scholarly journals Gait Planning and Sensory Feedback Control for Robotic Sensor Systems in Smart Cities

2019 ◽  
Vol 31 (6) ◽  
pp. 2073
Author(s):  
Helin Wang ◽  
Hao Zhang ◽  
Zhuping Wang ◽  
Qijun Chen
Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 721
Author(s):  
Maha Aldoumani ◽  
Baris Yuce ◽  
Dibin Zhu

In this paper, the performance, modelling and application of a planar electromagnetic sensor are discussed. Due to the small size profiles and their non-contact nature, planar sensors are widely used due to their simple and basic design. The paper discusses the experimentation and the finite element modelling (FEM) performed for developing the design of planar coils. In addition, the paper investigates the performance of various topologies of planar sensors when they are used in inductive sensing. This technique has been applied to develop a new displacement sensor. The ANSYS Maxwell FEM package has been used to analyse the models while varying the topologies of the coils. For this purpose, different models in FEM were constructed and then tested with topologies such as circular, square and hexagon coil configurations. The described methodology is considered an effective way for the development of sensors based on planar coils with better performance. Moreover, it also confirms a good correlation between the experimental data and the FEM models. Once the best topology is chosen based on performance, an optimisation exercise was then carried out using uncertainty models. That is, the influence of variables such as number of turns and the spacing between the coils on the output inductance has been investigated. This means that the combined effects of these two variables on the output inductance was studied to obtain the optimum values for the number of turns and the spacing between the coils that provided the highest level of inductance from the coils. Integrated sensor systems are a pre-requisite for developing the concept of smart cities in practice due to the fact that the individual sensors can hardly meet the demands of smart cities for complex information. This paper provides an overview of the theoretical concept of smart cities and the integrated sensor systems.


2019 ◽  
Author(s):  
Amanda M. Zimmet ◽  
Amy J. Bastian ◽  
Noah J. Cowan

ABSTRACTIt is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.


eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Amanda M Zimmet ◽  
Di Cao ◽  
Amy J Bastian ◽  
Noah J Cowan

It is thought that the brain does not simply react to sensory feedback, but rather uses an internal model of the body to predict the consequences of motor commands before sensory feedback arrives. Time-delayed sensory feedback can then be used to correct for the unexpected—perturbations, motor noise, or a moving target. The cerebellum has been implicated in this predictive control process. Here, we show that the feedback gain in patients with cerebellar ataxia matches that of healthy subjects, but that patients exhibit substantially more phase lag. This difference is captured by a computational model incorporating a Smith predictor in healthy subjects that is missing in patients, supporting the predictive role of the cerebellum in feedback control. Lastly, we improve cerebellar patients’ movement control by altering (phase advancing) the visual feedback they receive from their own self movement in a simplified virtual reality setup.


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