Alex Ellery

Terrain Response Estimation Using an Instrumented Rocker-Bogie Mobility System

This paper presents a procedure to model the drawbar pull and resistive torque of an unknown terrain as a function of normal load and slip using on-board rover instruments. Kapvik , which is a planetary micro-rover prototype with a rocker-bogie mobility system, is simulated in two dimensions. A suite of sensors is used to take relevant measurements; in addition to typical rover measurements, forces above the wheel hubs and rover forward velocity are sensed. An estimator determines the drawbar pull, resistive torque, normal load, and slip of the rover. The collected data are used to create a polynomial fit model that closely resembles the real terrain response.

Efficient Control of an AUV-Manipulator System: An Application for the Exploration of Europa

Autonomous control of a robotic manipulator mounted on a submersible autonomous underwater vehicle (AUV) is simulated with various strategies employing combinations of feedback and feedforward control. Feedforward compensation of the manipulator motion is accomplished using a model of the system kinematics and dynamics. Hydrodynamic effects including drag, buoyancy, and added mass, as well as the reaction of the vehicle, are all compensated. Effective manipulator position control is accomplished through stabilization of the vehicle orientation and system barycenter. Stabilization of the vehicle position using feedback and/or feedforward control is also considered for comparison. Compensation of the hydrodynamic effects while stabilizing the vehicle orientation and allowing vehicle translation resulted in a significant reduction in power consumption. Although experimental verification of the results is required, the improvement in efficiency may be beneficial for submersible vehicles operating in extremely remote conditions or extraterrestrial environments such as the oceans of Jupiters moon, Europa.

Autonomous rock classification using Bayesian image analysis for Rover-based planetary exploration

Abstract A robust classification system is proposed to support autonomous geological mapping of rocky outcrops using grayscale digital images acquired by a planetary exploration rover. The classifier uses 13 Haralick textural parameters to describe the surface of rock samples, automatically catalogues this information into a 5-bin data structure, computes Bayesian probabilities, and outputs an identification. The system has been demonstrated using a library of 30 digital images of igneous, sedimentary and metamorphic rocks. The images are 3.5×3.5xa0cm 2 in size and composed of 256×256 pixels with 256 grayscale levels. They are first converted to gray level co-occurrence matrices which quantify the number of times adjacent pixels of similar intensity are present. The Haralick parameters are computed from these matrices. When all 13 parameters are used, classification accuracy, defined using an empirical scoring system, is 65% due to a large number of false positives. When the number of parameters and the choice of parameter is optimized, classification accuracy increases to 80%. The best results were achieved with 3 parameters that can be interpreted visually (angular second moment, contrast, correlation) together with two statistical parameters (sum of squares variance and difference variance) and a parameter derived from information theory (information measure of correlation II). The system has been kept simple not to draw excessive computational power from the rover. It could, however, be easily extended to handle additional parameters such as images acquired at different wavelengths.

Rover-Based Autonomous Science by Probabilistic Identification and Evaluation

Autonomous science augments the capabilities of planetary rovers by shifting the identification and selection of science targets from remote operators to the rover itself. This shift frees the rover from wasteful idle time and allows for more selective data collection. This paper presents an approach to autonomous science that is comprised of three components: a Bayesian network that uses image data to identify features; an evaluation algorithm that selects the best features; and, a path-planning algorithm that guides the rover to the most scientifically valuable features. Within this framework, the effectiveness of pairing a larger prime rover with a smaller scout rover to improve autonomous science is investigated. Laboratory-based experiments were used to validate the effectiveness of the Bayesian network for feature identification and the scoring algorithm that has been developed for feature evaluation. Simulations were used to compare the traditional use of a solo prime rover to that of also employing a scout. The results presented here indicate that the inclusion of a scout rover can allow the prime rover to avoid pitfalls or routes with low scientific value.

