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View article: SERN: Simulation-Enhanced Realistic Navigation for Multi-Agent Robotic Systems in Contested Environments
SERN: Simulation-Enhanced Realistic Navigation for Multi-Agent Robotic Systems in Contested Environments Open
The increasing deployment of autonomous systems in complex environments necessitates efficient communication and task completion among multiple agents. This paper presents SERN (Simulation-Enhanced Realistic Navigation), a novel framework …
View article: QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning
QuasiNav: Asymmetric Cost-Aware Navigation Planning with Constrained Quasimetric Reinforcement Learning Open
Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement lear…
View article: TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments Open
Autonomous robots exploring unknown areas face a significant challenge -- navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration…
View article: TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments
TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments Open
Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional explor…
View article: Online Learning Approach to Predict Value of Information
Online Learning Approach to Predict Value of Information Open
The modern battlefield environment presents commanders and analysts with an overwhelming amount of information.Only portions of this information are useful at any given moment, often requiring human intervention to parse out what is meanin…
View article: A Novel ROS2 QoS Policy-Enabled Synchronizing Middleware for Co-Simulation of Heterogeneous Multi-Robot Systems
A Novel ROS2 QoS Policy-Enabled Synchronizing Middleware for Co-Simulation of Heterogeneous Multi-Robot Systems Open
Recent Internet-of-Things (IoT) networks span across a multitude of stationary and robotic devices, namely unmanned ground vehicles, surface vessels, and aerial drones, to carry out mission-critical services such as search and rescue opera…
View article: HeteroSys: Heterogeneous and Collaborative Sensing in the Wild
HeteroSys: Heterogeneous and Collaborative Sensing in the Wild Open
Advances in Internet-of-Things, artificial intelligence, and ubiquitous computing technologies have contributed to building the next generation of context-aware heterogeneous systems with robust interoperability to control and monitor the …
View article: HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems
HeteroEdge: Addressing Asymmetry in Heterogeneous Collaborative Autonomous Systems Open
Gathering knowledge about surroundings and generating situational awareness for IoT devices is of utmost importance for systems developed for smart urban and uncontested environments. For example, a large-area surveillance system is typica…
View article: Reproducible and Portable Big Data Analytics in the Cloud
Reproducible and Portable Big Data Analytics in the Cloud Open
Cloud computing has become a major approach to enable reproducible computational experiments because of its support of on-demand hardware and software resource provisioning. Yet there are still two main difficulties in reproducing big data…
View article: Reproducible and Portable Big Data Analytics in the Cloud
Reproducible and Portable Big Data Analytics in the Cloud Open
Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud.…
View article: Top-K Ranking Deep Contextual Bandits for Information Selection Systems
Top-K Ranking Deep Contextual Bandits for Information Selection Systems Open
In today's technology environment, information is abundant, dynamic, and\nheterogeneous in nature. Automated filtering and prioritization of information\nis based on the distinction between whether the information adds substantial\nvalue t…
View article: Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection
Deep Upper Confidence Bound Algorithm for Contextual Bandit Ranking of Information Selection Open
Contextual multi-armed bandits (CMAB) have been widely used for learning to filter and prioritize information according to a user's interest. In this work, we analyze top-K ranking under the CMAB framework where the top-K arms are chosen i…