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 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.

scholarly journals Steering control based on model predictive control for obstacle avoidance of unmanned ground vehicle

2020 ◽  
Vol 53 (3-4) ◽  
pp. 501-518
Author(s):  
Chaofang Hu ◽  
Lingxue Zhao ◽  
Lei Cao ◽  
Patrick Tjan ◽  
Na Wang
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Obstacle Avoidance ◽  
Predictive Control ◽  
Path Tracking ◽  
Unmanned Ground Vehicle ◽  
Steering Control ◽  
Steering Angle ◽  
Ground Vehicle ◽  
Tracking Controller

In this paper, a strategy based on model predictive control consisting of path planning and path tracking is designed for obstacle avoidance steering control problem of the unmanned ground vehicle. The path planning controller can reconfigure a new obstacle avoidance reference path, where the constraint of the front-wheel-steering angle is transformed to formulate lateral acceleration constraint. The path tracking controller is designed to realize the accurate and fast following of the reconfigured path, and the control variable of tracking controller is steering angle. In this work, obstacles are divided into two categories: static and dynamic. When the decision-making system of the unmanned ground vehicle determines the existence of static obstacles, the obstacle avoidance path will be generated online by an optimal path reconfiguration based on direct collocation method. In the case of dynamic obstacles, receding horizon control is used for real-time path optimization. To decrease online computation burden and realize fast path tracking, the tracking controller is developed using the continuous-time model predictive control algorithm, where the extended state observer is combined to estimate the lumped disturbances for strengthening the robustness of the controller. Finally, simulations show the effectiveness of the proposed approach in comparison with nonlinear model predictive control, and the CarSim simulation is presented to further prove the feasibility of the proposed method.

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

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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