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Rogers, Jonathan

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    Risky Robotics: Developing a Practical Solution for Stochastic Optimal Control
    (Georgia Institute of Technology, 2015-04-15) Rogers, Jonathan
    Risk is a ubiquitous aspect of control and path planning for robots operating in unstructured real‐world environments. Nevertheless, humans still far surpass robots in their ability to evaluate complex tradeoffs under uncertainty through risk analysis and subsequent decision‐making. Many traditional approaches to the stochastic optimal control problem, such as Partially Observable Markov Decision Processes (POMDP’s), suffer from the curse of dimensionality and become computationally intractable in many real-world scenarios. In this seminar, a new class of stochastic control algorithms is proposed that makes use of emerging high‐performance computing devices, specifically GPUs, to perform real‐time uncertainty quantification (UQ) as part of a feedback control loop. These algorithms propagate the time‐varying probability density of the robot state and optimize control actions with respect to accuracy, obstacle avoidance, and other criteria. Key to practical implementation of these algorithms is the fact that many UQ algorithms can be parallelized; thus they can leverage emerging embedded high‐throughput devices for real‐time or near real‐time execution. Following an overview of the general formulation of these stochastic control algorithms, examples are provided in the form of autonomous parafoil and quadrotor flight controllers that make use of real‐time uncertainty analysis for obstacle avoidance in constrained environments. Recent experimental flight tests using embedded GPUs show that a strong coupling between UQ and optimal control offers a practical solution for risk mitigation by autonomous systems.
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    Applications of internal translating mass technologies to smart weapons systems
    (Georgia Institute of Technology, 2009-09-28) Rogers, Jonathan
    The field of guided projectile research has continually grown over the past several decades. Guided projectiles, typically encompassing bullets, mortars, and artillery shells, incorporate some sort of guidance and control mechanism to generate trajectory alterations. This serves to increase accuracy and decrease collateral damage. Control mechanisms for smart weapons must be able to withstand extreme acceleration loads at launch while remain simple for cost and reliability reasons. One type of control mechanism utilizes controllable internal translating masses (ITM's) that oscillate within the projectile to generate control forces. Several techniques for using internal translating masses for smart weapon flight control purposes are explored here. Specifically, the use of ITM's as direct control mechanisms, as a means to increase control authority, and as a means to protect the smart weapons sensor suite are examined. It is first shown that oscillating a mass orthogonal to the projectile axis of symmetry generates reasonable control force in statically-stable rounds. Trade studies examine the impact of mass size, mass offset from the center of gravity, and reductions in static stability on control authority. Then, the topic of static margin control through mass center modification is explored. This is accomplished by translating a mass in flight along the projectile axis of symmetry. Results show that this system allows for greater control authority and reduced throw-off error at launch. Another study, aimed at examining shock reduction potential at launch rather than static margin alteration, also considers ITM movement along the projectile centerline. In these studies, the ITM is comprised of sensitive electronic sensors, and is configured as a first-order damper during launch. Trade study results show that although the mechanism cannot substantially reduce the magnitude of launch loads, it is successful at dampening harmful structural vibrations typically experienced after muzzle exit. Finally, an active control system is developed for the ITM control mechanism using sliding mode methodology. Example cases and Monte Carlo simulations incorporating model uncertainties and sensor errors show that ITM control of projectiles can substantially reduce dispersion error. Furthermore, the novel sliding mode control law is shown to be highly robust to feedback disturbances. In a final study, combined ITM-canard control of projectiles is explored, concluding that ITM mechanisms can serve as a useful supplement in increasing the efficiency of currently-deployed control mechanisms.