Terrain Adaptive Navigation for planetary rovers

In common with other Mars exploration missions, human supervision of Europe's ExoMars Rover will be mostly indirect via orbital relay spacecraft and thus far from immediate. The gap between issuing commands and witnessing the results of the consequent rover actions will typically be on the order of several hours or even sols. In addition, it will not be possible to observe the external environment at the time of action execution. This lengthens the time required to carry out scientific exploration and limits the mission's ability to respond quickly to favorable science events. To increase potential science return for such missions, it will be necessary to deploy autonomous systems that include science target selection and active data acquisition. In this work, we have developed and integrated technologies that we explored in previous studies and used the resulting test bed to demonstrate an autonomous, opportunistic science concept on a representative robotic platform. In addition to progressing the system design approach and individual autonomy components, we have introduced a methodology for autonomous science assessment based on terrestrial field science practice. © 2009 Wiley Periodicals, Inc.

[1]  Terrance L. Huntsberger,et al.  Closed loop control for autonomous approach and placement of science instruments by planetary rovers , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Brian C. Williams,et al.  A Reactive Model-based Programming Language for Robotic Space Explorers , 2001 .

[3]  P. Backes,et al.  Automated rover positioning and instrument placement , 2005, 2005 IEEE Aerospace Conference.

[4]  Maria Fox,et al.  Validating Plans in the Context of Processes and Exogenous Events , 2005, AAAI.

[5]  P. Gazis,et al.  Autonomous identification of carbonates using near-IR reflectance spectra during the February 1999 Marsokhod field tests , 1999 .

[6]  M. Fox,et al.  The 3rd International Planning Competition: Results and Analysis , 2003, J. Artif. Intell. Res..

[7]  James A. Hendler,et al.  HTN Planning: Complexity and Expressivity , 1994, AAAI.

[8]  Jorge L. Vago,et al.  Development of the ESA ExoMars Rover , 2005 .

[9]  Mark Woods,et al.  MMOPS: Assessing the Impact of On-Board Autonomy for Deep Space Robotic Missions , 2006 .

[10]  David S. Wettergreen,et al.  SECOND EXPERIMENTS IN THE ROBOTIC INVESTIGATION OF LIFE IN THE ATACAMA DESERT OF CHILE , 2005 .

[11]  Tara A. Estlin,et al.  Oasis: Onboard autonomous science investigation system for opportunistic rover science , 2007, J. Field Robotics.

[12]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.

[13]  Randy Sargent,et al.  Performance Evaluation of Handoff for Instrument Placement , 2006 .

[14]  Maria Fox,et al.  PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains , 2003, J. Artif. Intell. Res..

[15]  Maria Fox,et al.  VAL: automatic plan validation, continuous effects and mixed initiative planning using PDDL , 2004, 16th IEEE International Conference on Tools with Artificial Intelligence.

[16]  Nicola Muscettola,et al.  Automated Planning and Scheduling for Goal-Based Autonomous Spacecraft , 1998, IEEE Intell. Syst..

[17]  Randy Sargent,et al.  Multi-Target Single Cycle Instrument Placement , 2005 .

[18]  E. D. Smith,et al.  Increased Flexibility and Robustness of Mars Rovers , 1999 .

[19]  Bernhard Nebel,et al.  Plan Reuse Versus Plan Generation: A Theoretical and Empirical Analysis , 1995, Artif. Intell..

[20]  J.L. Bresina,et al.  Mission operations planning: beyond MAPGEN , 2006, 2nd IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT'06).

[21]  David R. Thompson,et al.  Autonomous Detection of Novel Biologic and Geologic Features in Atacama Desert Rover Imagery , 2006 .

[22]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Joseph. Wood,et al.  The geomorphological characterisation of Digital Elevation Models , 1996 .

[24]  D.R. Thompson,et al.  Performance Comparison of Rock Detection Algorithms for Autonomous Planetary Geology , 2007, 2007 IEEE Aerospace Conference.

[25]  Takeo Kanade,et al.  A Cooperative Algorithm for Stereo Matching and Occlusion Detection , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Andrew Shaw,et al.  Landmark recognition for localisation and navigation of aerial vehicles , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[27]  Maurice E. Tucker,et al.  Sedimentary rocks in the field , 1996 .

[28]  Mark Woods,et al.  Image based localisation and autonomous image assessment for a Martian aerobot , 2008 .

[29]  Mark A. Ruzon,et al.  Autonomous image analyses during the 1999 Marsokhod rover field test , 2001 .

[30]  G. Klingelhöfer,et al.  Identification of morphological biosignatures in Martian analogue field specimens using in situ planetary instrumentation. , 2008, Astrobiology.

[31]  Ramon Abel Castano,et al.  Automated Target Selection for Opportunistic Rover Science , 2006 .

[32]  Russell H. Taylor,et al.  Planning and execution of straight line manipulator trajectories , 1979 .

[33]  Reid G. Simmons,et al.  A task description language for robot control , 1998, Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190).

[34]  Rob Sherwood,et al.  Using Autonomy Flight Software to Improve Science Return on Earth Observing One , 2005, J. Aerosp. Comput. Inf. Commun..

[35]  Alex Fukunaga,et al.  Iterative Repair Planning for Spacecraft Operations Using the Aspen System , 2000 .

[36]  David R. Thompson,et al.  Data Mining During Rover Traverse: From Images to Geologic Signatures , 2005 .

[37]  Rob Sherwood,et al.  Lessons learned from autonomous sciencecraft experiment , 2005, AAMAS '05.