Incorporating climate change in I&M planning for engineering structures - A Deep Reinforcement Learning Framework

In the last decades, climate change is causing our environment to change rapidly, unprecedented in recent history. Civil engineering structures are dependent on the deteriorating environment they are situated in. Changes can cause an increase in loading due to, for example, extreme weather events or alter the structure’s resistance by, for...

Autonomous conflict resolution for high-density urban operations using deep reinforcement learning

Low-altitude, high-density air traffic is expected to grow in the coming decades with several companies being certified to initiate urban operations for both freight and passenger transport. However, traditional human-centered Air Traffic Control operations (ATCos) are not scalable to handle the increased demand to maintain safe separation...

Spinning-off the AI: Defining the Value Proposition of an Artificial Intelligence solution

Artificial Intelligence technology offers computational, decision-making, and optimizing abilities that surpass every previously established traditional computation method. By being able to navigate across large amounts of data, the realized solutions learn on their own and provide results that would be unattainable with other ways. The complex...

Exploration and Coverage: A Deep Reinforcement Learning Approach

This work addresses the problem of exploration and coverage using visual inputs. Exploration and coverage is a fundamental problem in mobile robotics, the goal of which is to explore an unknown environment in order to gain vital information. Some of the diverse scenarios and applications in which exploratory robots can have a significant impact...