A novel method of sensing and classifying terrain for autonomous unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) play a vital role in preserving human life during hostile military operations and extend our reach by exploring extraterrestrial worlds during space missions. These systems generally have to operate in unstructured environments which contain dynamic variables and unpredictable obstacles, making the seemingly simple task of traversing from A-B extremely difficult. Terrain is one of the biggest obstacles within these environments as it could potentially cause a vehicle to become stuck and render it useless, therefore autonomous systems must possess the ability to directly sense terrain conditions. Current autonomous vehicles use look-ahead vision systems and passive laser scanners to navigate a safe path around obstacles; however these methods lack detail when considering terrain as they make predictions using estimations of the terrain’s appearance alone. This study establishes a more accurate method of measuring, classifying and monitoring terrain in real-time. A novel instrument for measuring direct terrain features at the wheel-terrain contact interface is presented in the form of the Force Sensing Wheel (FSW). Additionally a classification method using unique parameters of the wheel-terrain interaction is used to identify and monitor terrain conditions in real-time. The combination of both the FSW and real-time classification method facilitates better traversal decisions, creating a more Terrain Capable system

Methods for the improvement of power resource prediction and residual range estimation for offroad unmanned ground vehicles

Unmanned Ground Vehicles (UGVs) are becoming more widespread in their deployment. Advances in technology have improved not only their reliability but also their ability to perform complex tasks. UGVs are particularly attractive for operations that are considered unsuitable for human operatives. These include dangerous operations such as explosive ordnance disarmament, as well as situations where human access is limited including planetary exploration or search and rescue missions involving physically small spaces. As technology advances, UGVs are gaining increased capabilities and consummate increased complexity, allowing them to participate in increasingly wide range of scenarios. UGVs have limited power reserves that can restrict a UGV’s mission duration and also the range of capabilities that it can deploy. As UGVs tend towards increased capabilities and complexity, extra burden is placed on the already stretched power resources. Electric drives and an increasing array of processors, sensors and effectors, all need sufficient power to operate. Accurate prediction of mission power requirements is therefore of utmost importance, especially in safety critical scenarios where the UGV must complete an atomic task or risk the creation of an unsafe environment due to failure caused by depleted power. Live energy prediction for vehicles that traverse typical road surfaces is a wellresearched topic. However, this is not sufficient for modern UGVs as they are required to traverse a wide variety of terrains that may change considerably with prevailing environmental conditions. This thesis addresses the gap by presenting a novel approach to both off and on-line energy prediction that considers the effects of weather conditions on a wide variety of terrains. The prediction is based upon nonlinear polynomial regression using live sensor data to improve upon the accuracy provided by current methods. The new approach is evaluated and compared to existing algorithms using a custom ‘UGV mission power’ simulation tool. The tool allows the user to test the accuracy of various mission energy prediction algorithms over a specified mission routes that include a variety of terrains and prevailing weather conditions. A series of experiments that test and record the ‘real world’ power use of a typical small electric drive UGV are also performed. The tests are conducted for a variety of terrains and weather conditions and the empirical results are used to validate the results of the simulation tool. The new algorithm showed a significant improvement compared with current methods, which will allow for UGVs deployed in real world scenarios where they must contend with a variety of terrains and changeable weather conditions to make accurate energy use predictions. This enables more capabilities to be deployed with a known impact on remaining mission power requirement, more efficient mission durations through avoiding the need to maintain excessive estimated power reserves and increased safety through reduced risk of aborting atomic operations in safety critical scenarios. As supplementary contribution, this work created a power resource usage and prediction test bed UGV and resulting data-sets as well as a novel simulation tool for UGV mission energy prediction. The tool implements a UGV model with accurate power use characteristics, confirmed by an empirical test series. The tool can be used to test a wide variety of scenarios and power prediction algorithms and could be used for the development of further mission energy prediction technology or be used as a mission energy planning tool