This research will investigate the problem of position estimation for planetary rovers. Diverse algorithmic filters are available for collecting input data and transforming that data to useful information for the purpose of position estimation process. The terrain has sandy soil which might cause slipping of the robot, and small stones and pebbles which can affect trajectory. The Kalman Filter, a state estimation algorithm was used for fusing the sensor data to improve the position measurement of the rover. For the rover application the locomotion and errors accumulated by the rover is compensated by the Kalman Filter. The movement of a rover in a rough terrain is challenging especially with limited sensors to tackle the problem. Thus, an initiative was taken to test drive the rover during the field trial and expose the mobile platform to hard ground and soft ground(sand). It was found that the LSV system produced speckle image and values which proved invaluable for further research and for the implementation of data fusion. During the field trial,It was also discovered that in a at hard surface the problem of the steering rover is minimal. However, when the rover was under the influence of soft sand the rover tended to drift away and struggled to navigate. This research introduced the laser speckle velocimetry as an alternative for odometric measurement. LSV data was gathered during the field trial to further simulate under MATLAB, which is a computational/mathematical programming software used for the simulation of the rover trajectory. The wheel encoders came with associated errors during the position measurement process. This was observed during the earlier field trials too. It was also discovered that the Laser Speckle Velocimetry measurement was able to measure accurately the position measurement but at the same time sensitivity of the optics produced noise which needed to be addressed as error problem. Though the rough terrain is found in Mars, this paper is applicable to a terrestrial robot on Earth. There are regions in Earth which have rough terrains and regions which are hard to measure with encoders. This is especially true concerning icy places like Antarctica, Greenland and others. The proposed implementation for the development of the locomotion system is to model a system for the position estimation through the use of simulation and collecting data using the LSV. Two simulations are performed, one is the differential drive of a two wheel robot and the second involves the fusion of the differential drive robot data and the LSV data collected from the rover testbed. The results have been positive. The expected contributions from the research work includes a design of a LSV system to aid the locomotion measurement system. Simulation results show the effect of different sensors and velocity of the robot. The kalman filter improves the position estimation process.

Wheel slip is a common problem in planetary rover exploration tasks. During the current Mars Exploration Rover (MER) mission, the Spirit rover almost became trapped on a dune because of wheel slip. As rover missions on harsh terrains expand in scope, mission success will depend not only on rover safety, but also alacrity in task completion. Speed combined with exploration of varied and difficult terrains, the risk of slip increases dramatically. We first characterize slip performance of a rover on harsh terrains by implementing a novel High Fidelity Traversability Analysis (HFTA) algorithm in order to provide slip prediction and detection capabilities to a planetary rover. The algorithm, utilizing path and energy cost functions in conjunction with simulated navigation, allows a rover to select the best path through any given terrain by predicting high slip paths. Integrated software allows the rover to then accurately follow a designated path while compensating for slippage, and reach intended goals independent of the terrain over which it is traversing. The algorithm was verified using ROAMS, a high fidelity simulation package, at 3.5x real time speed. We propose an adaptive path following algorithm as well as a human-trained neural network to traverse multiple harsh terrains using slip as an advantage. On a near-real-time system, and at rover speeds 15 times the current average speed of the Mars Exploration Rovers, we show that the adaptive algorithm traverses paths in less time than a standard path follower. We also train a standard back-propagation neural network, using human and path following data from a near-real-time system. The neural network demonstrates it ability to traverse new paths on multiple terrains and utilize slip to minimize time and path error.

京都大学 / 0048 / 新制・論文博士 / 博士(農学) / 乙第13294号 / 論農博第2877号 / 新制||農||1073(附属図書館) / 学位論文||R1||N5239(農学部図書室) / (主査)教授 清水 浩, 准教授 中嶋 洋, 教授 飯田 訓久 / 学位規則第4条第2項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

This thesis looks at the SherpaTT planetary rover. The rover is a hybrid walking and driving rover that has been developed and built by DFKI and has already been deployed on several Mars analogue field studies. The SherpaTT rover wheels were found to become entangled in rocks during the last field deployment in Morocco. As human intervention would be impossible on Mars the aim is to reduce the possibility of rock entanglements by performing a mechanical redesign of the wheels. During this redesign care is taken to ensure the current traction, slip-resistance, weight and strength are not adversely affected. In addition, the durability of the wheels is investigated in terms of materials to review whether the current wheels are suitable for a mars deployment. An investigation into the grousers design results in a changed design that aims to both reduce rock entanglements and increase wheel performance by optimising the grouser height and number over several different wheel and terrain cases. Wheels are produced for four scenarios, a rigid wheel on hard ground, a rigid wheel on soft ground, a flexible wheel on hard ground and a flexible wheel on soft ground. A conceptual investigation into the wheel fork design is carried out to examine the effects of changing three properties of the wheel fork. The magnitude and location of the stress is compared for each. Materials are investigated resulting in the recommendation of several potential material choices which provide an increase in the overall strength and hardness. While SherpaTT is still in development the 6000 class of aluminium is recommended due to the relative ease with which it can be worked with. Once SherpaTT moves onto the final stages it is recommended that at least the grousers are made from the 7000 class of aluminium, which have higher levels of strength and hardness.

Planetary rovers are robots that need to perform autonomous navigation, because of the long delay communication and no human assistance. Furthermore, they need to perform the optimal estimation of its position in order to have a good performance on its navigation system. The need for good performance filters for estimating the actual position of mobile robots of this kind is needed, due to the fact that sensors are noisy and that information is of vital importance for a planetary rover’s mission. Besides, good accurate sensors for the matter, are not easy to find for space application. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented to analyze a data set of a 4-wheel robot, and later used for comparison on accuracy in the estimation of its pose. The analysis will give the possibility to know the right combination of sensors, recognize some issues during the trajectory. Furthermore, this study has been made with aims to give the reader knowledge of state of the art in planetary rovers, their constraints and consideration while developing them. The robot used for the research has been developed for an international competition of field robot automation. The main goal is to navigate autonomously through flowerpots performing different tasks as flowerpot collection, distance traveled and robustness on localization and navigation algorithms. / <p>Validerat; 20120822 (anonymous)</p>