A simple real-time algorithm for safe navigation of a non-holonomic robot in complex unknown environments with moving obstacles

2014 ◽  
Author(s):  
Andrey V. Savkin ◽  
Chao Wang
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Safe Navigation ◽  
Moving Obstacles ◽  
Unknown Environments ◽  
Holonomic Robot

A method for real-time safe navigation in noisy environments

2013 ◽  
Author(s):  
Seyed Ali Salehi Neyshabouri ◽  
Mohammad Reza Niknezhad ◽  
Ehsan Kamali ◽  
Seyed Sadegh Mohseni Salehi Monfared
Keyword(s):  
Real Time ◽  
Noisy Environments ◽  
Safe Navigation

A Distributed Real-Time Algorithm for Preference-Based Agreement

2011 ◽  
Vol 44 (1) ◽  
pp. 8933-8938
Author(s):  
Daniel Zelazo ◽  
Mathias Bürger ◽  
Frank Allgöwer
Keyword(s):  
Real Time ◽  
Time Algorithm

scholarly journals Real-Time Algorithm for Versatile Displacement Sensors Based on Self-Mixing Interferometry

2016 ◽  
Vol 16 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Antonio Luna Arriaga ◽  
Francis Bony ◽  
Thierry Bosch
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Displacement Sensors

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

A real-time algorithm for fault identification in machining centres

2011 ◽  
Vol 44 (1) ◽  
pp. 5201-5206 ◽  
Author(s):  
L. Ferrarini ◽  
M. Allevi ◽  
A. Dedè
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Fault Identification

scholarly journals Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos

2017 ◽  
Author(s):  
Aniket Bera ◽  
Tanmay Randhavane ◽  
Dinesh Manocha
Keyword(s):  
Personality Traits ◽  
Real Time ◽  
Personality Trait ◽  
User Study ◽  
Movement Patterns ◽  
Time Algorithm ◽  
High Density ◽  
Motion Model ◽  
Classification Criterion ◽  
Trait Theory

We present a real-time algorithm to automatically classify the behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian and computes its motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics and use them for crowd prediction. Our learning scheme is general and we highlight its performance in identifying the personality of different pedestrians in low and high density crowd videos. We also evaluate the accuracy by comparing the results with a user study.

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