Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-13T07:43:27.320Z Has data issue: false hasContentIssue false

Autonomy for ground-level robotic space exploration: framework, simulation, architecture, algorithms and experiments

Published online by Cambridge University Press:  18 June 2014

R. Correal*
Affiliation:
University Complutense of Madrid, Software Engineering and Artificial Intelligence Dept. C/ Profesor José García Santesmases, s/n. 28040 Madrid, Spain
G. Pajares
Affiliation:
University Complutense of Madrid, Software Engineering and Artificial Intelligence Dept. C/ Profesor José García Santesmases, s/n. 28040 Madrid, Spain
J. J. Ruz
Affiliation:
University Complutense of Madrid, Computers Architecture and Automation Dept. C/ Profesor José García Santesmases, s/n. 28040 Madrid, Spain
*
*Corresponding author. E-mail: rcorreal@estumail.ucm.es

Summary

Robotic surface planetary exploration is a challenging endeavor, with critical safety requirements and severe communication constraints. Autonomous navigation is one of the most crucial and yet risky aspects of these operations. Therefore, a certain level of local autonomy for onboard robots is an essential feature, so that they can make their own decisions independently of ground control, reducing operational costs and maximizing the scientific return of the mission. In addition, existing tools to support research in this domain are usually proprietary to space agencies, and out of reach of most researchers. This paper presents a framework developed to support research in this field, a modular onboard software architecture design and a series of algorithms that implement a visual-based autonomous navigation approach for robotic space exploration. It allows analysis of algorithms' performance and functional validation of approaches and autonomy strategies, data monitoring and the creation of simulation models to replicate the vehicle, sensors, terrain and operational conditions. The framework and algorithms are partly supported by open-source packages and tools. A set of experiments and field testing with a physical robot and hardware are described as well, detailing results and algorithms' processing time, which experience an incremented of one order of magnitude when executed in space-certified like hardware, with constrained resources, in comparison to using general purpose hardware.

