IMPROVING MISSION SURVIVABILITY OF UGV USING POLYNOMIAL NON-LINEAR REGRESSION FOR POWER PREDICTION.

Accurate prediction of required energy is essential for efficient deployment of Unmanned Ground Vehicles (UGV). Typical UGV missions do not allow for the replenishment of power resources so mission survivability and overall mission success rate are reliant on accurate power prediction. The live prediction of resources required for a particular mission is therefore beneficial as it could allow for increased mission time and lessen the requirement of over provisioning power resources. Accurate power prediction has particular importance when considering critical missions where failure to complete atomic mission operations may lead to an unsafe situation. This paper presents a new approach to live energy prediction that considers the effects of weather conditions on off-road terrains based upon non-linear polynomial regression for prediction of mission energy consumption using live sensor data. The method is demonstrated and compared to existing methods using a simulation of a typical UGV mission. The mission simulation considers a UGV traversing a variety of terrain types in various weather conditions. The experimental results show a significant improvement in energy prediction compared to existing approaches and demonstrates the success of forward prediction using non-linear terrain/weather/energy consumption models. [ABSTRACT FROM AUTHOR]