A deep neural network solution towards mobile robot perception and exploration

2016 ◽  
Author(s):  
Shaohua Li
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Deep Neural Network ◽  
Network Solution ◽  
Robot Perception

scholarly journals Deep-neural-network solution of the electronic Schrödinger equation

2020 ◽  
Vol 12 (10) ◽  
pp. 891-897 ◽  
Author(s):  
Jan Hermann ◽  
Zeno Schätzle ◽  
Frank Noé
Keyword(s):  
Neural Network ◽  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
Deep Neural Network ◽  
Network Solution

scholarly journals Path Planning for Autonomous Driving of Mobile Robots using Deep Neural Network based Model Predictive Control

2021 ◽  
Vol 11 (11) ◽  
pp. 31-38
Author(s):  
Kiwon Yeom ◽  
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Deep Neural Network ◽  
Optimal Solution ◽  
Autonomous Driving ◽  
Physical Constraints ◽  
Predictive Controller

A car-like mobile robot is a nonlinear affine system, and the mobile robot has physical constraints such as velocity and acceleration. Thus, no satisfactory solution may not be provided during self-driving under unknown environments. Although Model Predictive Control (MPC) has provided good performance in terms of control strategy, it is difficult to optimize the control parameters due to the uncertainty and non-linearity of a process. In this paper, the Deep Neural Networks (DNN) based Model Predictive Controller (MPC) is derived for tracking the given path during self-driving. The proposed DNN MPC produces the global optimal solution which has better performance than traditional MPC in terms of the errors of position and orientation. This paper verifies that the proposed DNN MPC based controller can track the desired path with high precision for the car-like mobile robot. Keywords—Path planning, autonomous driving, mobile robot, deep neural network, model predictive control.

scholarly journals A New Landmark Detection Approach for Slam Algorithm Applied in Mobile Robot

2020 ◽  
Vol 30 (7) ◽  
pp. 31-36
Author(s):  
Xuan-Ha Nguyen ◽  
Van-Huy Nguyen ◽  
Thanh-Tung Ngo
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Deep Neural Network ◽  
Research Effort ◽  
Measurement Data ◽  
Neural Network Models ◽  
Landmark Detection ◽  
Camera System ◽  
Detection Approach ◽  
Slam Algorithm

Simultaneous Localization and Mapping is a key technique for mobile robot applications and has received much research effort over the last three decades. A precondition for a robust and life-long landmark-based SLAM algorithm is the stable and reliable landmark detector. However, traditional methods are based on laserbased data which are believed very unstable, especially in dynamic-changing environments. In this work, we introduce a new landmark detection approach using vision-based data. Based on this approach, we exploit a deep neural network for processing images from a stereo camera system installed on mobile robots. Two deep neural network models named YOLOv3 and PSMNet were re-trained and used to perform the landmark detection and landmark localization, respectively. The landmark’s information is associated with the landmark data through tracking and filtering algorithm. The obtained results show that our method can detect and localize landmarks with high stability and accuracy, which are validated by laser-based measurement data. This approach has opened a new research direction toward a robust and life-long SLAM algorithm.

scholarly journals Speech De-identification with Deep Neural Networks

2021 ◽  
Vol 25 (2) ◽  
pp. 257-269
Author(s):  
Ádám Fodor ◽  
László Kopácsi ◽  
Zoltán Ádám Milacski ◽  
András Lőrincz
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Personal Characteristics ◽  
The Internet ◽  
Text To Speech ◽  
Network Solution ◽  
Human Speech ◽  
Human Participants ◽  
The Voice

Cloud-based speech services are powerful practical tools but the privacy of the speakers raises important legal concerns when exposed to the Internet. We propose a deep neural network solution that removes personal characteristics from human speech by converting it to the voice of a Text-to-Speech (TTS) system before sending the utterance to the cloud. The network learns to transcode sequences of vocoder parameters, delta and delta-delta features of human speech to those of the TTS engine. We evaluated several TTS systems, vocoders and audio alignment techniques. We measured the performance of our method by (i) comparing the result of speech recognition on the de-identified utterances with the original texts, (ii) computing the Mel-Cepstral Distortion of the aligned TTS and the transcoded sequences, and (iii) questioning human participants in A-not-B, 2AFC and 6AFC tasks. Our approach achieves the level required by diverse applications.

scholarly journals When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach

2018 ◽  
Author(s):  
Ding Liu ◽  
Bihan Wen ◽  
Xianming Liu ◽  
Zhangyang Wang ◽  
Thomas Huang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Denoising ◽  
Deep Neural Network ◽  
Mutual Influence ◽  
State Of The Art ◽  
Back Propagation ◽  
Learning Approach ◽  
Network Solution ◽  
High Level

Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning.

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