Skip to main content
Log in

Three-layer intelligence of planetary exploration wheeled mobile robots: Robint, virtint, and humint

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

The great success of the Sojourner rover in the Mars Pathfinder mission set off a global upsurge of planetary exploration with autonomous wheeled mobile robots (WMRs), or rovers. Planetary WMRs are among the most intelligent space systems that combine robotic intelligence (robint), virtual intelligence (virtint), and human intelligence (humint) synergetically. This article extends the architecture of the three-layer intelligence stemming from successful Mars rovers and related technologies in order to support the R&D of future tele-operated robotic systems. Double-layer human-machine interfaces are suggested to support the integration of humint from scientists and engineers through supervisory (Mars rovers) or three-dimensional (3D) predictive direct tele-operation (lunar rovers). The concept of multilevel autonomy to realize robint, in particular, the Coupled-Layer Architecture for Robotic Autonomy developed for Mars rovers, is introduced. The challenging issues of intelligent perception (proprioception and exteroception), navigation, and motion control of rovers are discussed, where the terrains’ mechanical properties and wheel-terrain interaction mechanics are considered to be key. Double-level virtual simulation architecture to realize virtint is proposed. Key technologies of virtint are summarized: virtual planetary terrain modeling, virtual intelligent rover, and wheel-terrain interaction mechanics. This generalized three-layer intelligence framework is also applicable to other systems that require human intervention, such as space robotic arms, robonauts, unmanned deep-sea vehicles, and rescue robots, particularly when there is considerable time delay.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Robinson M. Soviet Union lunar rovers. http://lroc.sese.asu.edu/posts/11

  2. JPL. NASA/JPL Mars pathfinder. http://marsprogram.jpl.nasa.Gov/MPF/

  3. JPL. NASA/JPL Mars exploration rover mission. http://marsrovers.jpl.nasa.gov/home/index.html

  4. Squyres S W, Knoll A H, Arvidson R E, et al. Exploration of victoria crater by the Mars rover opportunity. Science, 2009; 324: 1058–1061

    Google Scholar 

  5. Squyres S W, Arvidson R E, Bell III J F, et al. The Spirit rover’s Athena science investigation at Gusev Crater, Mars. Science, 2004; 305: 794–799

    Google Scholar 

  6. JPL. Mars Science Laboratory–Curiosity: NASA’s next Mars rover. http://www.nasa.gov/mission_pages/msl/

  7. ESA. ExoMars Mission. http://www.esa.int/ SPECIALS/ExoMars/SEM10VLPQ5F_0.html

  8. Sun Z Z, Jia Y, Zhang H. Technological advancements and promotion roles of Chang’e-3 lunar probe mission. Sci China Tech Sci, 2013; 56: 2702–2708

    Google Scholar 

  9. JAXA. Moon lander SELENE 2. http://www. jspec.jaxa.jp/e/activity/selene2.html

  10. NASA. Solar System exploration–the 2006 Solar System exploration roadmap for NASA’s science mission directorate. http://www.lpi.usra.edu/vexag/road_map_final. pdf. 2006

  11. Hayati S, Volpe R, Backes P, et al. The Rocky _rover: A Mars sciencecraft prototype. In: Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, NM, USA: IEEE, 1997. 2458–2464

    Google Scholar 

  12. Zheng Y, Ouyang Z, Li C, et al. China’s lunar exploration program: Present and future. Planet Space Sci, 2008; 56: 881–886

    Google Scholar 

  13. Maimone M, Biesiadecki J, Tunstel E, et al. Surface navigation and mobility intelligence on the Mars exploration rovers. In: Intelligence for Space Robotics. Howard A M, Tunstel E W, eds. San Antonio, TX, USA, 2006

    Google Scholar 

  14. Bajracharya M, Maimone M W, Helmick D. Autonomy for Mars rovers: past, present, and future. Computer, 2008; 41: 44–50

    Google Scholar 

  15. Maimone M W, Leger P C, Biesiadecki J J. Overview of the Mars exploration rovers’ autonomous mobility and vision capabilities. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Space Robotics Workshop, Roma, Italy, 2007

    Google Scholar 

  16. Montferrer A, Bonyuet D. Cooperative robot teleoperation through virtual reality interfaces. In: Proceedings of the Sixth International Conference on Information Visualization, London, 2002. 243–248

