Improving Planetary Rover Attitude Estimation via MEMS Sensor Characterization

Micro Electro-Mechanical Systems (MEMS) are currently being considered in the space sector due to its suitable level of performance for spacecrafts in terms of mechanical robustness with low power consumption, small mass and size, and significant advantage in system design and accommodation. However, there is still a lack of understanding regarding the performance and testing of these new sensors, especially in planetary robotics. This paper presents what is missing in the field: a complete methodology regarding the characterization and modeling of MEMS sensors with direct application. A reproducible and complete approach including all the intermediate steps, tools and laboratory equipment is described. The process of sensor error characterization and modeling through to the final integration in the sensor fusion scheme is explained with detail. Although the concept of fusion is relatively easy to comprehend, carefully characterizing and filtering sensor information is not an easy task and is essential for good performance. The strength of the approach has been verified with representative tests of novel high-grade MEMS inertia sensors and exemplary planetary rover platforms with promising results

State estimation technique for a planetary robotic rover

Given the long traverse times and severe environmental constraints on a planet like Mars, the only option feasible now is to observe and explore the planet through more sophisticated planetary rovers. To achieve increasingly ambitious mission objectives under such extreme conditions, the rovers must have autonomy. Increased autonomy, obviously, relies on the quality of estimates of rover's state i.e. its position and orientation relative to some starting frame of reference. This research presents a state estimation approach based on Extended Kalman Filter (EKF) to fuse distance from odometry and attitude from an Inertial Measurement Unit (IMU), thus mitigating the errors generated by the use of either system alone. To simulate a Sun-sensor based approach for absolute corrections, a magnetic compass was used to give absolute heading updates. The technique was implemented on MotherBot, a custom-designed skid steered rover. Experimental results validate the application of the presented estimation strategy. It showed an error range within 3% of the distance travelled as compared to about 8% error obtained from direct fusion

Adaptive Localization and Mapping for Planetary Rovers

Future rovers will be equipped with substantial onboard autonomy as space agencies and industry proceed with missions studies and technology development in preparation for the next planetary exploration missions. Simultaneous Localization and Mapping (SLAM) is a fundamental part of autonomous capabilities and has close connections to robot perception, planning and control. SLAM positively affects rover operations and mission success. The SLAM community has made great progress in the last decade by enabling real world solutions in terrestrial applications and is nowadays addressing important challenges in robust performance, scalability, high-level understanding, resources awareness and domain adaptation. In this thesis, an adaptive SLAM system is proposed in order to improve rover navigation performance and demand. This research presents a novel localization and mapping solution following a bottom-up approach. It starts with an Attitude and Heading Reference System (AHRS), continues with a 3D odometry dead reckoning solution and builds up to a full graph optimization scheme which uses visual odometry and takes into account rover traction performance, bringing scalability to modern SLAM solutions. A design procedure is presented in order to incorporate inertial sensors into the AHRS. The procedure follows three steps: error characterization, model derivation and filter design. A complete kinematics model of the rover locomotion subsystem is developed in order to improve the wheel odometry solution. Consequently, the parametric model predicts delta poses by solving a system of equations with weighed least squares. In addition, an odometry error model is learned using Gaussian processes (GPs) in order to predict non-systematic errors induced by poor traction of the rover with the terrain. The odometry error model complements the parametric solution by adding an estimation of the error. The gained information serves to adapt the localization and mapping solution to the current navigation demands (domain adaptation). The adaptivity strategy is designed to adjust the visual odometry computational load (active perception) and to influence the optimization back-end by including highly informative keyframes in the graph (adaptive information gain). Following this strategy, the solution is adapted to the navigation demands, providing an adaptive SLAM system driven by the navigation performance and conditions of the interaction with the terrain. The proposed methodology is experimentally verified on a representative planetary rover under realistic field test scenarios. This thesis introduces a modern SLAM system which adapts the estimated pose and map to the predicted error. The system maintains accuracy with fewer nodes, taking the best of both wheel and visual methods in a consistent graph-based smoothing approach

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

3D Path planning using a fuzzy logic navigational map for Planetary Surface Rovers

This work proposes an innovative app navigation path-planning problem exploration rovers by including terrain characteristics. The objective is to enhance the typical 2D arithmetical cost function by adding 3D information computed from the laser-scanned terrain such as terrain height, slopes, shadows, orientation and terrain roughness. This paper describes the algorithm developed by UPM and GMV and the tests made at the GMV outdoor test facilities using the Moon-Hound rover. This rover is a 50 Kg rover including a Sick laser mounted on a pan&tilt unit for generation of 3D Digital Elevation Models (DEM’s). Experimental results have shown the effectiveness of the proposed approach

Planetary rovers and data fusion

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

System Development of an Unmanned Ground Vehicle and Implementation of an Autonomous Navigation Module in a Mine Environment

