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Autonomous Rover Technologies (ART)
This project is no longer active.
Head: William (Red) L. Whittaker
Contact: Dimitrios (Dimi) Apostolopoulos
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center(s) / consortia:
 Space Robotics Initiative (SRI)
 Field Robotics Center (FRC)
The primary goal of this project is to research and develop the enabling technologies for autonomous planetary robot perception, position estimation, navigation, and integrated exploratory science from a robot, and validate such technologies through aggressive and rigorous field experimentation.

The specific research objectives for FY99, are:

  • Navigation and science from panoramic imagery: Prior research in wide field imaging developed teleoperated remote viewing and demonstrated its merits for robots, but fell short of the scope and benefits possible for automation with wide imagery. The immense opportunity generated by capturing lateral and longitudinal views from a rover simultaneously, has not been exploited. We research techniques for autonomous visual deduced reckoning, landmark based navigation, and scientific characterizations using panoramic imagery.
  • Advanced radar perception and safeguarding: Sonar, stereo, and laser have dominated robot perception research, but each has liability and downfall for application in space. Radar holds the prospect for modeling, safeguarding, and navigation from a space robot with advantages of operating in and through dust, in vacuum and atmosphere. We investigate the merits of ultra high-frequency radar to detect objects, map terrain features, and even profile shallow subsurface geology in substantial dust accumulation during long traverses.
  • Science data classification from multiple sensors: No "perfect" sensor or classification methodology exists for robustly distinguishing interesting science observations, like evidence for life, geologic anomalies, fossils, and meteorites among other rocks. We have been developing a principled framework within which output from a variety of sensors and multiple classification algorithms is used to confirm or deny the detection of a scientific object of interest.
  • Advanced rover autonomy: Extensive research has gone into obstacle detection and avoidance methods for autonomous robots. However, these methods largely rely on knowledge of robot characteristics (such as sensor coverage and mobility). Providing a robot with health monitoring and error recovery capabilities will allow the robot to notice that its turning radius as increased and incorporate this into its planning allowing a mission to continue even though a malfunction has occurred. We have been developing a general health monitoring capability capable of detecting failures in the drive, steering, and sensor components of the vehicle. An error recovery capability is also under investigation which will use the error diagnosis to modify obstacle detection and avoidance behavior.