Improved obstacle avoidance and navigation for an autonomous ground vehicle

Binod Giri; Hyunsu Cho; Benjamin C Williams; Hokchhay Tann; Bicky Shakya; Vishal Bharam; David J. Ahlgren

doi:10.1117/12.2083448

8 February 2015 Improved obstacle avoidance and navigation for an autonomous ground vehicle

Binod Giri, Hyunsu Cho, Benjamin C Williams, Hokchhay Tann, Bicky Shakya, Vishal Bharam, David J. Ahlgren

Proceedings Volume 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques; 940608 (2015) https://doi.org/10.1117/12.2083448
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

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.

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Binod Giri, Hyunsu Cho, Benjamin C Williams, Hokchhay Tann, Bicky Shakya, Vishal Bharam, and David J. Ahlgren "Improved obstacle avoidance and navigation for an autonomous ground vehicle", Proc. SPIE 9406, Intelligent Robots and Computer Vision XXXII: Algorithms and Techniques, 940608 (8 February 2015); https://doi.org/10.1117/12.2083448

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KEYWORDS

Global Positioning System

Robots

Image processing

Sensors

Navigation systems

Cameras

Computer architecture

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