1 citations · 2 across the 13 of their papers we have counts for
8 papers · 1 filter
Input Bias Correction in Gaussian Process Regression
Muzaffar Qureshi, Tochukwu E. Ogri, Trivikram Satharasi +2
In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped u…
Decentralized Scalar Field Mapping using Gaussian Process
Hossein Papi, Muzaffar Qureshi, Kyle Volle +2
Decentralized Gaussian process (GP) methods provide a scalable framework for multi-agent scalar-field estimation by allowing each agent to maintain a local GP over its assigned sub…
On sparsity and directional forgetting in adaptive control
Tochukwu E. Ogri, Trivikram Satharasi, Muzaffar Qureshi +2
This paper develops a sparsity-promoting memory regressor extension (MRE) adaptation law with directional forgetting for nonlinear control-affine systems with linearly parameterize…
A Hough transform approach to safety-aware scalar field mapping using Gaussian Processes
Muzaffar Qureshi, Trivikram Satharasi, Tochukwu E. Ogri +2
This paper presents a framework for mapping unknown scalar fields using a sensor-equipped autonomous robot operating in unsafe environments. The unsafe regions are defined as regio…
Adaptive Control with Sparse Identification of Nonlinear Dynamics
Trivikram Satharasi, Tochukwu E. Ogri, Muzaffar Qureshi +2
This paper develops a sparsity-promoting integral concurrent learning (SP-ICL) adaptation law for a linearly parametrized uncertain nonlinear control-affine systems. The unknown pa…
Decentralized Scalar Field Mapping using Gaussian Process
Hossein Papi, Muzaffar Qureshi, Kyle Volle +1
Decentralized Gaussian process (GP) methods offer a scalable framework for multi-agent scalar-field estimation by replacing a centralized global model with multiple local models ma…
A Taylor Series Approach to Correct Localization Errors in Robotic Field Mapping using Gaussian Processes
Muzaffar Qureshi, Tochukwu Elijah Ogri, Kyle Volle +1
Gaussian Processes (GPs) are powerful non-parametric Bayesian models for regression of scalar fields, formulated under the assumption that measurement locations are perfectly known…
Safe Adaptive Feedback Control via Barrier States
Trivikram Satharasi, Tochukwu E. Ogri, Muzaffar Qureshi +2
This paper presents a safe feedback control framework for nonlinear control-affine systems with parametric uncertainty by leveraging adaptive dynamic programming (ADP) with barrier…