Eric L. Akers

Design, fabrication, and evaluation of a mobile robot for polar environments

This paper describes the design, fabrication, and evaluation of a mobile robot as part of a radar system for measuring ice characteristics in Greenland and Antarctica. The robot can survive the polar environments, navigate the terrains, is capable of carrying the necessary radar equipment while providing power, can tow an antenna, and can provide precise positioning for the bistatic synthetic aperture radar (SAR).

Automated Polar Ice Thickness Estimation From Radar Imagery

This paper focuses on automating the task of estimating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheets surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time. Two primary methods have been studied in this paper, namely, edge-based and active contour. Results are evaluated and presented using the metrics of time requirements and accuracy. Automated ice thickness estimation results from 2006 and 2007 Greenland field campaigns illustrate that the edge-based approach offers faster processing (seconds compared to minutes), but suffers from a lack of continuity and smoothness aspects that active contours provide. The active contour approach is more accurate when compared to ground truth selections provided by human experts, and has proven to be more robust to image artifacts. It is shown that both techniques offer advantages which could be integrated to yield a more effective system.

Mobile Robots for Polar Remote Sensing

Mobile robots are becoming more heavily used in environments where human involvement is limited, impossible, or dangerous. These robots perform some of the more dangerous and laborious human tasks on Earth and throughout the solar system, many times with greater efficiency and accuracy, saving both time and resources. As we explore further away from Earth, higher levels of autonomy are also becoming more desired in such applications, one of them being remote sensing. This chapter covers mobile robots that have been designed and built at the University of Kansas to facilitate seismic and radar remote sensing of ice sheets in polar regions. These robots have been developed for and deployed in unstructured, polar environments. System designs, components, deployment and data acquisition algorithms, and experimental results are discussed. In this chapter, future applications, such as an autonomous multi-robot seismic surveying surveying team, are simulated. Future planetary missions will hopefully incorporate similar robotic systems to conduct insitu experiments on other planets. Christopher M. Gifford Electrical Engineering and Computer Science Department, University of Kansas, Lawrence, KS 66045, e-mail: cgifford@eecs.ku.edu Eric L. Akers Mathematics and Computer Science Department, Elizabeth City State University, Elizabeth City, NC 27909, e-mail: elakers@mail.ecsu.edu Richard S. Stansbury Department of Computer and Software Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, e-mail: richard.stansbury@erau.edu Arvin Agah Electrical Engineering and Computer Science Department, University of Kansas, Lawrence, KS 66045, e-mail: agah@ku.edu

Mobile Robots for Harsh Environments: Lessons Learned from Field Experiments

Mobile robots for harsh environments provide useful means for automating the collection of research data in the field by reducing human involvement. MARVIN II has been designed and constructed to autonomously collect radar measurements to determine properties of the polar ice sheets. This paper discusses the lessons learned from a number of field experiments with its predecessor MARVIN, and how these lessons influenced the new design of MARVIN II.

Design and Simulation of a Polar Mobile Robot

This paper describes the design and simulation of a mobile robot for missions in polar regions. The robot was designed to provide mobility, power, precise positioning and environmental protection for a bistatic synthetic aperture radar for polar regions to measure ice thickness and other ice sheet characteristics. The robot is required to carry and protect the radar system and to tow a large antenna, while providing precise positioning of the antenna to the accuracy of within a few centimeters. In parallel to the design and fabrication of an actual robot, a simulation model of the robot was designed and a virtual prototype was built to perform numerous experiments without the need for actual deployment in polar regions. These experiments tested robot characteristics such as slopes in the terrain, rolling effects, turning radii, antenna attachments, and payload distribution.

Simulation and Testbeds of Autonomous Robots in Harsh Environments

Software development for autonomous field robots can be quite challenging due to the difficulties associated with testing and evalution of the robot and its components. Software for the mobile robot must undergo extensive testing throughout its lifecycle. However, testing a robot for harsh environments requires access to the field, logistic support, maintenance of the robot, and adherence to safety standards. Time, budgetary, and logistics constraints often limit the amount of testing, which in turn adversely affects the quality of the software developed for the field mobile robot.

Automated Polar ice thickness estimation from radar imagery

This work focuses on automating the task of estimating Polar ice thickness from airborne radar data acquired over Greenland and Antarctica. This process involves the identification and accurate selection of the ice sheets surface location and interface between the ice sheet and the underlying bedrock for each measurement. Identifying the surface and bedrock locations in the radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume, and how they may contribute to global climate change. The time-consuming manual approach requires sparse hand-selection of surface and bedrock interfaces by several human experts, and interpolating between the selections to save time.

Establishing field and base camp servers for remote sensing of ice sheets in ilulissat, Greenland

The University of Indiana and Elizabeth City State University are working with the Center for Remote Sensing of Ice Sheets (CReSIS) to develop and deploy cyberinfrastructure grid computing resources and synthetic aperture radar (SAR) data storage capabilities for Greenland and Antarctic fieldwork. This paper will detail the Summer 2008 efforts of the Polar Grid team to configure and establish field and base camp servers. The base camp system consists of one eight-core server, three raid arrays with 34 terabytes (TB) of total storage in a RAID 10 configuration (13 TB useable), a custom designed compact peripheral control interface (CPCI) system, and 80 TB of external storage in the form of 40 two TB MyBook external hard drives. The field camp system also consisted of one eight-core server, but did not contain the raid arrays. Multiple SATA hard drives were used to store the data.

Large-scale localization using only a camera

The automation of a robot to measure ice sheets is necessary for CReSIS. The approach described in this paper is to use a single camera for localization for use in many types of large-scale environments. In order to operate in large-scale environments, a hybrid map approach has been used; both topological and geometric maps. An appearance-based approach is used for recognizing the different locations. In order to simplify testing, all experiments were performed offline and the topological and geometric testing was performed separately. The results of the testing showed that 94% of the images were localized correctly in the testing environment. The system typically required at least two images to solve the global localization problem, and around three images to solve the kidnapped robot problem. These images are localized to within 1 foot and 45 degrees of the actual position and orientation.