The UGV power resource management project considers the energy used by Unmanned Ground Vehicles (UGVs) as they navigate their dynamic environment.
The concept is based on the efficient application of statistical analysis and prediction methods to UGV live sensor data and available context data from GIS and weather data systems. The synthesised data sets will be used to build descriptive models of the interaction between the vehicles and the terrain being traversed, and subsequently predictive models to describe future terrain/vehicle interactions and subsequent traffic ability, safe speeds, mobility and navigation risks.
The study aims for improvement of the current state of the art approaches to live terrain identification/characterisation by exploiting already existing (but typically unexploited) UGV sensor data, combined with recently available big-data sources such as meteorological and Geographical Information Systems (GIS) data.
The study discovered that typical basic UGV sensor arrays provide rich information that is currently ignored. By employing this raw data in a more rigorous analysis, a detailed impression of the interaction between the drive-train and the terrain being traversed can be ascertained.
When synthesised with information from other sensors as well as readily available priori information from big data sources, new knowledge is created that has the potential to describe in detail the current terrain and its condition, as well as the likely behaviour of yet to be traversed terrains.