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Found 10 papers, 0 papers with code
Backup Plan Constrained Model Predictive Control with Guaranteed Stability
To include this new safety concept in control problems, we formulate a feasibility maximization problem aiming to maximize the feasibility of the primary and alternative missions.
Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles
We propose a multi-level stochastic optimization framework that monitors an attacker's capability of generating the adversarial perturbations.
Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data
Due to recent climate changes, we have seen more frequent and severe wildfires in the United States.
Path Integral Methods with Stochastic Control Barrier Functions
In this paper, we utilize Stochastic Control Barrier Functions (SCBFs) constraints to limit sample regions in the sample-based algorithm, ensuring safety in a probabilistic sense and improving sample efficiency with a stochastic differential equation.
Learning Wildfire Model from Incomplete State Observations
As wildfires are expected to become more frequent and severe, improved prediction models are vital to mitigating risk and allocating resources.
Time Coordination of Multiple UAVs over Switching Communication Networks with Digraph Topologies
This paper presents a time-coordination algorithm for multiple UAVs executing cooperative missions.
Learning Image Attacks toward Vision Guided Autonomous Vehicles
While adversarial neural networks have been shown successful for static image attacks, very few approaches have been developed for attacking online image streams while taking into account the underlying physical dynamics of autonomous vehicles, their mission, and environment.
High Accuracy Tumor Diagnoses and Benchmarking of Hematoxylin and Eosin Stained Prostate Core Biopsy Images Generated by Explainable Deep Neural Networks
Our neural network framework thus is automated, explainable and performs high precision H&E staining and destaining of low cost native RGB images, and is computer vision and physician authenticated for rapid and accurate tumor diagnoses.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets.