Characterization, modeling, and control of Ni-Ti shape memory alloy based on electrical resistance feedback

The use of shape memory alloy actuators has steadily increased within the fields of aerospace, robotics, and biomedical engineering due to their superior properties compared to other actuation systems. Position control of shape memory alloy actuators is difficult due to the highly non-linear behavior but has been well studied using numerous approaches. Electrical resistance can be used to estimate strain in shape memory alloy actuator wire due to a correlation between the two parameters. Previous models of this correlation are subject to one or more drawbacks such as being limited to a single applied load, not accounting for hysteresis effects, or applying only to a specific actuator size. This article presents a stress–strain–resistance model that accounts for varying applied load, major and minor hysteresis effects and is normalized in terms of actuator geometry. Results of simulation and a simple position control experiment are demonstrated, validating the performance of the model. Furthermore, a correlation between the model and an augmented version of the Liang and Rogers model is also presented.

Progress towards 3D-printed mechatronic systems

Most additive manufacturing (referred to as 3D printing henceforth) is applied to the creation of static structures. This severely restricts the scope of 3D printing techniques. To be sure, 3D printing can build structures in many different materials including plastics, metals and ceramics. This severely Nevertheless, monolithic structures are the rule of the day and the prospect of 3D-printed multi-material structures are still a research problem. In this paper, we look further to expanding the capabilities of 3D printing to manufacturing full mechatronic systems - specifically electric motor and their supporting electronics. We have taken preliminary steps towards this goal presented here - if fully successful, it will demonstrate that additive manufacturing constitutes a universal constructor in the von Neumann sense.

Rover vision—fundamentals

No single modality sensor can provide sufficient data to extract all the relevant features of the environment but vision is the most information-rich modality. It is the primary sensory modality for planetary rovers in providing distance observation for navigation and obstacle avoidance. Furthermore, it is the most information-rich form of sensory data—in autonomous rovers it is the primary means of generating maps of the locality representing distance information about the external world. Stereovision is required for the recognition of objects, the determination of their positions and orientations in space, and for visual servoing. Vision also provides the basis for scientific analysis by the science team and as the first step towards autonomous science. Natural environments such as those on planetary surfaces are unstructured making image processing more difficult. Furthermore, sensor data are always corrupted by noise and characterized by limited observability.

Notes on extraterrestrial applications of 3D-printing with regard to self-replicating machines

We suggest that 3D printing offers potential as a universal constructing machine that may be programmed to construct any configuration of robotic mechanism. The most difficult components of such an end-to-end machine are the actuators and the controllers. To that end, we present initial progress in attempting to develop 3D-printable electric motors and electronic circuitry. Our specific application is in a lunar environment to manufacture lunar resources into a robotic infrastructure with productive capacity.

Science-influenced mobile robot guidance using Bayesian Networks

The high cost of planetary rover missions limits risk-taking and therefore restricts scientific exploration. Also, limited autonomy requires time-consuming manual commands that must be issued to the rover from a great distance. This paper explores the combination of vision-based geological information inferred from a Bayesian Network (BN) with the guidance system of a micro-rover scout. Simulation is used to study the abilities of the developed algorithm.

MoonDust Lunar Dust Simulation and Mitigation

The feasibility of extended exploration and human presence on the Moon and Mars depends critically on dealing with various environmental factors, and especially on the effects of dust. One of the most restricting facets of lunar surface exploration, as experienced by the prior Apollo landed missions, is the fine lunar dust, its high adherence, and its restrictive friction-like action. Moreover, the lunar dust particle size distribution extends generally into the submicron range, where it could potentially have toxic effects on exposed astronauts through their respiratory system. MoonDust is a project being performed in collaboration with the Canadian Space Agency to study the effects of lunar dust on optics and mechanical elements, and to develop innovative nano-filtration solutions to extend their operational lifetime within a lunar and/or Mars environment. To assist this work, a small lunar environment simulation vacuum chamber is being developed at MPBC, to enable the study of lunar dust effects on optics elements and rotary mechanisms, at pressures brought down below 10 -5 Torr. The developed simulator includes an injection system for lunar dust simulants, an excimer UV laser-light source for vacuum UV (VUV), and various diagnostic ports for relevant optical and electrical measurements. The MoonDust innovative dust mitigation solution exploits key characteristics of the lunar dust while incorporating nano-filtration technologies based on carbon nanotubes (CNT) materials. The aim is to minimize the required consumables while providing high capacity and high efficiencies for the more dangerous submicron particles. This paper reports on the development of the lunar environmental chamber and the associated lunar dust simulator. Some of the preliminary trial experimental results for filters based on CNTs for optical devices and rotary mechanical joint protection are also presented.