Autonomous Robots Lab
Autonomous Robots Lab
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J Karl Hedrick
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Distributed Bayesian Filter Using Measurement Dissemination for Multiple Unmanned Ground Vehicles With Dynamically Changing Interaction Topologies
Distributed Bayesian filters for multi-vehicle network by using Latest-In-and-Full-Out exchange protocol of measurements
Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach
Learning a deep neural net policy for end-to-end control of autonomous vehicles
Measurement dissemination-based distributed Bayesian filter using the latest-in-and-full-out exchange protocol for networked unmanned vehicles
Model predictive control-based target search and tracking using autonomous mobile robot with limited sensing domain
Cooperative search using human-UAV teams
Distributed target localization using a group of UGVs under dynamically changing interaction topologies
Generating plans that predict themselves
Goal Inference Improves Objective and Perceived Performance in Human-Robot Collaboration
Parallel interacting multiple model-based human motion prediction for motion planning of companion robots
Interacting multiple model-based human motion prediction for motion planning of companion robots
Model predictive control-based probabilistic search method for autonomous ground robot in a dynamic environment
A framework for autonomous vehicles with goal inference and task allocation capabilities to support peer collaboration with human agents
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