AUTOAPPROACH is a computer program that enables a mobile robot to approach a target autonomously, starting from a distance of as much as 10 m, in response to a single command. AUTOAPPROACH is used in conjunction with (1) software that analyzes images acquired by stereoscopic cameras aboard the robot and (2) navigation and path-planning software that utilizes odometer readings along with the output of the image-analysis software.
Intended originally for application to an instrumented, wheeled robot (rover) in scientific exploration of Mars, AUTOAPPROACH could be adapted to terrestrial applications, notably including the robotic removal of land mines and other unexploded ordnance. A human operator generates the approach command by selecting the target in images acquired by the robot cameras. The approach path consists of multiple legs. Feature points are derived from images that contain the target and are thereafter tracked to correct odometric errors and iteratively refine estimates of the position and orientation of the robot relative to the target on successive legs. The approach is terminated when the robot attains the position and orientation required for placing a scientific instrument at the target. The workspace of the robot arm is then autonomously checked for self/terrain collisions prior to the deployment of the scientific instrument onto the target.
This program was written by Terrance Huntsberger and Yang Cheng of NASA's Jet Propulsion Laboratory. For further information, access the Technical Support Package (TSP) free on-line at www.techbriefs.com/tsp under the Software category. This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393-3425. Refer to NPO-30529
This Brief includes a Technical Support Package (TSP).
Single-Command Approach and Instrument Placement by a Robot on a Target
(reference NPO-30529) is currently available for download from the TSP library.
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Overview
The document is a technical support package from NASA's Jet Propulsion Laboratory (JPL) detailing advancements in rover autonomy for planetary exploration, particularly focusing on the Field Integrated, Design and Operations (FIDO) rover. This rover serves as a terrestrial analog for the Mars Exploration Rovers (MER) set to launch in 2003. The primary goal of the research is to enhance the rover's autonomy to maximize scientific data collection while minimizing the need for ground control interactions.
The FIDO rover is equipped with a sophisticated suite of sensors, including an inertial navigation unit (INU), sun sensors, and various imaging systems. These sensors facilitate multisensor fusion through an Extended Kalman Filter (EKF) approach, which, combined with pattern recognition and tracking algorithms, allows the rover to navigate autonomously. The document outlines the challenges faced in rover navigation, such as reaching destinations hundreds of meters away, precision rendezvous with landers, and the autonomous deployment of scientific instruments.
The rover's navigation strategy has evolved from traditional sense/plan/act methods, which require significant computational resources and time, to more reactive methods that can operate effectively under the constraints of mass and power typical of long-range missions. The FIDO rover features a six-wheel rocker-bogie suspension system, enabling it to traverse obstacles up to 30 cm high, and is equipped with a 4-degrees-of-freedom mast for deploying scientific instruments.
The document also discusses the long-range rendezvous capabilities necessary for future Mars missions, such as the Mars Sample Return (MSR) mission planned for 2013. The rover must navigate autonomously to remote sites, collect samples, and return to the lander. The navigation precision required varies with distance, necessitating different techniques for long-range, mid-range, and short-range traverses.
In summary, the document highlights the innovative approaches being developed at JPL to enhance rover autonomy, focusing on the FIDO rover's capabilities and the algorithms that enable it to operate effectively in challenging environments. These advancements are crucial for the success of future Mars missions and have broader implications for robotics and autonomous systems in various fields.