Type
Articles
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Stone, H. W., “Mars Pathfinder Microrover, A Small, Low-Cost, Low-Power SpacecraftAIAA Forum on Advanced Developments in Space Robotics, Madison, WI, USA (1996).Google Scholar
2.Mishkin, A. H., Morrison, J. C., Nguyen, T. T., Stone, H. W., Cooper, B. K. and Wilcox, B. H., “Experiences with Operations and Autonomy of the Mars Pathfinder Microrover,” Proceedings of the IEEE Aerospace Conference, Snowmass, Colorado, USA (1998), pp. 337351.Google Scholar
3.Crisp, J. A., Adler, M., Matijevic, J. R., Squyres, S. W., Arvidson, R. E. and Kass, D. M., “Mars exploration rover mission,” J. Geophys. Res. 108 (E12), 80618078 (2003).CrossRefGoogle Scholar
4.Smith, P. H.et al., “Introduction to special section on the Phoenix mission: Landing site characterization experiments, mission overviews, and expected science,” J. Geophys. Res. 113, E00A18 (2008).CrossRefGoogle Scholar
5.Grotzinger, J. P.et al., “Mars science laboratory mission and science investigation,” Space Sci. Rev. 170 (1–4), 556 (2012).CrossRefGoogle Scholar
6.Correal, R. and Pajares, G., “Modeling, simulation and onboard autonomy software for robotic exploration on planetary environments,” Int'l. Conf. DAta Systems In Aerospace (DASIA), Malta (2011).Google Scholar
7.Correal, R. and Pajares, G., “Onboard Autonomous Navigation Architecture for a Planetary Surface Exploration Rover and Functional Validation Using Open-Source Tools,” ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA), ESA/ESTEC Noordwijk, the Netherlands (2011).Google Scholar
8.Thueer, T., Krebs, A., Siegwart, R. and Lamon, P., “Performance comparison of rough-terrain robots—;simulation and hardware,” J. Field Robot. 24 (3)251271 (2007).CrossRefGoogle Scholar
9.Yen, J., Jain, A. and Balaram, J., “ROAMS: Rover Analysis, Modeling and Simulation Software,” Proceedings of the IntInternational Symposium on Artificial Intelligence and Robotics & Automation in Space (i-SAIRAS), Noordwijk, the Netherlands (1999).Google Scholar
10.Jain, A., Guineau, J., Lim, C., Lincoln, W., Pomerantz, M., Sohl, G. and Steele, R., “ROAMS: Planetary Surface Rover Simulation Environment,” Proceedings of the IntInternational Symposium on Artificial Intelligence and Robotics & Automation in Space (i-SAIRAS), Nara, Japan (2003).Google Scholar
11.Edwards, L., Fluckiger, L., Nguyen, L. and Washington, R., “VIPER: Virtual Intelligent Planetary Exploration Rover,” Proceedings of the IntInternational Symposium on Artificial Intelligence and Robotics & Automation in Space (i-SAIRAS), Quebec, Canada (2001).Google Scholar
12.Neveu, C. and Shirley, M., “LiveInventor: An Interactive Development Environment for Robot Autonomy,” Proceedings of the IntInternational Symposium on Artificial Intelligence and Robotics & Automation in Space (i-SAIRAS), Japan (2003).Google Scholar
13.Maurette, M. and Rastel, L., “Planetary Rover Simulation and Operation,” ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA), ESA/ESTEC, Noordwijk, The Netherlands (2002).Google Scholar
14.Odwyer, A. and Correal, R., “Experiences in Producing a Preliminary Navigation OBSW Prototype for the Exomars Rover Based on EDRES,” ESA Workshop on Advanced Space Technologies for Robotics and Automation (ASTRA), ESA/ESTEC, Noordwijk, The Netherlands (2008).Google Scholar
15.Poulakis, P., Joudrier, L., Wailliez, S. and Kapellos, K., “3DROV: A Planetary Rover System Design, Simulation and Verification Tool,” Proceedings of the IntInternational Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), Los Angeles, USA (2008).Google Scholar
16.MATLAB® Primer (The MathWorks, Inc., Natick, MA, 2012).Google Scholar
17.Webots Reference Manual (Cyberbotics Ltd., Lausanne, Switzerland, 2006).Google Scholar
18.Gerkey, B. P., Vaughan, R.T. and Howard, A., “The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems,” Proceedings of the IntInternational Conference on Advanced Robotics, Coimbra, Portugal (2003) pp. 317323.Google Scholar
19.McMillan, S., DynaMechs: A Multibody Dynamic Simulation Library, (2003) available http://dynamechs.sourceforge.net/.Google Scholar
20.VORTEX Developer Guide, A Manual for the VORTEX Simulation Toolkit (Cmlabs Simulations, Inc., Montreal, Canada, 2002).Google Scholar
