Title:
Risky Robotics: Developing a Practical Solution for Stochastic Optimal Control
Risky Robotics: Developing a Practical Solution for Stochastic Optimal Control
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Author(s)
Rogers, Jonathan
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Abstract
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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Date Issued
2015-04-15
Extent
58:08 minutes
Resource Type
Moving Image
Resource Subtype
Lecture