567 research outputs found
The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah
(ABRIDGED) In previous work, two platforms have been developed for testing
computer-vision algorithms for robotic planetary exploration (McGuire et al.
2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been
tested at geological and astrobiological field sites in Spain (Rivas
Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a
geological field site in Malta. In this work, we (i) apply a Hopfield
neural-network algorithm for novelty detection based upon color, (ii) integrate
a field-capable digital microscope on the wearable computer platform, (iii)
test this novelty detection with the digital microscope at Rivas Vaciamadrid,
(iv) develop a Bluetooth communication mode for the phone-camera platform, in
order to allow access to a mobile processing computer at the field sites, and
(v) test the novelty detection on the Bluetooth-enabled phone-camera connected
to a netbook computer at the Mars Desert Research Station in Utah. This systems
engineering and field testing have together allowed us to develop a real-time
computer-vision system that is capable, for example, of identifying lichens as
novel within a series of images acquired in semi-arid desert environments. We
acquired sequences of images of geologic outcrops in Utah and Spain consisting
of various rock types and colors to test this algorithm. The algorithm robustly
recognized previously-observed units by their color, while requiring only a
single image or a few images to learn colors as familiar, demonstrating its
fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International
Journal of Astrobiolog
Adaptive and intelligent navigation of autonomous planetary rovers - A survey
The application of robotics and autonomous systems in space has increased dramatically. The ongoing Mars rover mission involving the Curiosity rover, along with the success of its predecessors, is a key milestone that showcases the existing capabilities of robotic technology. Nevertheless, there has still been a heavy reliance on human tele-operators to drive these systems. Reducing the reliance on human experts for navigational tasks on Mars remains a major challenge due to the harsh and complex nature of the Martian terrains. The development of a truly autonomous rover system with the capability to be effectively navigated in such environments requires intelligent and adaptive methods fitting for a system with limited resources. This paper surveys a representative selection of work applicable to autonomous planetary rover navigation, discussing some ongoing challenges and promising future research directions from the perspectives of the authors
Automated Global Feature Analyzer - A Driver for Tier-Scalable Reconnaissance
For the purposes of space flight, reconnaissance field geologists have trained to become astronauts. However, the initial forays to Mars and other planetary bodies have been done by purely robotic craft. Therefore, training and equipping a robotic craft with the sensory and cognitive capabilities of a field geologist to form a science craft is a necessary prerequisite. Numerous steps are necessary in order for a science craft to be able to map, analyze, and characterize a geologic field site, as well as effectively formulate working hypotheses. We report on the continued development of the integrated software system AGFA: automated global feature analyzerreg, originated by Fink at Caltech and his collaborators in 2001. AGFA is an automatic and feature-driven target characterization system that operates in an imaged operational area, such as a geologic field site on a remote planetary surface. AGFA performs automated target identification and detection through segmentation, providing for feature extraction, classification, and prioritization within mapped or imaged operational areas at different length scales and resolutions, depending on the vantage point (e.g., spaceborne, airborne, or ground). AGFA extracts features such as target size, color, albedo, vesicularity, and angularity. Based on the extracted features, AGFA summarizes the mapped operational area numerically and flags targets of "interest", i.e., targets that exhibit sufficient anomaly within the feature space. AGFA enables automated science analysis aboard robotic spacecraft, and, embedded in tier-scalable reconnaissance mission architectures, is a driver of future intelligent and autonomous robotic planetary exploration
Space exploration: The interstellar goal and Titan demonstration
Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed
The Role of Robots and Automation in Space
Advanced space transportation systems based on the shuttle and interim upper stage will open the way to the use of large-scale industrial and commercial systems in space. The role of robot and automation technology in the cost-effective implementation and operation of such systems in the next two decades is discussed. Planning studies initiated by NASA are described as applied to space exploration, global services, and space industrialization, and a forecast of potential missions in each category is presented. The appendix lists highlights of space robot technology from 1967 to the present
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
Dynamic path planning for reconfigurable rovers using a multi-layered grid
Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number
of places visited during the mission. It depends heavily on the information that is available for the traversed
surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images.
Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute
multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning
algorithm based on the Fast Marching Method is proposed. Environment information coming from different
sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension
is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes
advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance
is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and
field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental
terrain
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