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

[+] Author Affiliations
Gangadhar Rajashekaraiah, Hakki Erhan Sevil, Atilla Dogan

University of Texas at Arlington, Arlington, TX

Paper No. DSCC2017-5330, pp. V002T21A009; 10 pages
doi:10.1115/DSCC2017-5330
From:
  • ASME 2017 Dynamic Systems and Control Conference
  • 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
  • Tysons, Virginia, USA, October 11–13, 2017
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5828-8
  • Copyright © 2017 by ASME

abstract

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.

Copyright © 2017 by ASME
Topics: Vehicles

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