Roving The Moon

Researchers in the Robotics Institute of Carnegie Mellon University's School of Computer Science are building a robotic prospector for NASA that can creep over rocky slopes and then anchor itself as a stable platform for drilling deep into extraterrestrial soils.

Called "Scarab," this four-wheeled robot will never leave the Earth. But it will demonstrate technologies that a lunar rover will need to find concentrations of hydrogen, possibly water and other volatile chemicals on the moon that could be mined to produce fuel, water and air that are essential for supporting lunar outposts. Such technology is essential if human explorers are to return to the Moon and conduct important experiments at the lunar surface.

Scarab is equipped with a Canadian-made drill for obtaining meter-long geological core samples and features a novel rocker-arm suspension that enables the robot to plant its belly on the ground for drilling operations.

"A lunar prospector will face a hostile environment in the perpetual darkness of craters at the moon's southern pole, where ground temperatures are minus 385 degrees and no energy source is at hand," said William "Red" Whittaker, the Fredkin Research Professor and principal investigator of the NASA-funded project. "It's a place where humans can't work effectively, but where Scarab will thrive, even while operating on the electrical power required to illuminate a 100-watt light bulb."

Robotic prospecting on the moon poses substantial, sometimes conflicting challenges. Scarab must be agile enough to travel miles over sandy, rock-strewn soil, but also serve as a stable drilling platform. Operating for months in total darkness, it cannot rely on solar energy or batteries for power. Instead it will use a radioisotope source that places a premium on energy efficiency. To navigate in total darkness, Scarab must rely on new, low-power, laser-based sensors.

"As a consequence of the power restrictions, it's not very speedy," said DavidWettergreen, associate research professor of robotics and leader of Scarab's software and autonomy development. With a top speed of just four inches per second, Scarab tries the patience of even the most laid-back observer. When faced with particularly large obstacles or drilling tasks, it may pause to store up extra power.

To optimize efficiency, the robot must be as light as possible. But to operate the coring drill, the vehicle also has to be massive enough to apply sufficient downward pressure on the drill and counter the torque of the rotating drill. Researchers estimate it must weigh at least 250 kilograms, or about 550 pounds.

The suspension allows Scarab to make the most of its weight by enabling it to lower its 5 1/2-foot-by-3-foot body to the ground for drilling operations. "One of the design innovations was to put the drill in the center of the robot," Wettergreen said, rather than attaching it to an arm. "Scarab can apply its entire mass onto the drill, so that everything is assisting the drilling operation."

The suspension also makes it possible for Scarab to raise its body as much as 21 inches off the ground, so it can straddle rocks or lean as it negotiates steep slopes.

"It's a good combination vehicle that does two things very well," said John Caruso, project manager at NASA's Glenn Research Center in Cleveland. "Scarab is successful because it achieves the design simplicity of a single-purpose machine while accomplishing the multiple purposes of driving and drilling in darkness."

Also important is that the vehicle has been developed as an integrated package based on the requirements of an entire prospecting mission, Caruso said. NASA hasn't announced such a mission as yet, he noted, but developing the technology now will ultimately lower the technical risk for such an undertaking. Glenn Research Center is developing radioisotope power sources for deep space and lunar applications.

The drill is being built by the Northern Centre For Advanced Technology Inc. in Sudbury, Ontario, and will be capable of processing and analyzing the geologic cores it obtains.

Researchers at NASA's Ames Research Center are collaborating to evaluate navigational sensors and algorithms for operation in darkness, such as a "light striper" being built at Carnegie Mellon that detects obstructions by shining laser beams and then looking for distortions in the beams.

Researchers at the Robotics Institute have been working since March to build the robot and develop its autonomous navigation and scientific software. The carbon-composite body was designed and built by a team of engineers headed by John Thornton, a student who also builds streamlined racers featured in Carnegie Mellon's annual Buggy Races.

Development work continues on software that can use all of Scarab's motions to best advantage and enable it to navigate autonomously in the dark.

A field experiment planned for the end of the year will put driving and drilling in the dark together in a complete demonstration of the lunar mission concept. The project is funded through NASA's Johnson Space Center in Houston and its In-situ Resource Utilization program.

Whittaker has announced that he is assembling a team to compete for the Google Lunar X-Prize and its $20 million grand prize for operating a privately funded robot on the moon by 2012. That effort is separate and distinct from the NASA-funded Scarab project, which is developing technologies that could be used on the moon but are being tested on Earth.

Guessing Robots Predict Their Environments For Better Navigation

Engineers at Purdue University are developing robots able to make "educated guesses" about what lies ahead as they traverse unfamiliar surroundings, reducing the amount of time it takes to successfully navigate those environments. The method works by using a new software algorithm that enables a robot to create partial maps as it travels through an environment for the first time. The robot refers to this partial map to predict what lies ahead.

The more repetitive the environment, the more accurate the prediction and the easier it is for the robot to successfully navigate, said C.S. George Lee, a Purdue professor of electrical and computer engineering who specializes in robotics.

"For example, it's going to be easier to navigate a parking garage using this map because every floor is the same or very similar, and the same could be said for some office buildings," he said.

Both simulated and actual robots in the research used information from a laser rangefinder and odometer to measure the environment and create the maps of the layout.

The algorithm modifies an approach, called SLAM, which was originated in the 1980s. The name SLAM, for simultaneous localization and mapping, was coined in the early 1990s by Hugh F. Durrant-Whyte and John J. Leonard, then engineers at the University of Oxford in the United Kingdom.

SLAM uses data from sensors to orient a robot by drawing maps of the immediate environment. Because the new method uses those maps to predict what lies ahead, it is called P-SLAM.

"Its effectiveness depends on the presence of repeated features, similar shapes and symmetric structures, such as straight walls, right-angle corners and a layout that contains similar rooms," Lee said. "This technique enables a robot to make educated guesses about what lies ahead based on the portion of the environment already mapped."

Research findings were detailed in a paper that appeared in April in IEEE Transactions on Robotics, published by the Institute of Electrical and Electronics Engineers. The paper was authored by doctoral student H. Jacky Chang, Lee, assistant professor Yung-Hsiang Lu and associate professor Y. Charlie Hu, all in Purdue's School of Electrical and Computer Engineering.

Potential applications include domestic robots and military and law enforcement robots that search buildings and other environments.

The Purdue researchers tested their algorithm in both simulated robots and in a real robot navigating the corridors of a building on the Purdue campus. Findings showed that a simulated robot using the algorithms was able to successfully navigate a virtual maze while exploring 33 percent less of the environment than would ordinarily be required.

Future research will extend the concept to four robots working as a team, operating with ant-like efficiency to explore an unknown environment by sharing the mapped information through a wireless network. The researchers also will work toward creating an "object-based prediction" that recognizes elements such as doors and chairs, as well as increasing the robots' energy efficiency.

Robots operating without the knowledge contained in the maps must rely entirely on sensors to guide them through the environment. Those sensors, however, are sometimes inaccurate, and mechanical errors also cause the robot to stray slightly off course.

The algorithm enables robots to correct such errors by referring to the map, navigating more precisely and efficiently.

"When the robot makes a turn to round a corner, let's say there is some mechanical error and it turns slightly too sharp or not sharply enough," Lee said. "Then, if the robot continues to travel in a straight line that small turning error will result in a huge navigation error in the long run."

The research has been funded by the National Science Foundation.

In separate work, Purdue undergraduate students in a senior design class have developed a prototype firefighting robot called Firebot.