A real-time framework for kinodynamic planning with application to quadrotor obstacle avoidance
- This thesis presents a full-stack, real-time planning framework for kinodynamic robots that is enabled by a novel application of machine learning for reachability analysis. As products of this work, three contributions are discussed in detail in this thesis. The first contribution is the novel application of machine learning for rapid approximation of reachable sets for dynamical systems. The second contribution is the synthesis of machine learning, sampling-based motion planning, and optimal control into a cohesive planning framework that is built on an offline-online computation paradigm. The final contribution is the application of this planning framework on a quadrotor system to produce, arguably, one of the first demonstrations of fully-online kinodynamic motion planning. During physical experiments, the framework is shown to execute planning cycles at a rate 3 Hz to 5 Hz, a significant improvement over existing techniques. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory. An event-based replanning structure is implemented to handle the case of dynamic, even adversarial, obstacles. A locally reactive control layer, inspired by potential fields methods, is added to the framework to help minimizes replanning events and produce graceful avoidance maneuvers in the presence of high speed obstacles.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Allen, Ross E
|Stanford University, Department of Aeronautics and Astronautics.
|Pavone, Marco, 1980-
|Pavone, Marco, 1980-
|Statement of responsibility
|Ross E. Allen.
|Submitted to the Department of Aeronautics and Astronautics.
|Thesis (Ph.D.)--Stanford University, 2016.
- © 2016 by Ross Emerson Allen
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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