6 papers
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 system. The unknown par…
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…
Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
Goutam Das, Michael Dorothy, Kyle Volle +1
Game theory provides the gold standard for analyzing adversarial engagements, offering strong optimality guarantees. However, these guarantees often become brittle when assumptions…
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…