    Google Scholar 

  17. Backes P G, Tharp G K, Tso K S. The Web Interface for Telescience (WITS). In: Proceedings of the IEEE International Conference on Robotics and Automation, Albuquerque, NM, USA: IEEE, 1997. 411–417

    Google Scholar 

  18. Wright J R, Hartman F R, Cooper B K, et al. Driving on Mars with RSVP: building safe and effective command sequences. IEEE Robot Autom Mag, 2006; 13: 37–45

    Google Scholar 

  19. Young K. Mars rover escapes from the “Bay of Lamentation”. 2006. http://www.newscientist.com/article/dn9286-mars-rover-escapes-fromthe-bay-of-lamentation.html

  20. Ding L, Gao H B, Deng Z Q, et al. Design of comprehensive highfidelity/ high-speed virtual simulation system for lunar rover. In: Proc. IEEE Conference on Robotics, Automation and Mechatronics, Chengdu, China, 2008

    Google Scholar 

  21. Dvorak D, Bollella G, Canham T, et al. Project golden gate: towards real-time Java in space missions. In: Proceedings of the Seventh IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, Vienna, Austria: IEEE, 2004. 15–22

    Google Scholar 

  22. JPL. Mars exploration rovers objectives. http://marsrover.nasa.gov/science/objectives.html

  23. Blake D F, Morris R V, Kocurek G, et al. Curiosity at Gale Crater, Mars: Characterization and analysis of the rocknest sand shadow. Science, 2013, 341: 1239505

    Google Scholar 

  24. Wikipedia. Curiosity (rover). http://en.wikipedia.org/wiki/Curiosity_ (rover)#cite_note-MSLUSAToday-16

  25. JPL. Mars science laboratory contribution to Mars exploration program science goals. http://mars.jpl.nasa.gov/msl/mission/science/goals/

  26. ESA. Scientific objectives of the ExoMars Rover. http://exploration. esa.int/science-e/www/object/index.cfm?fobjectid=45082

  27. Neal C R. The Moon 35_years after Apollo: What’s left to learn? Chem Erde-Geochem, 2009; 69: 3–43

    Google Scholar 

  28. Tanaka S, Mitani T, Iijima Y, et al. The science objectives of Japanese Lunar Lander Project SELENE-II. In: Proceedings of the 42nd Lunar and Planetary Science Conference, The Woodlands, TX, USA, 2011

    Google Scholar 

  29. Harbin Institute of Technology (HIT). The Lunar Rover prototype exhibited in Zhuhai Airshow, the locomotion system of which was developed by HIT. 2006. http://today.hit.edu.cn/articles/2006/11-08/11132413.htm

    Google Scholar 

  30. Baumgartner E, Bonitz R, Melko J, et al. The Mars exploration rover instrument positioning system. In: Proceedings of the 2005 IEEE Aerospace Conference, Big Sky, MT, USA: IEEE, 2005. 1–19

    Google Scholar 

  31. Leger C C, Trebi-Ollennu A, Wright J R, et al. Mars Exploration Rover surface operations: driving spirit at Gusev Crater. In: Proceedings of the 2005 IEEE International Conference on Systems, Man and Cybernetics, Big Sky, MT, USA: IEEE, 2005. 1815–1822

    Google Scholar 

  32. JPL. MSL Science corner. http://msl-scicorner. jpl.nasa. gov/

  33. Volpe R, Nesnas I, Estlin T, et al. The CLARAty architecture for robotic autonomy. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA: IEEE, 2001. 1121–1132

    Google Scholar 

  34. Nesnas I A D, Reid S, Danie G, et al. CLARAty: Challenges and steps toward reusable robotic software. Int J Adv Robot Syst, 2006; 3: 23–30

    Google Scholar 

  35. Hartman F R, Cooper B, Leger C, et al. Data visualization for effective rover sequencing. In: Proceedings of the 2005 IEEE Aerospace Conference, Big Sky, MT, USA: IEEE, 2005. 1378–1383

    Google Scholar 

  36. Yen J, Jain A, Balaram J. ROAMS: Rover Analysis Modeling and Simulation software. In: Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, ESTEC, Noordwijk, the Netherlands, 1999

    Google Scholar 

  37. Volpe R. Rover functional autonomy development for the Mars mobile science laboratory. In: Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, USA: IEEE, 2003. 643–652

    Google Scholar 

  38. Golombek M P, Anderson R C, Barnes J R. Overview of the Mars Pathfinder mission: launch through landing, surface operations, data sheets, and science results. J Geophys Res, 1999; 104: 8523–8553

