Motion Planning Method for In-Pipe Walking Robots Using Height Maps and CNN-Based Pipe Branches Detector

2019 ◽  
pp. 1-24
Author(s):  
Sergei Savin
Keyword(s):  
Neural Networks ◽  
Motion Planning ◽  
Prediction Accuracy ◽  
Planning Method ◽  
Walking Robots ◽  
Walking Robot ◽  
Deep Convolutional Neural Networks ◽  
Sequence Generation ◽  
Simulation Based ◽  
Inner Surface

In this chapter, the problem of motion planning for an in-pipe walking robot is studied. One of the key parts of motion planning for a walking robot is a step sequence generation. In the case of in-pipe walking robots it requires choosing a series of feasible contact locations for each of the robot's legs, avoiding regions on the inner surface of the pipe where the robot cannot step to, such as pipe branches. The chapter provides an approach to localization of pipe branches, based on deep convolutional neural networks. This allows including the information about the branches into the so-called height map of the pipeline and plan the step sequences accordingly. The chapter shows that it is possible to achieve prediction accuracy better than 0.5 mm for a network trained on a simulation-based dataset.

Feasibility Study of Designing a Three Legged Walking Robot: Tribot

1991 ◽  
Author(s):  
Yueh-Jaw Lin ◽  
Aaron Tegland
Keyword(s):  
Feasibility Study ◽  
Legged Robots ◽  
Walking Robots ◽  
Design Parameters ◽  
Optimized Design ◽  
Motion Simulation ◽  
Walking Robot ◽  
Design Considerations ◽  
Simulation Based ◽  
Weight Distributions

Abstract In recent years, walking robot research has become an important robotic research topic because walking robots possess mobility, as oppose to stationary robots. However, current walking robot research has only concentrated on even numbered legged robots. Walking robots with odd numbered legs are still lack of attention. This paper presents the study on an odd numbered legged (three-legged) walking robot — Tribot. The feasibility of three-legged walking is first investigated using computer simulation based on a scaled down tribot model. The computer display of motion simulation shows that a walking robot with three legs is feasible with a periodic gait. During the course of the feasibility study, the general design of the three-legged robot is also analyzed for various weights, weight distributions, and link lengths. In addition, the optimized design parameters and limitations are found for certain knee arrangements. These design considerations and feasibility study using computer display can serve as a general guideline for designing odd numbered legged robots.

scholarly journals Implementation Method of the Robot Adaptation to Contact Interaction Mode Changes Using Deep Fully Connected Neural Networks

2020 ◽  
Vol 24 (1) ◽  
pp. 206-214
Author(s):  
S. I. Savin ◽  
L. Yu. Vorochaeva ◽  
A. V. Malchikov ◽  
A. M. Salikhzyanov ◽  
E. M. Zalyaev
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Contact Interaction ◽  
Training Sample ◽  
Bipedal Walking ◽  
Walking Robots ◽  
Support Surface ◽  
Walking Robot ◽  
Additional Advantage ◽  
Harmonic Components

Purpose of research. The present paper conserns the problem of using reaction predictors in the control system of bipedal walking robots. The main advantage of using predictors is the ability to exclude unknown reaction forces from the dynamics equations and, consequently, from the robot control problem statements based on the model. An additional advantage of predictor setting of control tasks is also discussed in the paper, namely the possibility of its use to predict changes in contact interaction modes, such as slipping motion or foot lifting from the supporting surface.Methods. The following methods are used in the research: the method of dynamics of multi-mass systems is necessary for developing a mathematical model of the behavior of a walking robot and describing its contact interaction with the support surface, the method of neural networks is used to develop a predictor that allows one to forecast the values of reactions between the robot’s foot and the surface.Results. The paper shows that there is a connection between the frequencies of the harmonic components of robot movements (the ratio p of these frequencies in the experiment and the training sample) and the quality of reactions predictor operation of the support surface. This indicates the importance of applying a representative spectrum of walking robot movement frequencies in forming a training sample, and the poor generalizability of the predictor in relation to movement frequency.Conclusion. The paper has considered the use of a reaction predictor to identify the possibility of changing the mode of contact interaction, based on the measurement of discrepancies between local linearizations for various discrete steps. The results obtained in this work will be used in the development of a motion control system for a bipedal walking robot, which allows the device to adapt to the parameters of the support surface on which the movement occurs.

scholarly journals Object Detection and Depth Estimation Approach Based on Deep Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4755
Author(s):  
Huai-Mu Wang ◽  
Huei-Yung Lin ◽  
Chin-Chen Chang
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Prediction Accuracy ◽  
Depth Estimation ◽  
Disparity Estimation ◽  
Depth Information ◽  
Deep Convolutional Neural Networks ◽  
Epipolar Constraint

In this paper, we present a real-time object detection and depth estimation approach based on deep convolutional neural networks (CNNs). We improve object detection through the incorporation of transfer connection blocks (TCBs), in particular, to detect small objects in real time. For depth estimation, we introduce binocular vision to the monocular-based disparity estimation network, and the epipolar constraint is used to improve prediction accuracy. Finally, we integrate the two-dimensional (2D) location of the detected object with the depth information to achieve real-time detection and depth estimation. The results demonstrate that the proposed approach achieves better results compared to conventional methods.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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