10 papers
Forward-Invariant Policy Classes for Safe Reinforcement Learning in Multicopter Control
Chieh Tsai, Muhammad Junayed Hasan Zahed, Jinzhi Shen +5
This paper proposes a reinforcement learning (RL) framework for safe gain scheduling based on forwardinvariance-induced action-space design. Under stated nominalmodel and inversion…
Distributed Continuous Aerial Surveillance by UAS Swarms Under Formal Mission Specifications
Hossein Rastgoftar
Persistent aerial surveillance using multi-unmanned aerial systems (UASs) requires decentralized coordination, continuous team reconfiguration, and provable mission correctness des…
Online Reinforcement Learning for Safe Gain Scheduling in Nonlinear Quadrotor Control
Muhammad Junayed Hasan Zahed, Chieh Tsai, Salim Hariri +1
This paper presents an online reinforcement-learning framework for safe gain scheduling of a nonlinear quadcopter controller. Rather than learning thrust and torque commands direct…
RACF: A Resilient Autonomous Car Framework with Object Distance Correction
Chieh Tsai, Hossein Rastgoftar, Salim Hariri
Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe operations resulting in human loss…
Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
Chieh Tsai, Muhammad Junayed Hasan Zahed, Salim Hariri +1
This paper proposes a safe reinforcement learning (RL) framework based on forward-invariance-induced action-space design. The control problem is cast as a Markov decision process,…
Deep Q-Learning-Based Gain Scheduling for Nonlinear Quadcopter Dynamics
Hossein Rastgoftar, Muhammad J. H. Zahed
This paper presents a deep Q-network (DQN)-based gain-scheduling framework for safety-critical quadcopter trajectory tracking. Instead of directly learning control inputs, the prop…
Safety-Critical Reinforcement Learning with Viability-Based Action Shielding for Hypersonic Longitudinal Flight
Hossein Rastgoftar
This paper presents a safety-critical reinforcement learning framework for nonlinear dynamical systems with continuous state and input spaces operating under explicit physical cons…
Finite-State Decentralized Policy-Based Control With Guaranteed Ground Coverage
Hossein Rastgoftar
We propose a finite-state, decentralized decision and control framework for multi-agent ground coverage. The approach decomposes the problem into two coupled components: (i) the st…
Deep Neural Network-Based Aerial Transport in the Presence of Cooperative and Uncooperative UAS
Muhammad Junayed Hasan Zahed, Hossein Rastgoftar
We present a resilient deep neural network (DNN) framework for decentralized transport and coverage using uncrewed aerial systems (UAS) operating in . The proposed DN…
A Physics-Informed Fixed Skyroad Model for Continuous UAS Traffic Management (C-UTM)
Muhammad Junayed Hasan Zahed, Hossein Rastgoftar
Unlike traditional multi-agent coordination frameworks, which assume a fixed number of agents, UAS traffic management (UTM) requires a platform that enables Uncrewed Aerial Systems…