21.Leger, C., Automated Synthesis and Optimization of Robot Configurations: An Evolutionary Approach, (The Robotics Institute, Carnegie Mellon University 1999).Google Scholar
22.RobotSim documentation, (Cogmation Robotics Inc., Manitoba, Canada, 2010).Google Scholar
23.Hugues, L. and Bredeche, N., “Simbad : An Autonomous Robot Simulation Package for Education and Research,” Proceedings of the IntInternational Conference on Simulation of Adaptive Behavior, Rome, Italy (2006).Google Scholar
24.Blanco, J. L., Development of Scientific Applications with the Mobile Robot Programming Toolkit (University of Malaga, Spain, 2010).Google Scholar
25.Montemerlo, M., Roy, N. and Thrun, S., “Perspectives on standardization in mobile robot programming: The carnegie mellon navigation (CARMEN) toolkin,” In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IROS (2003) pp. 24362441.Google Scholar
26.Staranowicz, A. and Mariottini, G. L., “A Survey and Comparison of Commercial and Open-Source Robotic Simulator Software,” Proceedings of the IntInternational Conference on PErvasive Technologies Related to Assistive Environments (PETRA), Crete, Greece (2011).Google Scholar
27.Koenig, N. and Howard, A., “Design and Use Paradigms for Gazebo, an Open-Source Multi-Robot Simulator,” Proceedings of the IntInternational Conference on Intelligent Robots and Systems, Sendai, Japan (2004).Google Scholar
28.Quigley, M., Conley, K., Gerkey, B., et al.ROS: An Open-Source Robot Operating System,” Open-Source Software Workshop, IEEE, ICRA (2009).Google Scholar
29.Yen, J., “Slip validation and prediction for Mars Exploration Rovers,” Sensor Transducers Mag. 90, 233242 (2008).Google Scholar
30.Wong, J. Y., Theory of Ground Vehicles (John Wiley & Sons, 2001).Google Scholar
31.Henning, M. and Spruiell, M., Distributed Programming with Ice (ZeroC, Inc. 2010).Google Scholar
32.Joudrier, L. and Elfving, A., “Challenges of the ExoMars Rover Control,” American Institute of Aeronautics and Astronautics, AIAA 2009–1807, Seattle, Washington, USA (2009).Google Scholar
33.Nilsson, N. J., Principles of Artificial Intelligence (Tioga Publishing Co., Palo Alto, 1982).CrossRefGoogle Scholar
34.Brooks, R., “A robust layered control system for a mobile robot,” IEEE J. Robot. Autom. RA-2 (1), 1423 (1986).CrossRefGoogle Scholar
35.Volpe, R., Nesnas, I. A. D., Estlin, T., Mutz, D., Petras, R. and Das, H., “The CLARAty Architecture for Robotic Autonomy,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, USA (2001).Google Scholar
36.Ingrand, F., Lacroix, S., Lemai-Chenevier, S. and Py, F., “Decisional autonomy of planetary rovers,” J. Field Robot. 24 (7), 559580 (2007).CrossRefGoogle Scholar
37.Konolige, K., “Small Vision System: Hardware and Implementation,” Proceedings of the International Symposium on Robotics Research, Hayama, Japan (1997) pp. 111116.Google Scholar
38.Bradski, G. and Kaehler, A., Learning OpenCV. Computer Vision with the OpenCV Library (O'Reilly Media, 2008).Google Scholar
39.Goldberg, S., Maimone, M. and Matthies, L., “Stereo Vision and Rover Navigation Software for Planetary Exploration,” IEEE Aerospace Conference, Big Sky, Montana, USA (2002).Google Scholar
40.Biesiadicki, J. and Maimone, M., “The Mars Exploration Rover Surface Mobility Flight Software: Driving Ambition,” IEEE Aerospace Conference, Big Sky, Montana (USA) (2006).Google Scholar
41.Angelova, A., Matthies, L., Helmick, D. and Perona, P., “Learning and prediction of slip from visual information,” J. Field Robot. 24, 205231 (2007).CrossRefGoogle Scholar
42.Helmick, D., Angelova, A. and Matthies, L., “Terrain adaptive navigation for planetary rovers,” J. Field Robot. (2009).CrossRefGoogle Scholar
43.Correal, R., Pajares, G. and Ruz, J. J., “Mejora del Proceso de Correspondencia en Imágenes Estereoscópicas Mediante Filtrado Homomórfico y Agrupaciones de Disparidad,” Revista Iberoamericana de Automática e Informática Industrial 10 (2), 178184 (2013).CrossRefGoogle Scholar
44.Matthies, L.et al., “Computer vision on Mars,” Int. J. Comput. Vis. 75 (1), 6792 (2007).CrossRefGoogle Scholar
45.Garlan, D., Allen, R. and Ockerbloom, J., “Architectural mismatch: Why reuse is still so hard,” IEEE Softw. 26 (4), 6669 (2009).CrossRefGoogle Scholar