    Google Scholar 

  39. Richard V M, Steven W R, Ralf G, et al. Identification of Carbonate-rich outcrops on Mars by the Spirit rover. Science, 2010; 329: 421–424

    Google Scholar 

  40. Squyres S W, Knoll A H, Arvidson R E, et al. Two years at Meridiani Planum: Results from the Opportunity rover. Science, 2006; 313: 1403–1407

    Google Scholar 

  41. Herkenhoff K E, Squyres S W, Arvidson R E, et al. Evidence from Opportunity’s microscopic imager for water on meridiani planum. Science, 2004; 306: 1727–1730

    Google Scholar 

  42. Bell III J F, Squyres S W, Arvidson R E, et al. Pancam multispectral imaging results from the Spirit Rover at Gusev Crater. Science, 2004; 305: 800–806

    Google Scholar 

  43. Golombek M P, Arvidson R E, Bell III J F, et al. Assessment of Mars exploration rover landing site predictions. Nature, 2005; 436: 44–48

    Google Scholar 

  44. Rover Team. Characterization of the Martian surface deposits by the Mars Pathfinder rover, Sojourner. Science, 1997; 278: 1765–1767

    Google Scholar 

  45. Fergason R L, Christensen P R, Bell III J F, et al. Physical properties of the Mars exploration rover landing sites as inferred from Mini-TES-derived thermal inertia. J Geophys Res, 2006; 111: 1–18

    Google Scholar 

  46. Arvidson R E, Anderson R C, Bell III J F, et al. Localization and physical properties experiments conducted by Opportunity at Meridiani Planum. Science, 2004; 306: 1730–1733

    Google Scholar 

  47. Arvidson R E, Anderson R C, Bartlett P, et al. Localization and physical properties experiments conducted by Spirit at Gusev Crater. Science, 2004; 305: 821–824

    Google Scholar 

  48. Biesiadecki J J, Baumgartner E T, Bonitz R G, et al. Mars exploration rover surface operations: Driving Opportunity at Meridiani Planum. IEEE Robot Autom Mag, 2006; 13: 63–71

    Google Scholar 

  49. JPL. User interfaces. http://www-robotics.jpl. nasa.gov/applications/applicationArea.cfm?App=11

  50. Alami R, Chatila R, Fleury S, et al. An architecture for autonomy. Int J Robot Res, 1998; 17: 315–337

    Google Scholar 

  51. Estlin T, Gaines D, Bornstein B, et al. Supporting increased autonomy for a Mars rover. In: Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, Hollywood, USA, 2008

    Google Scholar 

  52. Ingrand F, Lacroix S, Lemai-Chenevier S, et al. Decisional autonomy of planetary rovers. J Field Robot, 2007; 24: 559–580

    Google Scholar 

  53. Volpe R, Nesnas I, Estlin T, et al. CLARAty: Coupled Layer Architecture for Robotic Autonomy. NASA, Jet Propulsion Laboratory, Pasadena, CA, USA, Technical Report D–19975, 2000

    Google Scholar 

  54. Ojeda L, Cruz D, Reina G, et al. Current-based slippage detection and odometry correction for mobile robots and planetary rovers. IEEE T Robot, 2006; 22: 366–378

    Google Scholar 

  55. Lutz D. New Mars rover’s mechanics to be used to study Martian soil properties. 2012. http://news.wustl.edu/news/pages/ 23139.aspx

    Google Scholar 

  56. Iagnemma K. Terrain estimation methods for enhanced autonomous rover mobility. In: Intelligence for Space Robotics. Howard A M, Tunstel E W, eds. San Antonio, TX, USA, 2006

    Google Scholar 

  57. Iagnemma K, Kang S, Shibly H, et al. Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers. IEEE T Robot, 2004; 20: 921–927

    Google Scholar 

  58. Ray L E. Estimation of terrain forces and parameters for rigidwheeled vehicles. IEEE T Robot, 2009; 25: 717–726

    Google Scholar 

  59. Ding L, Yoshida K, Nagatani K, et al. Parameter identification for planetary soil based on a decoupled analytical wheel-soil interaction terramechanics model. In: Proceedings of the IEEE/RSJ Int. Conf. Intelligent Robots and Systems, St. Louis, MO, USA: IEEE, 2009. 4122–4127

    Google Scholar 

  60. Ding L, Gao H, Deng Z, et al. An approach of identifying mechanical parameters for lunar soil based on integrated wheel-soil interaction terramechanics model of rovers (in Chinese). Acta Aeronautica Ast, 2011; 32: 1112–1123

