Affiliation:
1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA
2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA USA,
Abstract
NASA’s Mars Exploration Rovers (MER) have collected a great diversity of geological science results, thanks in large part to their surface mobility capabilities. The six wheel rocker/bogie mobility system provides driving capabilities in a range of terrain types, while the onboard IMU measures actual rover attitude changes (roll, pitch and yaw, but not position) quickly and accurately. Four stereo camera pairs provide accurate position knowledge and/or terrain assessment. Solar panels generally provide enough energy to drive the vehicle for at most four hours each day, but drive time is often restricted by other planned activities. Driving along slopes in nonhomogeneous terrain injects unpredictable amounts of slip into each drive. These restrictions led to the creation of driving strategies that alternately use more or less onboard autonomy, to maximize drive speed and distance at the cost of increased complexity in the sequences of commands built by human Rover Planners each day. Commands to the MER vehicles are typically transmitted at most once per day, so mobility operations are encoded as event-driven sequences of individual motion commands. Motions may be commanded using quickly-executing Directed commands which perform only reactive motion safety checks (e.g., real-time current limits, maximum instantaneous vehicle tilt limit), slowly-executing position measuring Visual Odometry (VisOdom) commands, which use images to accurately update the onboard position estimate, or slow-to-medium speed Autonomous Navigation (AutoNav) commands, which use onboard image processing to perform predictive terrain safety checks and optional autonomous Path Selection. In total, the MER rovers have driven more than 10 kilometers over Martian terrain during their first 21 months of operation using these basic modes. In this paper we describe the strategies adopted for selecting between human-planned Directed drives versus rover-adaptive Autonomous Navigation, Visual Odometry and Path Selection drives.
Subject
Applied Mathematics,Artificial Intelligence,Electrical and Electronic Engineering,Mechanical Engineering,Modeling and Simulation,Software
Cited by
43 articles.
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