UNMANNED VEHICLE OBSTACLE DETECTION AND AVOIDANCE USING DANGER ZONE APPROACH

2013 ◽  
Vol 37 (3) ◽  
pp. 529-538 ◽  
Author(s):  
Ta-Chung Wang ◽  
Tz-Jian Lin
Keyword(s):  
Obstacle Avoidance ◽  
Relative Velocity ◽  
Unmanned Vehicles ◽  
Obstacle Detection ◽  
Servo Motor ◽  
Danger Zone ◽  
Unmanned Vehicle ◽  
Algebraic Sets ◽  
Time Period ◽  
Distance Data

This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment. The vehicle uses an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obtain the distance data, and the profile of the obstacle. In this avoidance algorithm we will use the danger zone concept to judge whether the obstacle will cause a possible collision. The danger zone concept surrounds the vehicle through the intersection of semi-algebraic sets. These semi-algebraic sets use the relative velocity of the obstacle to calculate the area in which obstacles will collide with the vehicle within a pre-specified time period. Combining the profile of the boundary of the obstacle with the danger zone concept, a method for determining the safe maneuvers to avoid collisions is also provided.

Unmanned Vehicle Obstacle Avoidance Using Single Sensor Scan Method and Danger Zone Estimation

2013 ◽  
Vol 284-287 ◽  
pp. 1976-1980
Author(s):  
Tz Jian Lin ◽  
Ta Chung Wang
Keyword(s):  
Obstacle Avoidance ◽  
Relative Velocity ◽  
Unmanned Vehicles ◽  
Ultrasonic Sensor ◽  
Servo Motor ◽  
Danger Zone ◽  
Unmanned Vehicle ◽  
Time Period ◽  
Single Sensor ◽  
Distance Data

This paper proposes an obstacle avoidance algorithm for unmanned vehicles in unknown environment by a single sensor. The scan system is composed of an ultrasonic sensor and a servo motor which rotates from 0 to 180 degrees to obtain the distance data, and the profile of the obstacle can be depicted by a histogram which we use to find out the boundary of the obstacle. In this avoidance algorithm we will use the danger zone concept to judge whether the obstacle will cause a possible collision. The danger zone concept surrounds the vehicle by a sphere and uses the relative velocity to calculate the area in which obstacles will collide with the vehicle within a pre-specified time period. Combining the profile of the boundary of the obstacle with the danger zone concept, we can determine the maneuvers to avoid collisions.

scholarly journals A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1069
Author(s):  
Shibbir Ahmed ◽  
Baijing Qiu ◽  
Fiaz Ahmad ◽  
Chun-Wei Kong ◽  
Huang Xin
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
State Of The Art ◽  
Obstacle Detection ◽  
Crop Monitoring ◽  
Data Intensive ◽  
Structural Details ◽  
Dynamic Operating ◽  
New Generation ◽  
The Internet Of Things

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

Research on Management System of Automatic Driver Decision-Making Knowledge Base for Unmanned Vehicle

2019 ◽  
Vol 33 (04) ◽  
pp. 1959013 ◽  
Author(s):  
Zhaoxia Zhang ◽  
Qing Jiang ◽  
Rujing Wang ◽  
Liangtu Song ◽  
Zhengyong Zhang ◽  
...  
Keyword(s):  
Decision Making ◽  
Knowledge Base ◽  
Management System ◽  
Autonomous Driving ◽  
Unmanned Vehicles ◽  
Knowledge Model ◽  
Unmanned Vehicle ◽  
Base Management System ◽  
Decision Making System ◽  
Base Management

The acquisition, presentation and management of autonomous driving decision-making knowledge of unmanned vehicles are the key and difficult issues in the autonomous driving decision-making system of unmanned vehicles. This paper presents a knowledge model, which includes problem description layer and problem-solving knowledge layer. The automatic driving decision knowledge base of unmanned vehicle is composed of a set of knowledge models. Knowledge model supports knowledge representation and reasoning. Based on the WEB visualization knowledge modeling tool and visualization knowledge service tool, we construct the decision-making knowledge base management system for autonomous driving of unmanned vehicles and then construct the autonomous driving decision-making system of unmanned vehicles. The reasoning example shows that the knowledge base management system can effectively improve the knowledge acquisition, representation and maintenance efficiency of autonomous driving decision-making system, which is of great significance in enhancing the intelligence level of autonomous driving decision-making system.

scholarly journals A Hands-Free Obstacle Detection and Avoidance System

2015 ◽  
Vol 5 (3) ◽  
pp. 801-804
Author(s):  
M. Abdul-Niby ◽  
M. Alameen ◽  
O. Irscheid ◽  
M. Baidoun ◽  
H. Mourtada
Keyword(s):  
Obstacle Avoidance ◽  
Visually Impaired ◽  
Low Cost ◽  
Cost Effective ◽  
Obstacle Detection ◽  
Text To Speech ◽  
Voice Signal ◽  
Visually Impaired People ◽  
Impaired People ◽  
The Voice

