scholarly journals Application of Path Planning and Image Processing for Rescue Robots

2021 ◽  
Vol 34 (x) ◽  
pp. 1
Author(s):  
Chuan-Yen Lu ◽  
Chia-Chia Kao ◽  
Yu-Hsiang Lu ◽  
Jih-Gau Juang
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Rescue Robots

scholarly journals Design and Implementation of Autonomous Quadcopter using SITL Simulator

2020 ◽  
pp. 1-16
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Service Delivery ◽  
Raspberry Pi ◽  
End User ◽  
Autonomous Flight ◽  
Python Script ◽  
Design And Implementation ◽  
Additional Task ◽  
Military Security

In recent years Quadcopter has been used in many applications such as military, security & surveillance, service delivery, disaster rescue and much more due to its flexibility of flying. In this paper, Quadcopter will be used for mail delivery between many locations that is received from the end user. The Quadcopter will execute an autonomous flight using the concept of companion PC. Raspberry PI 3 (RPI3) will control the Quadcopter by command the controller of the drone (Pixhawk) by using DroneKit-Python API to send MAVLink messages to the Ardupilot. This concept is useful to perform an additional task to the autopilot and provide such a smart capability like image processing and path planning which cannot be done by the flight controller alone. Basically, the idea has been stimulated and the code has been tested by using the SITL Simulator with MAVProxy under Ubuntu environment. The result of controlling the Quadcopter using Python script was excellent and give a motivation to implement the same script on a real Quadcopter. The implementation on real Quadcopter was perfect as it has given the same behavior as the SITL drone in the simulation.

An Optimal Image Processing Method for Simultaneous Detection of Weld Seam Position and Weld Gap in Robotic Arc Welding

2020 ◽  
pp. 1341-1357
Author(s):  
Amruta Rout ◽  
Deepak BBVL ◽  
Bibhuti Bhusan Biswal ◽  
Golak Bihari Mahanta ◽  
Bala Murali Gunji
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Edge Detection ◽  
Weld Seam ◽  
Industrial Robot ◽  
Simultaneous Detection ◽  
Seam Tracking ◽  
Robotic Welding ◽  
Labview Software ◽  
Edge Detection Method

For robot path planning the weld seam positions need to be known in advance as the industrial robot generally work in teach and playback mode. In this paper, a vision sensor has been utilized for automation of robotic welding path planning. A seam tracking algorithm has been proposed for a butt type of weld joint with varying weld gap for effective measurement of weld path positions and weld gap simultaneously. For this first an image acquisition algorithm technique has been proposed for capturing of image of weld seam in gray scale mode. Then in image processing at first one pattern matching algorithm for tracking of weld seam path is performed. Then different edge detection techniques have been applied to find the most efficient edge detection method for obtaining the characteristics of weld seam edge. Then best edge fitting method has been applied to fit the edges along the weld seam edge and the pixel values on the edges were measured. The weld gap and the midpoint between edges points are measured simultaneously by vision assistant toolbox in LabVIEW software background.

Grid Based Path Planning Using CNN & Artificial Potential Field Method

2013 ◽  
Vol 392 ◽  
pp. 830-836 ◽  
Author(s):  
Shamina Akter ◽  
Deok Jin Lee ◽  
Shin Taek Lim ◽  
Kil To Chong
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Potential Field ◽  
Planning Process ◽  
Vision System ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Field Function ◽  
Potential Value

This proposed path planning method combines cellular neural network (CNN) with artificial potential field approach. The fundamental operation based on CNN gray scale image processing and artificial potential is the additional approach for global path-planning. Every point of the environment has a potential value with respect to start and destination position. In the trajectory planning process, a minimum search of potential value of every surrounding neighbor points around a point is done and the neighbor point with the least minimum value is selected as the next location. This procedure is repeated until the goal point is reached. The advantage of using CNN based image processing with artificial potential field function in a vision system is its effectiveness in robot localization while the use of minimum potential value gives a simple yet efficient path planning method. Their feedback criterion is similar to a procedure in filtering the image and it frequently updates the information about obstacles and free path. The parallel processing properties of CNN makes the proposed method robust for real time application.

scholarly journals Path Planning for Two-Link Navigation in an Unknown Environment Using Webcam

2010 ◽  
Vol 166-167 ◽  
pp. 369-374
Author(s):  
Shahed Shojaeipour ◽  
Ali Khaki Sedigh ◽  
Ali Shojaeipour ◽  
Edmund Ng Giap Weng ◽  
Nooshin Hadavi
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Robot Navigation ◽  
Voronoi Diagrams ◽  
Target Point ◽  
Matlab Software ◽  
Unknown Environment ◽  
New Information ◽  
Route Information

In this article, we used image processing by a webcam connected on top of the arm robot. The robot navigation is in an unknown environment. Then start point and target point were determined for the robot, so the robot needs to have a program for path planning using Voronoi diagrams to find the path. After the possible path for moving the robot was found, the route information obtained was sent to the arm robot. The arm robot moves in the workspace and any time new information was processed via the webcam. The program was written using MATLAB software which at controls the robot’s movement the unknown environment.

Sign in / Sign up

Export Citation Format

Share Document