Integrated simulation environment for unmanned autonomous systems: towards a conceptual framework
Modelling and Simulation in Engineering
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The primary contribution of this research is the design, implementation, and field testing of an Adaptive Planning Framework (APF) that can address the problem of autonomous operation in a complex, unstructured environment. It encapsulates a new and unique approach to dynamic situation assessment, behavior management, and decision-making. This research also included a literature review and development of a Reference Implementation. The thesis behind this research is that a well-organized, three-stage process of (1) understanding the current situation, (2) understanding the suitability and viability of the available behaviors in light of that situation, and (3) providing the capability to autonomously make and execute behavior-related decisions, all in real-time, provides new levels of intelligence to autonomous ground vehicles (AGV). This research was performed using the resources of the UF Center for Intelligent Machines and Robotics. This environment provided the ability to collaboratively explore engineering alternatives, create experimental software, and test it in a real-world setting, ultimately leading to the creation of the Reference Implementation. All this was aimed at validating the thesis of the research and producing a more robust APF, operationally proven in a representative physical environment. The Adaptive Planning Framework has been shown to be both a viable method for representing and managing complex, situation-dependent behavior on an AGV and a valuable contribution to researchers tasked with developing and fielding such a vehicle. The viability of the architecture and design was demonstrated by the development and testing of the Reference Implementation. The value of the APF can be measured by the major role it is playing in the architecture and design of the AGV being fielded by Team Gator Nation for competing in the 2007 DARPA Urban Challenge. The Adaptive Planning Framework makes a significant contribution to advancing the state of the practice of intelligent systems in general and AGVs in particular. Its adoption by Team Gator Nation means that it will be improved and extended by future researchers. Presuming that occurs, this work will have been the catalyst of a new way of achieving more intelligent and more autonomous ground vehicles.**This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer.