Obstacle Identification for Vision Assisted Control Architecture of a Hybrid Mechanism Mobile Robot

2017 ◽  
Author(s):  
Anil Kumar ◽  
Hailin Ren ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Mobile Robot ◽  
Detection Algorithm ◽  
Monocular Vision ◽  
Autonomous Control ◽  
Control Architecture ◽  
Detection Algorithms ◽  
Hybrid Mechanism ◽  
Ultrasonic Ranging ◽  
On Line ◽  
Vision Algorithm

This paper presents a monocular vision-based, unsupervised floor detection algorithm for semi-autonomous control of a Hybrid Mechanism Mobile Robot (HMMR). The paper primarily focuses on combining monocular vision cues with inertial sensing and ultrasonic ranging for on-line obstacle identification and path planning in the event of limited wireless connectivity. A novel, unsupervised vision algorithm was developed for floor detection and identifying traversable areas, in order to avoid obstacles in semi-autonomous control architecture. The floor detection algorithms were validated and experimentally tested in an indoor environment under various lighting conditions.

scholarly journals THE SYSTEM OF VIDEO-DATA PROCESSING FOR THE AUTONOMOUS CONTROL OF MOBILE ROBOT

2014 ◽  
pp. 82-85
Author(s):  
Denis Vershok ◽  
Rauf Sadykhov ◽  
Andrei Selikhanovich ◽  
Klaus Schilling ◽  
Hubert Roth
Keyword(s):  
Data Processing ◽  
Mobile Robot ◽  
Detection Algorithm ◽  
Straight Line Segment ◽  
Video Data ◽  
Autonomous Control ◽  
Walsh Transform ◽  
Straight Line ◽  
Third Stage ◽  
The Given

This paper describes the system of video-data processing based on monocular vision for autonomous control of mobile robot. The system allows detecting obstacles in a robot environment modeled as a set of straight-line segment. The given system consists of three basic stages and uses original algorithms, ensuring the required precision and realization of the system in the real time. The first stage uses a fast edge detection algorithm on the basis of two- dimensional Walsh transform. The algorithm of modified Hough transform is used for detection of straight-line segments. The third stage «segment tracking» uses Kalman filtration for tracking segments in a monocular sequence of images.

Design and Control Architecture of a Small Mobile Robot with Hybrid Locomotion System

2014 ◽  
Vol 598 ◽  
pp. 619-622 ◽  
Author(s):  
Badrul Aisham ◽  
Qadir Bakhsh ◽  
Khalid Hasnan ◽  
Aftab Ahmed
Keyword(s):  
Mobile Robot ◽  
Control Architecture ◽  
Motion System ◽  
Locomotion System ◽  
Hybrid Mechanism ◽  
Track System ◽  
Low Energy Consumption ◽  
Loading And Unloading ◽  
Hybrid Locomotion ◽  
And Control

This paper presents design and control architecture of a small mobile robot with hybrid locomotion system. It consist the combination of wheel and track type motion system resulting as a hybrid mechanism. The wheel locomotion system helps the robot to move on flat surface or path platform with high velocity, high manoeuvrability at low energy consumption. The track system is designed for rough and unstructured path, where robot need to cross irregular surface with high stability. This system works on interchangeable locomotion phenomena by means of track tensioner unit (TTU). The TTU helps the loading and unloading of track system by means of rack and pinion mechanism. This design of small hybrid mobile robot is improves robot applications and also enhance its flexibilty, verstality to work in multi type of terrains.

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals A novel algorithm to detect non-wear time from raw accelerometer data using deep convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaheen Syed ◽  
Bente Morseth ◽  
Laila A. Hopstock ◽  
Alexander Horsch
Keyword(s):  
Threshold Value ◽  
Detection Algorithm ◽  
Classification Model ◽  
Type I ◽  
Accelerometer Data ◽  
Deep Convolutional Neural Networks ◽  
Major Drawback ◽  
Wear Time ◽  
Detection Algorithms ◽  
Type Ii Errors

AbstractTo date, non-wear detection algorithms commonly employ a 30, 60, or even 90 mins interval or window in which acceleration values need to be below a threshold value. A major drawback of such intervals is that they need to be long enough to prevent false positives (type I errors), while short enough to prevent false negatives (type II errors), which limits detecting both short and longer episodes of non-wear time. In this paper, we propose a novel non-wear detection algorithm that eliminates the need for an interval. Rather than inspecting acceleration within intervals, we explore acceleration right before and right after an episode of non-wear time. We trained a deep convolutional neural network that was able to infer non-wear time by detecting when the accelerometer was removed and when it was placed back on again. We evaluate our algorithm against several baseline and existing non-wear algorithms, and our algorithm achieves a perfect precision, a recall of 0.9962, and an F1 score of 0.9981, outperforming all evaluated algorithms. Although our algorithm was developed using patterns learned from a hip-worn accelerometer, we propose algorithmic steps that can easily be applied to a wrist-worn accelerometer and a retrained classification model.

scholarly journals Autonomous Control for an Omnidirectional Mobile Robot with the Orthogonal-Wheel Assembly.

1999 ◽  
Vol 17 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Jun Tang ◽  
Keigo Watanabe ◽  
Katsutoshi Kuribayashi ◽  
Yamato Shiraishi
Keyword(s):  
Mobile Robot ◽  
Autonomous Control ◽  
Omnidirectional Mobile Robot

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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