A novel InSAR based off-road positive and negative obstacle detection technique for unmanned ground vehicle

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.

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Obstacle Detection for Unmanned Ground Vehicle in Cross-Country Environment

Advances in Mechanical and Electronic Engineering - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-642-31507-7_87 ◽

2012 ◽

pp. 549-553

Author(s):

Yibing Zhao ◽

Jining Li ◽

Linhui Li ◽

Hai Wang

Keyword(s):

Obstacle Detection ◽

Unmanned Ground Vehicle ◽

Ground Vehicle ◽

Cross Country

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Obstacle detection for unmanned ground vehicle on uneven and dusty environment

10.1117/12.2176041 ◽

2015 ◽

Author(s):

Tok Son Choe ◽

Jin Bae Park ◽

Sang Hyun Joo ◽

Yong Woon Park

Keyword(s):

Obstacle Detection ◽

Unmanned Ground Vehicle ◽

Ground Vehicle

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

Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications ◽

10.1115/dscc2017-5330 ◽

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.

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