scholarly journals A Neural Network Enhanced Stereo Vision Obstacle Detection and Avoidance System for Unmanned Ground Vehicle

2013 ◽  
Author(s):  
Fanjun Liu ◽  
Binggang Cao
Keyword(s):  
Neural Network ◽  
Stereo Vision ◽  
Obstacle Detection ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

scholarly journals Obstacle Detection in Hybrid Cross-Country Environment Based on Markov Random Field for Unmanned Ground Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Feng Ding ◽  
Yibing Zhao ◽  
Lie Guo ◽  
Mingheng Zhang ◽  
Linhui Li
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Line Segment ◽  
Obstacle Detection ◽  
Corner Detection ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Markov Random ◽  
Cross Country ◽  
Hybrid Cross

In order to detect the obstacle from the large amount of 3D LIDAR data in hybrid cross-country environment for unmanned ground vehicle, a new graph approach based on Markov random field was presented. Firstly, the preprocessing method based on the maximum blurred line is applied to segment the projection of every laser scan line inx-yplane. Then, based onK-means clustering algorithm, the same properties of the line are combined. Secondly, line segment nodes are precisely positioned by using corner detection method, and the next step is to take advantage of line segment nodes to build an undirected graph for Markov random field. Lastly, the energy function is calculated by means of analyzing line segment features and solved by graph cut. Two types of line mark are finally classified into two categories: ground and obstacle. Experiments prove the feasibility of the approach and show that it has better performance and runs in real time.

Evalaution and Control of Mirror Sliding Friction of Unmanned Ground Vehicle using Radial Basis Neural Network

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
Xiaohui Yang ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Sliding Friction ◽  
Rbf Neural Network ◽  
Simulation Method ◽  
Vehicle Speed ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Disturbance Term ◽  
And Control ◽  
Msf Model

In order to effectively analyse the mirror sliding friction(MSF) degree of unmanned ground vehicle(UGV) and improve its anti-disturbance performance, a simulation method for MSF degree of UGV based on RBF neural network is proposed. A single-input and double-output RBF neural network is adopted to estimate the uncertain dynamic parameters of the MSF model. The obtained parameters are used to describe the MSF control law based on RBF neural network. An adaptive law based on slow time-varying disturbance characteristics is designed to estimate the total friction disturbance term in the MSF model online. The simulation results show that the proposed method can analyse the MSF degree of unmanned ground vehicle at different speeds and gradients. The influence of gradient on the decline rate of friction degree is greater than that of vehicle speed. The mean error of friction disturbance term calculated by the method is only about 0.9% which has the advantage of low error of friction degree estimation when compared to conventional methods.

Obstacle detection for unmanned ground vehicle on uneven and dusty environment

2015 ◽  
Author(s):  
Tok Son Choe ◽  
Jin Bae Park ◽  
Sang Hyun Joo ◽  
Yong Woon Park
Keyword(s):  
Obstacle Detection ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle

PTEM Based Moving Obstacle Detection and Avoidance for an Unmanned Ground Vehicle

2017 ◽  
Author(s):  
Gangadhar Rajashekaraiah ◽  
Hakki Erhan Sevil ◽  
Atilla Dogan
Keyword(s):  
Obstacle Avoidance ◽  
Obstacle Detection ◽  
Mathematical Representation ◽  
Unmanned Ground Vehicle ◽  
Prior Work ◽  
Sensors And Actuators ◽  
Ground Vehicle ◽  
Simulink Simulation ◽  
Stationary Obstacles ◽  
Moving Obstacle

This study presents the development and implementation of an autonomous obstacle avoidance algorithm for an UGV (Unmanned Ground Vehicle). This research improves the prior work by enhancing the obstacle avoidance capability to handle moving obstacles as well as stationary obstacles. A mathematical representation of the area of operation with obstacles is formulated by PTEM (Probabilistic Threat Exposure Map). The PTEM quantifies the risk in being at a position in an area with different types of obstacles. A LRF (Laser Range Finder) sensor is mounted on the UGV for obstacle data in the area that is used to construct the PTEM. A guidance algorithm processes the PTEM and generates the speed and heading commands to steer the UGV to assigned waypoints while avoiding obstacles. The main contribution of this research is to improve the PTEM framework by updating it continuously as new LRF readings are obtained, on the contrary to the prior work with fixed PTEM. The improved PTEM construction algorithm is implemented in a MATLAB/Simulink simulation environment that includes models of the UGV, LRF, all the sensors and actuators needed for the control of the UGV. The performance of the algorithm is also demonstrated in real time experiments with an actual UGV system.

scholarly journals Kontrol Attitude Unmanned Ground Vehicle (UGV) menggunakan Backpropagation Neural Network

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ike Bayusari ◽  
Bhakti Yudho Suprapto
Keyword(s):  
Neural Network ◽  
Unmanned Ground Vehicle ◽  
Backpropagation Neural Network ◽  
Ground Vehicle

Unmaned Ground Vehicle (UGV) merupakan teknologi kendaraan darat tanpa awak yang berguna untuk mempermudah pekerjaan manusia dalam berbagai bidang seperti transportasi, aktivitas logistik industri, search and resque, pertahanan dan keamanan, juga beberapa bidang lainnya. Pengendalian attitude menjadi permasalahan karena membutuhkan ketelitian akibat adanya pengaruh kecepatan. Selain itu, bagaimana UGV tersebut mengikuti jalur yang ditentukan juga memerlukan pengendalian attitude yang optimal. Penelitian ini bertujuan untuk merancang dan menguji performa serta untuk mengatahui tingkat keberhasilan dan keakuratan pengendalian UGV menggunakan algoritma Backpropagaion Neural Network. Dari hasil pengujian didapatkan bahwa algoritma ini berhasil mengikuti data uji yang diberikan dengan nilai MSE yang kecil.

scholarly journals Obstacle Detection for Unmanned Ground Vehicle on Uneven Terrain

2016 ◽  
Vol 65 (2) ◽  
pp. 342-348 ◽  
Author(s):  
Tok Son Choe ◽  
Sang Hyun Joo ◽  
Yong Woon Park ◽  
Jin Bae Park
Keyword(s):  
Obstacle Detection ◽  
Unmanned Ground Vehicle ◽  
Ground Vehicle ◽  
Uneven Terrain
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