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Proceedings Paper

Improved obstacle avoidance and navigation for an autonomous ground vehicle
Author(s): Binod Giri; Hyunsu Cho; Benjamin C Williams; Hokchhay Tann; Bicky Shakya; Vishal Bharam; David J. Ahlgren
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Paper Abstract

This paper presents improvements made to the intelligence algorithms employed on Q, an autonomous ground vehicle, for the 2014 Intelligent Ground Vehicle Competition (IGVC). In 2012, the IGVC committee combined the formerly separate autonomous and navigation challenges into a single AUT-NAV challenge. In this new challenge, the vehicle is required to navigate through a grassy obstacle course and stay within the course boundaries (a lane of two white painted lines) that guide it toward a given GPS waypoint. Once the vehicle reaches this waypoint, it enters an open course where it is required to navigate to another GPS waypoint while avoiding obstacles. After reaching the final waypoint, the vehicle is required to traverse another obstacle course before completing the run. Q uses modular parallel software architecture in which image processing, navigation, and sensor control algorithms run concurrently. A tuned navigation algorithm allows Q to smoothly maneuver through obstacle fields. For the 2014 competition, most revisions occurred in the vision system, which detects white lines and informs the navigation component. Barrel obstacles of various colors presented a new challenge for image processing: the previous color plane extraction algorithm would not suffice. To overcome this difficulty, laser range sensor data were overlaid on visual data. Q also participates in the Joint Architecture for Unmanned Systems (JAUS) challenge at IGVC. For 2014, significant updates were implemented: the JAUS component accepted a greater variety of messages and showed better compliance to the JAUS technical standard. With these improvements, Q secured second place in the JAUS competition.

Paper Details

Date Published: 8 February 2015
PDF: 14 pages
Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 940608 (8 February 2015); doi: 10.1117/12.2083448
Show Author Affiliations
Binod Giri, Trinity College (United States)
Hyunsu Cho, Trinity College (United States)
Benjamin C Williams, Trinity College (United States)
Hokchhay Tann, Trinity College (United States)
Bicky Shakya, Trinity College (United States)
Vishal Bharam, Trinity College (United States)
David J. Ahlgren, Trinity College (United States)


Published in SPIE Proceedings Vol. 9406:
Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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