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SEQUENCE:1
X-APPLE-TRAVEL-ADVISORY-BEHAVIOR:AUTOMATIC
UID:237066
DTSTAMP:20260602T095215Z
DTSTART;TZID=America/New_York:20260623T130000
DTEND;TZID=America/New_York:20260623T140000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/robotics-engineering-
 phd-speaking-and-writing-qualifiers-presentation-rohan-walia
SUMMARY:Robotics Engineering PhD Speaking and Writing Qualifiers Presentati
 on - Rohan Walia
DESCRIPTION:Belief Space Control of Safety-Critical Systems Under State-Dep
 endent Measurement Noise\n\n\n\n      \n      \n\n\n\nAbstract: Safety-cri
 tical control is imperative for deploying autonomous systems in the real w
 orld. Control Barrier Functions (CBFs) offer strong safety guarantees when
  accurate system and sensor models are available. However, widely used add
 itive, fixed-noise models are not representative of complex sensor modalit
 ies with state-dependent error characteristics. Although CBFs have been de
 signed to mitigate uncertainty using fixed worst-case bounds on measuremen
 t noise, this approach can lead to overly-conservative control. To solve t
 his problem, we extend the Belief Control Barrier Function (BCBF) framewor
 k to accommodate state-dependent measurement noise via the Generalized Ext
 ended Kalman Filter (GEKF) algorithm, which models measurement noise as a 
 linear function of the state. Using the original BCBF framework as baselin
 e, we demonstrate the performance of the BCBF-GEKF approach through simula
 tion results on a 1D single integrator setpoint regulation scenario and a 
 trajectory tracking scenario using 2D unicycle kinematics. Our results con
 firm that the BCBF-GEKF approach offers less conservative control with gre
 ater safety.\nAdvisor: Professor Kevin LeahyCommittee: Professor Constanti
 nos Chamzas, Professor Guanrui Li\nZoom Link: https://wpi.zoom.us/j/656728
 1852\n
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