85 research outputs found
Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions
Barrier function-based inequality constraints are a means to enforce safety
specifications for control systems. When used in conjunction with a convex
optimization program, they provide a computationally efficient method to
enforce safety for the general class of control-affine systems. One of the main
assumptions when taking this approach is the a priori knowledge of the barrier
function itself, i.e., knowledge of the safe set. In the context of navigation
through unknown environments where the locally safe set evolves with time, such
knowledge does not exist. This manuscript focuses on the synthesis of a zeroing
barrier function characterizing the safe set based on safe and unsafe sample
measurements, e.g., from perception data in navigation applications. Prior work
formulated a supervised machine learning algorithm whose solution guaranteed
the construction of a zeroing barrier function with specific level-set
properties. However, it did not explore the geometry of the neural network
design used for the synthesis process. This manuscript describes the specific
geometry of the neural network used for zeroing barrier function synthesis, and
shows how the network provides the necessary representation for splitting the
state space into safe and unsafe regions.Comment: Accepted into American Control Conference (ACC) 202
Underwater locomotion from oscillatory shape deformations
This paper considers underwater propulsion that is generated by variations in body shape. We summarize and extend some of the emerging approaches for the uniform modeling and control of such underactuated systems. Two examples illustrate these ideas
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