    Google Scholar 

  61. Dumond D. Terrain Classification using proprioceptive sensors. Dissertation of Doctor Degree. Hanover, NH, USA: Dartmouth College, 2011

    Google Scholar 

  62. Brooks C A, Iagnemma K. Vibration-based terrain classification for planetary exploration rovers. IEEE T Robot, 2005; 21: 1185–1191

    Google Scholar 

  63. Angelova A, Matthies L, Helmick D, et al. Learning and prediction of slip from visual information. J Field Robot, 2007; 24: 205–231

    Google Scholar 

  64. Leonard J J, Durrant-Whyte H F. Simultaneous map building and localization for an autonomous mobile robot. In: Proceedings of the IEEE/RSJ International Workshop on Intelligent Robots and Systems, Osaka, Japan: IEEE, 1991. 1442–1447

    Google Scholar 

  65. Durrant-Whyte H F, Bailey T. Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag, 2006; 13: 99–110

    Google Scholar 

  66. Dissanayake M W M G, Newman P, Clark S, et al. A solution to the simultaneous localisation and map building (SLAM) problem. IEEE T Robotic Autom, 2006; 17: 229–241

    Google Scholar 

  67. Davison A J, Kita N. 3D simultaneous localization and map-building using active vision for a robot moving on undulating terrain. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA: IEEE, 2011

    Google Scholar 

  68. Davison A J. Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, 2003. 384–391

    Google Scholar 

  69. Matthies L, Maimone M, Johnson A, et al. Computer vision on Mars. Int J Comput Vision, 2007; 75: 67–92

    Google Scholar 

  70. Cheng Y, Maimone M W, Matthies L. Visual odometry on the Mars exploration rovers-a tool to ensure accurate driving and science imaging. IEEE Robot Autom Mag, 2006; 13: 54–62

    Google Scholar 

  71. Maimone M, Cheng Y, Matthies L. Two years of visual odometry on the Mars exploration rovers. J Field Robot, 2007; 24: 169–186

    Google Scholar 

  72. Brooks C A, Iagnemma K. Self-supervised terrain classification for planetary surface exploration rovers. J Field Robot, 2012; 29: 445–468

    Google Scholar 

  73. Ojeda L, Borenstein J, Witus G, et al. Terrain characterization and classification with a mobile robot. J Field Robot, 2006; 23: 103–122

    MATH  Google Scholar 

  74. Gennery D B. Traversability analysis and path planning for a planetary rover. Auton Robot, 1999; 6: 131–146

    Google Scholar 

  75. Lacroix S, Mallet A, Bonnafous D. Autonomous rover navigation on unknown terrains: functions and integration. Int J Robot Res, 2002; 21: 917–942

    Google Scholar 

  76. Chhaniyara S, Brunskill C, Yeomans B, et al. Terrain trafficability analysis and soil mechanical property identification for planetary rovers: a survey. J Terramechnics, 2012; 49: 115–128

    Google Scholar 

  77. Iagnemma K, Genot F, Dubowsky S. Rapid physics-based roughterrain rover planning with sensor and control uncertainty. In: Proc. IEEE International Conference on Robotics and Automation, Detroit, MI, USA, 1999

    Google Scholar 

  78. Ishigami G, Nagatani K, Yoshida K. Path planning for planetary exploration rovers and its evaluation based on wheel slip dynamics. In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, IEEE: 2007. 2361–2366

    Google Scholar 

  79. Howard T M, Kelly A. Optimal rough terrain trajectory generation for wheeled mobile robots. Int J Robot Res, 2007; 26: 141–166

    Google Scholar 

  80. Kim J H, Kim Y H, Choi S H, et al. Evolutionary multi-objective optimization in robot soccer system for education. IEEE Comput Intell M, 2009; 4: 31–41

    Google Scholar 

  81. Tarokh M. Hybrid intelligent path planning for articulated rovers in rough terrain. Fuzzy Set Syst, 2008; 159: 2927–2937

    MathSciNet  Google Scholar 

  82. Noguchi N, Terao H. Path planning of an agricultural mobile robot by neural network and genetic algorithm. Comput Electron Agr, 1997; 18: 187–204

    Google Scholar 

  83. Kolmanovsky I, McClamroch N H. Development in nonholonomic control problems. IEEE Contr Syst Mag, 1995; 15: 20–36

    Google Scholar 

  84. Morin P, Samson C. Control of nonholonomic mobile robots based on the transverse function approach. IEEE T Robot, 2009; 25: 1058–1073

