Small cylindrical object navigating near free-surface is subject to wave disturbance. Wave disturbance can dramatically influence the depth control of the near-surface navigating object. A self-regulating fuzzy depth control method is proposed to keep the small cylindrical object navigate at the expected depth under free-surface disturbance. The near-surface dynamic model of the cylindrical object is presented and the fuzzy depth control method is modeled. The results obtained by a series of hardware-in-loop simulations demonstrate that the self-tuning fuzzy depth control method is valid to keep the small cylindrical object navigate steadily in the expected depth under free-surface disturbances.
This paper presents a vibrotactile belt to display precise directional information for visually impaired. Considering the characteristics of tactile perception, the torso-related transfer function was used to arrange actuators on the belt, and a coding algorithm using vibrotactile funneling illusion was proposed to display precise directional information. A psychophysical experiment was performed to evaluate the validity of the belt in displaying precise directional information. The experimental results indicated that the vibrotactile belt using our proposed coding algorithm achieves a resolution of 7.5 degrees with a high recognition accuracy of up to 91%. The current work provides valuable guidance for the design of vibrotactile navigation aids.
Haptic rendering of compliance is widely used in human–computer haptic interaction. Haptic impressions of virtual objects are usually controlled through rendering algorithms and devices. However, subjective feelings of compliance are easily affected by physical properties of objects, interactive modes, and so on. So it is important to ascertain the mapping relations between controlled physical parameters and subjective perceptual feelings. In this paper, a multi-layered perceptual model was built based on psychophysical experiments to discuss these relationships in a simplified scene. Interactive signals of physical stimuli are collected by the physical receptor layer, handled by the subjective classifier layer and finally generate the evaluation results of compliance. The physical perceptual layer is used to extract useful interaction features affecting perceptual results. The subjective classifier layer is used to analyze the perceptual dimensionality of the compliance perception. The final aim of the model is to determine the mapping relationships between interaction features and dimensions of perception space. Interactive features are extracted from the interaction data collected during the exploring process, perceptual dimensionality of the compliance perception was analyzed by the factor analysis method, and relations between hierarchical layers were obtained by multi-linear regression analysis. A verification test was performed to show whether the proposed model can predict the perceptual result of new samples well. The results indicate that the model was reliable to estimate the perceptual results of compliance with an accuracy of approximately 90%. This paper may contribute a lot to the design and improvement of human-computer interaction and intelligent sensing system.
Robotic systems are usually controlled to repetitively perform specific actions for manufacturing tasks. The traditional control methods are domain-dependent and model-dependent with cost of much human efforts. They cannot meet the new requirements of generality and flexibility in many areas such as intelligent manufacturing and customized production. This paper develops a general model-free approach to enable robots to perform multi-step object sorting tasks through deep reinforcement learning. Taking projected heightmap images from different time steps as input without extra high-level image analysis and understanding, critic models are designed to produce a pixel-wise Q value map for each type of action. It is a new trial to apply pixel-wise Q value-based critic networks to solve multi-step sorting tasks that involve many types of actions and complex action constraints. The experimental validations on simulated and realistic object sorting tasks demonstrate the effectiveness of the proposed approach. Qualitative results (videos), code for simulated and realistic experiments, and pre-trained models are available at https://github.com/JiatongBao/DRLSorting
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