzy-based mediator with showcase examples in robot navigation. The mediator receives outputs from two controllers and uses estimated collision probability for adapting the signal proportions in the final output. The approach was implemented and tested in simulation and on real robots with different footprints. The task complexity during testing varied from single obstacle avoidance to a realistic navigation in real environments. The obtained results showed that this approach is simple but effective.

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Fuzzy Logic Controller Design for Intelligent Robots

Cited by 20 publications

References 19 publications

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Review of Intelligent Control Systems with Robotics

Adaptive Fuzzy Mediation for Multimodal Control of Mobile Robots in Navigation-Based Tasks

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