    Google Scholar 

  85. Iagnemma K, Shibly H, Rzepniewski A, et al. Planning and control algorithms for enhanced rough-terrain rover mobility. In: Proceedings of the 6th International Symposium on Artificial Intelligence and Robotics & Automation in Space, St-Hubert, Quebec, Canada, 2001

    Google Scholar 

  86. Ishigami G, Miwa A, Nagatani K, et al. Terramechanics-based model for steering maneuver of planetary exploration rovers on loose soil. J Field Robot, 2007; 24: 233–250

    Google Scholar 

  87. Ding L, Gao H B, Deng Z Q, et al. Path-following control of wheeled planetary exploration robots moving on deformable rough terrain. Sci World J, 2014, http://dx.doi.org/10.1155/2014/793526

    Google Scholar 

  88. Helmick D M, Cheng Y, Clouse D, et al. Path following using visual odometry for a Mars rover in high-slip environments. In: Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA: IEEE, 2004. 772–789

    Google Scholar 

  89. Helmick D M, Roumeliotis S I, Cheng Y, et al. Slip-compensated path following for planetary exploration rovers. Adv Robotics, 2006; 20: 1257–1280

    Google Scholar 

  90. Ishigami G, Nagatani K, Yoshida K. Path following control with slip compensation on loose soil for exploration rover. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China: IEEE, 2006. 5552–5557

    Google Scholar 

  91. Ding L, Gao H B, Deng Z Q, et al. Slip-ratio-coordinated control of planetary exploration robots traversing over deformable rough terrain. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, China: IEEE, 2010. 4958–4963

    Google Scholar 

  92. Ding L. Wheel-soil interaction terramechanics for lunar/planetary exploration rovers: Modeling and application (in Chinese). Dissertation of Doctor Degree. Harbin: Harbin Institute of Technology, Harbin, 2009

    Google Scholar 

  93. Xia K. Research on tracking control of mobile robot based on wheel-soil interaction modeling (in Chinese). Master dissertation, School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, China, 2009

    Google Scholar 

  94. Wang D W, Low C B. Modeling and analysis of skidding and slipping in wheeled mobile robots: control design perspective. IEEE T Robot, 2008; 24: 676–687

    Google Scholar 

  95. Ding L, Gao H B, Guo J L, et al. Terramechanics-based analysis of slipping and skidding for wheeled mobile robots. In: Proceedings of the 31st Chinese Control Conference, Heifei, China: IEEE, 2012. 4966–4973

    Google Scholar 

  96. Ding L, Gao H B, Deng Z Q, et al. Advances in simulation of planetary wheeled mobile robots. In: Mobile Robots-Current Trends. Gacovski Z, ed. Rijeka, Croatia: In Tech Press, 2011. 375–402

    Google Scholar 

  97. Yen J, Jain A, Balaram J. ROAMS: Rover analysis modeling and simulation software. In: Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space, ESTEC, Noordwijk, the Netherlands, 1999

    Google Scholar 

  98. Estlin T, Gaines D, Bornstein B, et al. Supporting increased autonomy for a Mars rover. In: Proceedings of the International Symposium on Artifcial Intelligence, Robotics and Automation in Space, Hollywood, USA, 2008

    Google Scholar 

  99. Zhou F, Arvidson R E, Bennett K, et al. Simulations of Mars rover traverses. J Field Robot, 2014; 31: 141–160

    Google Scholar 

  100. Schäfer B, Gibbesch A, Krenn R, et al. Planetary rover mobility simulation on soft and uneven terrain. Vehicle Syst Dyn, 2010; 48: 149–169

    Google Scholar 

  101. Ding L, Nagatani K, Sato K, et al. Terramechanics-based high-fidelity dynamics simulation for wheeled mobile robot on deformable rough terrain. In: Proceedings of the IEEE International Conference on Robotics and Automation, Anchorage, Alaska, USA: IEEE, 2010. 4922–4927

    Google Scholar 

  102. Wright J, Hartman F R, Cooper B, et al. Terrain modeling for immersive visualization for the Mars exploration rovers. In: Proceedings of the SpaceOps, Montreal, Canada, 2004

    Google Scholar 

  103. Leger P C, Deen R G, Bonitz R G. Remote image analysis for Mars exploration rover mobility and manipulation operations. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Hawaii, USA, IEEE: 2005. 917–922

