This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle's overhead view to inform the ground vehicle's path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient path...

The aerial vehicle used in this research is a Miniature Aircraft Gas Xcell RC helicopter. It is outfitted with a sensor payload containing stereo vision cameras, GPS, and a digital compass. Geo-referenced images are gathered using the above sensors that will be used in this research to create a map of the operating region. The ground vehicle used in this research is an automated Suzuki Mini-Qua...

Chrono::Vehicle is a module of the open-source multi-physics simulation package Chrono, aimed at modeling, simulation, and visualization of wheeled and tracked ground vehicle multibody systems. Its software architecture and design was dictated by the desire to provide an expeditious and user-friendly mechanism for assembling complex vehicle models, while leveraging the underlying Chrono modelin...

The unmanned aerial vehicle (UAV) and the ground (UGV) can complete complex tasks through information sharing ensure mission execution capability of multiple carrier platforms. At same time, cooperative navigation use interaction between multi-platform sensors to improve relative positioning accuracy entire system. Aiming at problem deviation system model due gross errors in sensor measurement ...

When introducing commercial driving simulators a few years ago, the main interest was focused either on operating the vehicle or on the reactions of the driver in traffic and in emergency situations. A comprehensive modeling of the vehicle dynamics was not required for these tasks. However, there are driving simulator applications which make much higher demands on the modeling of the vehicle dy...