RI: Small: Incremental Sampling-Based Algorithms and Stochastic Optimal Control on Random Graphs

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Autonomous and semi-autonomous vehicles and systems have become indispensable both for civil (fire-fighting, nuclear waste handling, law-enforcement, deep ocean exploration and drilling, weather forecasting, transportation) and military (guided missiles, spacecraft, unmanned drones) applications. Automation, when coupled with information technology, will continue to permeate our society at ever increasing levels. Autonomous systems, which, thus far, have been a crucial component in homeland security applications (e.g., border patrol, persistent monitoring, etc), are now seen as a key factor of empowering people in their daily lives across work, leisure, and domestic tasks. The next generation of autonomous systems will operate and interact with humans in the household or the office. The recent investment of information technology companies such as Amazon and Google in robotics technology is likely to accelerate the adoption of these new technologies by the general public. The safe and reliable operation of all these autonomous systems hinges crucially on their ability to reason and navigate about their environment. The theory and methodologies developed in this research will make it possible to run highly sophisticated algorithms inside the "brain" of these autonomous systems to enable optimal decision-making, thus increasing their reliability, predictability, performance and fail-safe operation. Self-driving vehicles, anthropomorphic robots, aerial drones, manufacturing automation systems, and precision surgical instruments among others, will all benefit from the results of this research.The proposed research tackles a fundamental problem in the area of motion planning and trajectory generation for robotic and intelligent autonomous systems. A serious bottleneck in solving such problems under limited resource constraints (e.g., computer memory, time) is their high dimensionality that precludes the naïve use of discretizing the (continuous) state space. In this research it is proposed to develop new incremental, optimal sampling-based motion planning algorithms with improved convergence rates over existing methods, so as to enable close-to-real-time trajectory generation for autonomous vehicles operating in an uncertain and dynamically changing environment. To achieve this objective, this research will build on recent results and ideas from Rapidly-exploring Random Graphs (RRG), along with relaxation methods borrowed from the areas of Asynchronous Dynamic Programming (ADP) and Machine Learning (ML). Specifically, recent advances from machine learning can be used to address the three main issues hindering the broader applicability of probabilistic sampling based motion planners to a wider variety of problems: collision checking, efficient sampling, and local steering. One main tenet of the proposed research is the exploitation of the inherent parallelism of the proposed algorithms, which -- coupled with the recent advances in multi-core computer architectures and GPUs -- will enable real-time computations.