    Google Scholar 

  104. Griffiths A D, Coates A J, Jaumann R, et. al. Context for the ESA ExoMars rover: the Panoramic Camera (PanCam) instrument. Int J Astrobiol, 2006; 5: 269–275

    Google Scholar 

  105. Rekleitis I, Bedwani J L, Dupuis E. Autonomous planetary exploration using LiDAR data. In: Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, Japan, IEEE, 2009. 3025–3030

    Google Scholar 

  106. Carle P J F, Furgale P T, Barfoot T D. Long-range rover localization by matching LIDAR scans to orbital elevation maps. J Field Robot, 2010; 27: 344–370

    Google Scholar 

  107. Yoshida K. The SpaceDyn: A MATLAB toolbox for space and mobile robots. JRM, 2000; 12: 411–416

    Google Scholar 

  108. Ding L, Deng Z Q, Gao H B, et al. Experimental study and analysis of the wheels’ steering mechanics for planetary exploration WMRs moving on deformable terrain. Int J Robot Res, 2013; 32: 712–743

    Google Scholar 

  109. Bekker M G. Introduction to Terrain-Vehicle. Ann Arbor, MI, USA: The University of Michigan Press, 1969

    Google Scholar 

  110. Wong J Y. Terramechanics and Off-Road Vehicle Engineering. 2nd ed. Oxford, England: Elsevier, 2010

    Google Scholar 

  111. Ding L, Deng Z Q, Gao H B, et al. Planetary rovers’ wheel–soil interaction mechanics: new challenges and applications for wheeled mobile robots. Int Serv Robot, 2011; 4: 17–38

    Google Scholar 

  112. Ding L, Gao H B, Deng Z Q, et al. Experimental study and analysis on driving wheels’ performance for planetary exploration rovers moving in deformable soil. J Terramechnics, 2010; 48: 27–45

    Google Scholar 

  113. Ding L, Gao H B, Deng Z Q, et al. Wheel slip-sinkage and its prediction model of lunar rover. J Cent South Univ T, 2010; 17: 129–135

    Google Scholar 

  114. Ding L, Deng Z Q, Gao H B, et al. Interaction mechanics model for rigid driving wheels of planetary rovers moving on sandy terrain with consideration of multiple physical effects. J Field Robot, 2014, doi: 10.1002/rob.21533

    Google Scholar 

  115. Meirion-Griffith G, Spenko M. A modified pressure–sinkage model for small, rigid wheels on deformable terrains. J Terramechnics, 2011; 48: 149–155

    Google Scholar 

  116. Irani R A, Bauer R J, Warkentin A. A dynamic terramechanic model for small lightweight vehicles with rigid wheels and grousers operating in sandy soil. J Terramechnics, 2011; 48: 307–318

    Google Scholar 

  117. Gao H B, Guo J L, Ding L, et al. Longitudinal skid model for wheels of planetary exploration rovers based on terramechanics. J Terramechnics, 2013; 50: 327–343

    Google Scholar 

  118. Ding L, Gao H B, Deng Z Q, et al. Longitudinal slip versus skid of planetary rovers’ wheels traversing on deformable slopes. In: IEEE/ RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 2013, 2842–2848

    Google Scholar 

  119. Shibly H, Iagnemma K, Dubowsky S. An equivalent soil mechanics formulation for rigid wheels in deformable terrain, with application to planetary exploration rovers. J Terramechnics, 2005; 42: 1–13

    Google Scholar 

  120. Ding L, Gao H B, Li Y K, et al. Improved explicit-form equations for estimating dynamic wheel sinkage and compaction resistance on deformable terrain. Mech Mach Theory, 2015: 235–264

    Google Scholar 

  121. Guo S P, Li D X, Meng Y H, et al. Task space control of free-floating space robots using constrained adaptive RBF-NTSM. Sci China Tech Sci, 2014; 57: 828–837

    Google Scholar 

  122. Zhuang H C, Gao H B, Deng Z Q, et al. A review of heavy-duty legged robots. Sci China Tech Sci, 2014; 57: 298–314

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Liang Ding or HaiBo Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ding, L., Gao, H., Deng, Z. et al. Three-layer intelligence of planetary exploration wheeled mobile robots: Robint, virtint, and humint. Sci. China Technol. Sci. 58, 1299–1317 (2015). https://doi.org/10.1007/s11431-015-5853-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-015-5853-9

Keywords

Navigation