4 citations · 7 across the 22 of their papers we have counts for
7 papers · 1 filter
Synergistic Perception and Control Simplex for Verifiable Safe Vertical Landing
Ayoosh Bansal, Yang Zhao, James Zhu +6
Perception, Planning, and Control form the essential components of autonomy in advanced air mobility. This work advances the holistic integration of these components to enhance the…
Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles
Hyung-Jin Yoon, Hamidreza Jafarnejadsani, Petros Voulgaris
The deep neural network (DNN) models for object detection using camera images are widely adopted in autonomous vehicles. However, DNN models are shown to be susceptible to adversar…
Sampling Complexity of Path Integral Methods for Trajectory Optimization
Hyung-Jin Yoon, Chuyuan Tao, Hunmin Kim +2
The use of random sampling in decision-making and control has become popular with the ease of access to graphic processing units that can generate and calculate multiple random tra…
Learning Image Attacks toward Vision Guided Autonomous Vehicles
Hyung-Jin Yoon, Hamidreza Jafarnejadsani, Petros Voulgaris
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…
Estimation and Planning of Exploration Over Grid Map Using A Spatiotemporal Model with Incomplete State Observations
Hyung-Jin Yoon, Hunmin Kim, Kripash Shrestha +2
Path planning over spatiotemporal models can be applied to a variety of applications such as UAVs searching for spreading wildfire in mountains or network of balloons in time-varyi…
A Path Planning Framework for a Flying Robot in Close Proximity of Humans
Hyung-Jin Yoon, Christopher Widdowson, Thiago Marinho +2
We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation…
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
Hyung-Jin Yoon, Huaiyu Chen, Kehan Long +4
We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observat…