There are numerous benefits to the insights gained from the exploration and exploitation of underground mines. There are also great risks and challenges involved, such as accidents that have claimed many lives. To avoid these accidents, inspections of the large mines were carried out by the miners, which is not always economically feasible and puts the safety of the inspectors at risk. Despite the progress in the development of robotic systems, autonomous navigation, localization and mapping algorithms, these environments remain particularly demanding for these systems. The successful implementation of the autonomous unmanned system will allow mine workers to autonomously determine the structural integrity of the roof and pillars through the generation of high-fidelity 3D maps. The generation of the maps will allow the miners to rapidly respond to any increasing hazards with proactive measures such as: sending workers to build/rebuild support structure to prevent accidents. The objective of this research is the development, implementation and testing of a robust unmanned ground vehicle (UGV) that will operate in mine environments for extended periods of time. To achieve this, a custom skid-steer four-wheeled UGV is designed to operate in these challenging underground mine environments. To autonomously navigate these environments, the UGV employs the use of a Light Detection and Ranging (LiDAR) and tactical grade inertial measurement unit (IMU) for the localization and mapping through a tightly-coupled LiDAR Inertial Odometry via Smoothing and Mapping framework (LIO-SAM). The autonomous navigation module was implemented based upon the Fast likelihood-based collision avoidance with an extension to human-guided navigation and a terrain traversability analysis framework. In order to successfully operate and generate high-fidelity 3D maps, the system was rigorously tested in different environments and terrain to verify its robustness. To assess the capabilities, several localization, mapping and autonomous navigation missions were carried out in a coal mine environment. These tests allowed for the verification and tuning of the system to be able to successfully autonomously navigate and generate high-fidelity maps

NASA Tech Briefs, December 2012

The topics include: Pattern Generator for Bench Test of Digital Boards; 670-GHz Down- and Up-Converting HEMT-Based Mixers; Lidar Electro-Optic Beam Switch with a Liquid Crystal Variable Retarder; Feedback Augmented Sub-Ranging (FASR) Quantizer; Real-Time Distributed Embedded Oscillator Operating Frequency Monitoring; Software Modules for the Proximity-1 Space Link Interleaved Time Synchronization (PITS) Protocol; Description and User Instructions for the Quaternion to Orbit v3 Software; AdapChem; Mars Relay Lander and Orbiter Overflight Profile Estimation; Extended Testability Analysis Tool; Interactive 3D Mars Visualization; Rapid Diagnostics of Onboard Sequences; MER Telemetry Processor; pyam: Python Implementation of YaM; Process for Patterning Indium for Bump Bonding; Archway for Radiation and Micrometeorite Occurrence Resistance; 4D Light Field Imaging System Using Programmable Aperture; Device and Container for Reheating and Sterilization; Radio Frequency Plasma Discharge Lamps for Use as Stable Calibration Light Sources; Membrane Shell Reflector Segment Antenna; High-Speed Transport of Fluid Drops and Solid Particles via Surface Acoustic Waves; Compact Autonomous Hemispheric Vision System; A Distributive, Non-Destructive, Real-Time Approach to Snowpack Monitoring; Wideband Single-Crystal Transducer for Bone Characterization; Numerical Simulation of Rocket Exhaust Interaction With Lunar Soil; Motion Imagery and Robotics Application (MIRA): Standards-Based Robotics; Particle Filtering for Model-Based Anomaly Detection in Sensor Networks; Ka-band Digitally Beamformed Airborne Radar Using SweepSAR Technique; Composite With In Situ Plenums; Multi-Beam Approach for Accelerating Alignment and Calibration of HyspIRI-Like Imaging Spectrometers; JWST Lifting System; Next-Generation Tumbleweed Rover; Pneumatic System for Concentration of Micrometer-Size Lunar Soil

Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

A novel wireless low-cost inclinometer made from combining the measurements of multiple MEMS gyroscopes and accelerometers

Structural damage detection using inclinometers is getting wide attention from researchers. However, the high price of inclinometers limits this system to unique structures with a relatively high structural health monitoring (SHM) budget. This paper presents a novel low-cost inclinometer, the low-cost adaptable reliable angle-meter (LARA), which combines five gyroscopes and five accelerometers to measure inclination. LARA incorporates Internet of Things (IoT)-based microcontroller technology enabling wireless data streaming and free commercial software for data acquisition. This paper investigates the accuracy, resolution, Allan variance and standard deviation of LARA produced with a different number of combined circuits, including an accelerometer and a gyroscope. To validate the accuracy and resolution of the developed device, its results are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC) in laboratory conditions. The results of a load test experiment on a simple beam model show the high accuracy of LARA (0.003 degrees). The affordability and high accuracy of LARA make it applicable for structural damage detection on bridges using inclinometers.The authors are indebted to the Spanish Ministry of Economy and Competitiveness for the funding provided through the research project BIA2017-86811-C2-1-R directed by José Turmo and BIA2017-86811-C2-2-R. All these projects are funded with FEDER funds. The authors are also indebted to the Secretaria d’ Universitats i Recerca de la Generalitat de Catalunya, Catalunya, Spain for the funding provided through Agaur (2017 SGR 1482). It is also to be noted that funding for this research has been provided for Seyedmilad Komarizadehasl by Spanish Agencia Estatal de Investigación del Ministerio de Ciencia Innovación y Universidades grant and the Fondo Social Europeo grant (PRE2018-083238).Peer ReviewedPostprint (published version