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 ...

We present a methodology for the deployment of the maxsum algorithm, a well known decentralised algorithm for coordinating autonomous agents, for problems related to situational awareness. In these settings, unmanned autonomous vehicles are deployed to collect information about an unknown environment. Our methodology then helps identify the choices that need to be made to apply the algorithm to...

Research into reactive collision avoidance for unmanned aerial vehicles has been conducted on unmanned terrestrial and mini aerial vehicles utilising active Doppler radar obstacle detection sensors. Flight tests conducted by flying a mini UAV at an obstacle have confirmed that a simple reactive collision avoidance algorithm enables aerial vehicles to autonomously avoid obstacles. This builds up...

The focus of our research is to improve navigation capabilities of an offroad unmanned ground vehicle (UGV) by improving its perception range. We train an ortophoto classifier by using data gathered by UGV and then extrapolate the data into wider area by using aerial imagery from unmanned aerial vehicles (UAV). The current progress report analyses feasibility of using satellite images instead o...