We addressed limitations in Section7.

With the continual adoption of Uncrewed Aerial Vehicles (UAVs) across a wide-variety of application spaces, robust aerial manipulation remains a key research challenge. Aerial manipulation tasks require interacting with objects in the environment, often without knowing their dynamical properties like mass and friction a priori. Additionally, interacting with these objects can have a significant impact on the control and stability of the vehicle. We investigated an approach for robust control and non-prehensile aerial manipulation in unknown environments. In particular, we use model-based Deep Reinforcement Learning (DRL) to learn a world model of the environment while simultaneously learning a policy for interaction with the environment. We evaluated our approach on a series of push tasks by moving an object between goal locations and demonstrated repeatable behaviors across a range of friction values.

Connected vehicle technology is foreseen to play an important role in reducing the number of traffic accidents while being one of the main enabling components for autonomous driving. One of the application of such connection is to provide accurate information about the road condition such as friction level to drivers or the intelligent systems controlling the car. Road surface friction can be defined as the grip between car tyre and underlying surface. During winter times when the temperature decreases dramatically, friction level reduces substantially, which can increase the risk of car accidents. Studies indicate that road conditions such as surface temperature, type of road, and structure of the road sides play an important role in the measured friction level, and some of these conditions can vary significantly within short distances under specific weather situations. Road friction prediction based on the past sensor measurements available in the cars, e.g., temperature and sun light, has advantages of being independent of the road structure and surrounding infrastructure. Intelligent forecast systems rely on the availability of high quality data in order to allow their multiple actors to make correct decisions in diverse traffic situations. These systems have the potential to increase the safety of roads users by means of the timely sharing of road-related information. With the advances in car-to-car communication technology, today, Volvo cars are equipped with slippery road condition warning system to improve road safety and traffic flow.