Real-time Adaptive Prediction Method for Smooth Haptic Rendering
March 22, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xiyuan Hou, Olga Sourina
arXiv ID
1603.06674
Category
cs.HC: Human-Computer Interaction
Citations
5
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In this paper, we propose a real-time adaptive prediction method to calculate smooth and accurate haptic feedback in complex scenarios. Smooth haptic feedback is an important task for haptic rendering with complex virtual objects. However, commonly the update rate of the haptic rendering may drop down during multi-point contact in complex scenarios because high computational cost is required for collision detection and physically-based dynamic simulation. If the haptic rendering is done at a lower update rate, it may cause discontinuous or instable force/torque feedback. Therefore, to implement smooth and accurate haptic rendering, the update rate of force/torque calculation should be kept in a high and constant frequency. In the proposed method, the auto-regressive model with real-time coefficients update is proposed to predict interactive forces/torques during the physical simulation. In addition, we introduce a spline function to dynamically interpolate smooth forces/torques in haptic display according to the update rate of physical simulation. In the experiments, we show the feasibility of the proposed method and compare its performance with other methods and algorithms. The result shows that the proposed method can provide smooth and accurate haptic force feedback at a high update rate for complex scenarios.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted