Two Minds for One Vehicle: A Case Study in Deliberative and Reactive Navigation

• Main Author: Leedy, Brett Michael
• Other Authors: Mechanical Engineering
• Format: Others
• Published: Virginia Tech 2014
• Subjects: Grand Challenge; Reactive; Autonomous Navigation; Deliberative
• Online Access: http://hdl.handle.net/10919/31666; http://scholar.lib.vt.edu/theses/available/etd-04092006-215052/

There are two commonly accepted paradigms for organizing intelligence in robotic vehicles, namely reactive and deliberative. A third, a hybrid paradigm called integrated planning and execution, is considered a combination of the original two. Although these paradigms are well known to researchers, there are few published examples directly comparing their application and performance on similar vehicles operating in identical environments. Virginia Techâ s participation with two nearly identical vehicles in the DARPA Grand Challenge afforded a practical opportunity for such a case study. Both base vehicles were developed by modifying Club Car Pioneer XRT 1500 on-demand four wheel drive base platforms. Cliff was designed to use the reactive paradigm, while Rocky was designed to use the deliberative paradigm. Both vehicles were initially outfitted with sensor suites and computational capabilities commensurate with the paradigm being employed. The author of this thesis coordinated the activities of the two teams of undergraduate and graduate students who implemented the respective designs and software. Both vehicles proved capable of off-road navigation, including road following and obstacle avoidance in complex desert terrain. In the end, however, the reactive paradigm proved to be smoother and more reliable than the deliberative paradigm under the conditions of our testing. While both vehicles were extensively tested and compared using the competing paradigms, the team modified Rocky to use the more effective reactive paradigm for the Grand Challenge events. The deliberative case shows much promise for complex navigation, but added unnecessary complexity to desert road navigation. This case study, while necessarily limited in scope, may help to shed additional light on the tradeoffs and performance of competing approaches to machine intelligence. === Master of Science