A situation awareness interface for a bi-wheeled industrial hovercraft: design, development and evaluation

The intention of this thesis is to develop a prototype interface that enables an operator to control a bi-wheeled industrial hovercraft that will work within a fusion power plant if the automation system fails. This fusion power plant is part of the ITER project a conjoint effort of various industrialized countries to develop cleaner sources of energy. The development of the interface prototype will be based on situation awareness concepts, which provide a means to understand how human operators perceive the world around, then process that information and make decisions based on the knowledge that they already have and the projected knowledge of the reactions that will occur in the world in response to the actions the operator makes. Two major situation awareness methods will be used, GDTA as a means to discover the requirements the interface needs to solve, and SAGAT to conduct the evaluation on the three interfaces. This technique can isolate the differences an operator has in situation awareness when presented with relevant information given by each of the three interfaces that were built for this thesis. Where the first interface presents the information within the operator’s focal point of view in a pictorial style, the second interface shows the same information within the same point of view has the first interface but only shows it in a textual manner. While the third interface shows the relevant information in the operator’s peripheral field of view. Also SAGAT can provide insight on the question to know if providing the operator with feed-forward information about the stoppage distances of the bi-wheeled industrial hovercraft has any effect on the operator’s decision making.Universidade da Madeir

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