271 research outputs found
Urban Drone Navigation: Autoencoder Learning Fusion for Aerodynamics
Drones are vital for urban emergency search and rescue (SAR) due to the
challenges of navigating dynamic environments with obstacles like buildings and
wind. This paper presents a method that combines multi-objective reinforcement
learning (MORL) with a convolutional autoencoder to improve drone navigation in
urban SAR. The approach uses MORL to achieve multiple goals and the autoencoder
for cost-effective wind simulations. By utilizing imagery data of urban
layouts, the drone can autonomously make navigation decisions, optimize paths,
and counteract wind effects without traditional sensors. Tested on a New York
City model, this method enhances drone SAR operations in complex urban
settings.Comment: 47 page
Multi-Robot Systems: Challenges, Trends and Applications
This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics
Collaboratively Navigating Autonomous Systems
The objective of this project is to focus on technologies for enabling heterogeneous networks of autonomous vehicles to cooperate together on a specific task. The prototyped test bed consists of a retrofitted electric golf cart and a quadrotor designed to perform distributed information gathering to guide decision making across the entire test bed. The system prototype demonstrates several aspects of this technology and lays the groundwork for future projects in this area
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