In this paper, we present a low cost hands-free detection and avoidance system designed to provide mobility assistance for visually impaired people. An ultrasonic sensor is attached to the jacket of the user and detects the obstacles in front. The information obtained is transferred to the user through audio messages and also by a vibration. The range of the detection is user-defined. A text-to-speech module is employed for the voice signal. The proposed obstacle avoidance device is cost effective, easy to use and easily upgraded.

scholarly journals Vehicles for Open-Pit Mining with Smart Scheduling System for Transportation Based on 5G

2021 ◽  
Vol 12 (5) ◽  
pp. 827-835
Author(s):  
Mohanad F Jwaid, Husam K Salih Juboori
Keyword(s):  
Artificial Intelligence ◽  
Unmanned Vehicles ◽  
Open Pit Mine ◽  
Open Pit ◽  
Open Pit Mining ◽  
Transport Costs ◽  
Time Data ◽  
Nsga Ii ◽  
Scheduling System ◽  
Unmanned Vehicle

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancements coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. In addition to four other models we are proposing a decomposition-based restricted genetic dominance (DBCDP-NSGA-II) algorithm, which retains viable and non-facilitating solutions in small areas in order to improve the convergence, distribution and diversity of traditional high-dimensional multi-objective fast-dominated genetic sorting Algorithms (NSGA-II).

ROBOT LINE FOLLOWER PENGANTAR SURAT MENGGUNAKAN METODE FUZZY LOGIC STUDI KASUS FAKULTAS TEKNIK UNIVERSITAS PANCASILA

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Andini Putri ◽  
Febri Maspiyanti
Keyword(s):  
Fuzzy Logic ◽  
Obstacle Detection ◽  
Administrative Staff ◽  
Servo Motor ◽  
Motor Speed ◽  
Delivery Delay ◽  
Straight Line ◽  
Fuzzy Logic Method ◽  
Logic Method

Mail delivery at the Faculty of Engineering Pancasila University (FTUP) has many complaints regarding the delay of mail delivery from the administrative officer to the recipient of the mail. Lack of administrative staff responsible for the delay of mail acceptance can certainly cause great disadventages to the recipient; hence this can be a big factor inhibiting the dissemination of information. As technology develops, many research on robot able to accommodate human’s work. One type of robot that has been developed is the Robot Line Follower. In order to reduce the mail delivery delay, in this research we built a Line Follower Robot using Fuzzy Logic method. Fuzzy logic applied to this robot as a determinant of Servo motor speed and distance determinant of mail delivery area. This research yields 100% accuracy for straight line, 79% accuracy for U-line, 53% accuracy for 90o turn, 94,75% accuracy for obstacle detection, 87,75% accuracy for alarm, and 92,75% accuracy for RFID card detection, and 82% accuracy for RFID Card with option menu. Keywords: Accuracy, Fuzzy logic, Line Follower, RFID, Robot

Research on Intelligent Wheelchair Obstacle Avoidance Based on AdaBoost

2013 ◽  
Vol 312 ◽  
pp. 685-689 ◽  
Author(s):  
Jing Chen ◽  
Jing Li Niu ◽  
Dong Hai Chen
Keyword(s):  
Obstacle Avoidance ◽  
Visual Information ◽  
Technology Development ◽  
Obstacle Detection ◽  
Mobile Robot Navigation ◽  
Adaboost Algorithm ◽  
Intelligent Wheelchair ◽  
Vision Sensors ◽  
Computer Image Processing ◽  
Fuzzy Neural

With the computer image processing and technology development, vision sensors in mobile robot navigation and obstacle recognition was paid more and more attention. In this paper Adaboost algorithm is used to identify obstacles of intelligent wheelchair in Visual c + +6.0 platforms. With the AdaBoost algorithm training strong classifier for obstacle detection, then use the classifier to detect the target obstacle. Fuzzy neural network is used to fusion sonar information and visual information of wheelchair make the obstacle avoidance path of the wheelchair to be more intelligent and optimization.

scholarly journals Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4082 ◽  
Author(s):  
Zhengjun Qiu ◽  
Nan Zhao ◽  
Lei Zhou ◽  
Mengcen Wang ◽  
Liangliang Yang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Processing Speed ◽  
Paddy Field ◽  
Autonomous Navigation ◽  
Vision System ◽  
Obstacle Detection ◽  
Paddy Fields ◽  
Future Research ◽  
Moving Obstacles ◽  
Agricultural Machines

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

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