Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing
This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equip...
Saved in:
| Published in: | Robotics and autonomous systems Vol. 105; pp. 85 - 93 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01-07-2018
Elsevier |
| Subjects: | |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms.
•Slippage prediction associated with individual wheels in off-road mobile robots.•Machine learning regression algorithms considering proprioceptive sensing.•Gaussian process regression results in the best accuracy. It also returns the variance associated with each prediction.•This methodology will be exploited by the layers: path planning and motion control. |
|---|---|
| AbstractList | This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms. This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning regression algorithms are trained considering proprioceptive sensing. This contribution is validated by using the MIT single-wheel testbed equipped with an MSL spare wheel. The combination of IMU-related and torque-related features outperforms the torque-related features only. Gaussian process regression results in a proper trade-off between accuracy and computation time. Another advantage of this algorithm is that it returns the variance associated with each prediction, which might be used for future route planning and control tasks. The paper also provides a comparison between machine learning regression and classification algorithms. •Slippage prediction associated with individual wheels in off-road mobile robots.•Machine learning regression algorithms considering proprioceptive sensing.•Gaussian process regression results in the best accuracy. It also returns the variance associated with each prediction.•This methodology will be exploited by the layers: path planning and motion control. |
| Author | Iagnemma, Karl Fiacchini, Mirko Gonzalez, Ramon |
| Author_xml | – sequence: 1 givenname: Ramon surname: Gonzalez fullname: Gonzalez, Ramon email: ramon@robonity.com organization: robonity: tech consulting, Calle Extremadura, no. 5, 04740, Almeria, Spain – sequence: 2 givenname: Mirko orcidid: 0000-0002-3601-0302 surname: Fiacchini fullname: Fiacchini, Mirko email: mirko.fiacchini@gipsa-lab.grenoble-inp.fr organization: Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France – sequence: 3 givenname: Karl surname: Iagnemma fullname: Iagnemma, Karl email: kdi@mit.edu organization: Massachusetts Institute of Technology, 77 Massachusetts Av., 02139 Cambridge, MA, USA |
| BackLink | https://hal.univ-grenoble-alpes.fr/hal-01904487$$DView record in HAL |
| BookMark | eNp9kM1LxDAQxYMouH78BV5y9dA6mbTb5uBBxC9Y8KCCt5Cm0zVLNylJWfC_t90Vj54Ghvd7M--dsWMfPDF2JSAXIJY3mzyGJow5gqhzkDkIecQWoq4wq5T8PGYLUCiyulZwys5S2gCALCu5YO6td8Ng1sSHSK2zowuedyHy0HVZDKbl29C4nvj-QOI7Z_jW2C_nifdkond-zSOtI6U0o8a3k1MYoguWhtHtiCfyaVJdsJPO9Ikuf-c5-3h8eL9_zlavTy_3d6vMyhrHrERsjYBy2ZSqQ1TUFI1BBMQS56eByrZQVd2hVYbIKjS1gCWWtiyWFSp5zq4Pvl-m19MfWxO_dTBOP9-t9LwDoaAo6monJq08aG0MKUXq_gABem5Wb_Q-uJ6b1SAnWE7U7YGiKcbOUdTJOvJ26i-SHXUb3L_8D0MAhXI |
| CitedBy_id | crossref_primary_10_1007_s42452_021_04453_3 crossref_primary_10_3390_land10060648 crossref_primary_10_1017_S0263574721000035 crossref_primary_10_1186_s40648_021_00215_3 crossref_primary_10_1016_j_pss_2021_105271 crossref_primary_10_1007_s10846_023_01968_2 crossref_primary_10_1016_j_jterra_2024_100972 crossref_primary_10_3389_fnbot_2020_620378 crossref_primary_10_1016_j_compag_2019_105176 crossref_primary_10_1109_TITS_2023_3344799 crossref_primary_10_1115_1_4051016 crossref_primary_10_3390_app12031676 crossref_primary_10_3390_s23136127 crossref_primary_10_3389_frspt_2022_1080291 crossref_primary_10_1016_j_micpro_2021_104310 crossref_primary_10_1002_rob_22252 crossref_primary_10_1016_j_jterra_2021_04_005 crossref_primary_10_1016_j_cogsys_2019_09_006 crossref_primary_10_1002_rob_22047 crossref_primary_10_1007_s13369_022_07371_7 crossref_primary_10_1007_s41315_020_00142_3 crossref_primary_10_1016_j_biosystemseng_2022_04_020 crossref_primary_10_3390_robotics13010012 crossref_primary_10_1109_TITS_2021_3097385 crossref_primary_10_1016_j_robot_2019_05_007 crossref_primary_10_15446_dyna_v90n225_105688 crossref_primary_10_3390_s22031062 crossref_primary_10_1016_j_atech_2022_100131 crossref_primary_10_1007_s11370_023_00504_3 crossref_primary_10_1016_j_jterra_2021_11_003 crossref_primary_10_1002_rob_22141 crossref_primary_10_1017_S026357472300187X crossref_primary_10_3390_app12094789 |
| Cites_doi | 10.1016/j.robot.2017.05.014 10.1016/j.jterra.2016.10.001 10.1023/B:AURO.0000047286.62481.1d 10.1109/70.388775 10.1109/ICRA.2016.7487413 10.1177/0278364910370241 10.1002/rob.20179 10.1016/j.jterra.2017.09.001 10.1002/rob.21736 10.1007/s10514-008-9105-8 10.1023/B:STCO.0000035301.49549.88 10.1016/j.robot.2011.05.006 10.1007/s10514-015-9527-z 10.1007/s11263-007-0046-z |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. Distributed under a Creative Commons Attribution 4.0 International License |
| Copyright_xml | – notice: 2018 Elsevier B.V. – notice: Distributed under a Creative Commons Attribution 4.0 International License |
| DBID | AAYXX CITATION 1XC VOOES |
| DOI | 10.1016/j.robot.2018.03.013 |
| DatabaseName | CrossRef Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1872-793X |
| EndPage | 93 |
| ExternalDocumentID | oai_HAL_hal_01904487v1 10_1016_j_robot_2018_03_013 S0921889018300174 |
| GroupedDBID | --K --M -~X .~1 0R~ 123 1B1 1~. 1~5 29P 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AAXUO AAYFN ABBOA ABFNM ABFRF ABIVO ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACNNM ACRLP ACZNC ADBBV ADEZE ADJOM ADMUD ADTZH AEBSH AECPX AEFWE AEKER AENEX AFFNX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHJVU AHPGS AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD ASPBG AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ H~9 IHE J1W JJJVA KOM LG9 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ RXW SBC SCC SDF SDG SDP SES SET SEW SPC SPCBC SST SSV SSZ T5K TAE UNMZH WUQ XPP ~G- AAXKI AAYXX AFJKZ AKRWK CITATION 0SF 1XC VOOES |
| ID | FETCH-LOGICAL-c382t-522da1056b59f229eb4ba220225200350e5d4978f2c9aeec92a810625c5467293 |
| ISSN | 0921-8890 |
| IngestDate | Tue Oct 15 15:24:18 EDT 2024 Thu Nov 21 22:34:25 EST 2024 Fri Feb 23 02:33:49 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Gaussian process regression Inertial measurement unit (IMU) Machine learning regression Slip Mars science laboratory (MSL) wheel Mars Science Laboratory (MSL) wheel Gaussian Process Regression Machine Learning Regression Inertial Measurement Unit (IMU) |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c382t-522da1056b59f229eb4ba220225200350e5d4978f2c9aeec92a810625c5467293 |
| ORCID | 0000-0002-3601-0302 |
| OpenAccessLink | https://hal.univ-grenoble-alpes.fr/hal-01904487 |
| PageCount | 9 |
| ParticipantIDs | hal_primary_oai_HAL_hal_01904487v1 crossref_primary_10_1016_j_robot_2018_03_013 elsevier_sciencedirect_doi_10_1016_j_robot_2018_03_013 |
| PublicationCentury | 2000 |
| PublicationDate | July 2018 2018-07-00 2018-07 |
| PublicationDateYYYYMMDD | 2018-07-01 |
| PublicationDate_xml | – month: 07 year: 2018 text: July 2018 |
| PublicationDecade | 2010 |
| PublicationTitle | Robotics and autonomous systems |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V Elsevier |
| Publisher_xml | – name: Elsevier B.V – name: Elsevier |
| References | Angelova, Matthies, Helmick, Perona (b6) 2007; 24 S. Lee, R. Gonzalez, K. Iagnemma, Robust sampling-based motion planning for autonomous tracked vehicles in deformable high slip terrain, in: IEEE Int. Conf. on Robotics and Automation, ICRA, Stockholm, Sweden, 2016, pp. 2569–2574. G. Webster, D. Brown, Now a Stationary Research Platform. NASA’s Mars rover Spirit starts a new chapter in Red Planet Scientific Studies. Smola, Scholkopf (b22) 2004; 14 Bouguelia, Gonzalez, Iagnemma, Byttner (b11) 2017; 73 Barshan, Durrant-Whyte (b13) 1995; 11 Murphy (b24) 2012 Gonzalez, Iagnemma (b10) 2016 Manduchi, Castano, Talukder, Matthies (b3) 2005; 18 Karumanchi, Allen, Bailey, Scheding (b21) 2010; 29 Gonzalez, Fiacchini, Guzman, Alamo, Rodriguez (b25) 2011; 59 Gonzalez, Apostolopoulos, Iagnemma (b12) 2018; 35 Vapnik (b23) 1995 Maimone, Biesiadecki, Tunstel, Cheng, Leger (b2) 2006 Matthies, Maimone, Johnson, Cheng, Willson, Villalpando, Goldberg, Huertas (b4) 2007; 75 Wong (b8) 2001 LaValle (b1) 2006 Iagnemma, Dubowsky (b5) 2004 Ordonez, Gupta, Reese, Seegmiller, Kelly, Collins (b16) 2017; 95 Gonzalez, Jayakumar, Iagnemma (b19) 2017; 69 Gonzalez, Iagnemma (b7) 2017 Marsland (b17) 2015 Gonzalez, Jayakumar, Iagnemma (b20) 2017; 41 Iagnemma, Ward (b14) 2009; 26 Gonzalez, Rodriguez, Guzman (b15) 2014 Rasmussen, Williams (b18) 2006 Gonzalez (10.1016/j.robot.2018.03.013_b10) 2016 Gonzalez (10.1016/j.robot.2018.03.013_b15) 2014 LaValle (10.1016/j.robot.2018.03.013_b1) 2006 Murphy (10.1016/j.robot.2018.03.013_b24) 2012 Angelova (10.1016/j.robot.2018.03.013_b6) 2007; 24 Bouguelia (10.1016/j.robot.2018.03.013_b11) 2017; 73 Karumanchi (10.1016/j.robot.2018.03.013_b21) 2010; 29 Wong (10.1016/j.robot.2018.03.013_b8) 2001 Marsland (10.1016/j.robot.2018.03.013_b17) 2015 Gonzalez (10.1016/j.robot.2018.03.013_b7) 2017 Ordonez (10.1016/j.robot.2018.03.013_b16) 2017; 95 Vapnik (10.1016/j.robot.2018.03.013_b23) 1995 Maimone (10.1016/j.robot.2018.03.013_b2) 2006 Gonzalez (10.1016/j.robot.2018.03.013_b19) 2017; 69 Gonzalez (10.1016/j.robot.2018.03.013_b25) 2011; 59 Matthies (10.1016/j.robot.2018.03.013_b4) 2007; 75 Smola (10.1016/j.robot.2018.03.013_b22) 2004; 14 Manduchi (10.1016/j.robot.2018.03.013_b3) 2005; 18 10.1016/j.robot.2018.03.013_b9 Gonzalez (10.1016/j.robot.2018.03.013_b20) 2017; 41 Gonzalez (10.1016/j.robot.2018.03.013_b12) 2018; 35 Barshan (10.1016/j.robot.2018.03.013_b13) 1995; 11 Iagnemma (10.1016/j.robot.2018.03.013_b14) 2009; 26 Rasmussen (10.1016/j.robot.2018.03.013_b18) 2006 10.1016/j.robot.2018.03.013_b26 Iagnemma (10.1016/j.robot.2018.03.013_b5) 2004 |
| References_xml | – year: 2015 ident: b17 article-title: Machine Learning. An Algorithmic Perspective contributor: fullname: Marsland – volume: 24 start-page: 205 year: 2007 end-page: 231 ident: b6 article-title: Learning and prediction of slip from visual information publication-title: J. Field Robot. contributor: fullname: Perona – year: 2014 ident: b15 publication-title: Autonomous Tracked Robots in Planar Off-Road Conditions. Modelling, Localization and Motion Control contributor: fullname: Guzman – volume: 41 start-page: 311 year: 2017 end-page: 331 ident: b20 article-title: Stochastic mobility prediction of ground vehicles over large spatial regions: A geostatistical approach publication-title: Auton. Robots contributor: fullname: Iagnemma – year: 2004 ident: b5 publication-title: Mobile Robots in Rough Terrain. Estimation, Motion Planning, and Control with Application to Planetary Rovers contributor: fullname: Dubowsky – volume: 95 start-page: 207 year: 2017 end-page: 221 ident: b16 article-title: Learning of skid-steered kinematic and dynamic models for motion planning publication-title: Robot. Auton. Syst. contributor: fullname: Collins – volume: 69 start-page: 1 year: 2017 end-page: 11 ident: b19 article-title: Generation of stochastic mobility maps for large-scale route planning of ground vehicles: a case study publication-title: J. Terramech. contributor: fullname: Iagnemma – year: 1995 ident: b23 article-title: The Nature of Statistical Learning Theory contributor: fullname: Vapnik – volume: 73 year: 2017 ident: b11 article-title: Unsupervised detection of soil embedding events for planetary exploration rovers publication-title: J. Terramech. contributor: fullname: Byttner – volume: 75 start-page: 67 year: 2007 end-page: 92 ident: b4 article-title: Computer vision on mars publication-title: Int. J. Comput. Vis. contributor: fullname: Huertas – year: 2012 ident: b24 article-title: Machine Learning: A Probabilistic Perspective contributor: fullname: Murphy – volume: 29 start-page: 997 year: 2010 end-page: 1018 ident: b21 article-title: Non-parametric learning to aid path planning over slopes publication-title: Int. J. Robot. Res. contributor: fullname: Scheding – volume: 11 start-page: 328 year: 1995 end-page: 342 ident: b13 article-title: Inertial navigation systems for mobile robots publication-title: IEEE Trans. Robot. Autom. contributor: fullname: Durrant-Whyte – volume: 18 start-page: 81 year: 2005 end-page: 102 ident: b3 article-title: Obstacle detection and terrain classification for autonomous off-road navigation publication-title: Auton. Robots contributor: fullname: Matthies – volume: 26 start-page: 33 year: 2009 end-page: 46 ident: b14 article-title: Classification–based wheel slip detection and detector fusion for mobile robots on outdoor terrain publication-title: Auton. Robots contributor: fullname: Ward – year: 2001 ident: b8 article-title: Theory of Ground Vehicles contributor: fullname: Wong – start-page: 1 year: 2017 end-page: 14 ident: b7 article-title: Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges publication-title: J. Field Robot. contributor: fullname: Iagnemma – year: 2006 ident: b18 publication-title: Gaussian Processes for Machine Learning contributor: fullname: Williams – volume: 35 start-page: 231 year: 2018 end-page: 247 ident: b12 article-title: Slippage and immobilization detection for planetary exploration rovers via machine learning and proprioceptive sensing publication-title: J. Field Robot. contributor: fullname: Iagnemma – start-page: 45 year: 2006 end-page: 69 ident: b2 article-title: Surface navigation and mobility intelligence on the Mars Exploration Rovers publication-title: Intelligence for Space Robotics contributor: fullname: Leger – volume: 59 start-page: 711 year: 2011 end-page: 726 ident: b25 article-title: Robust tube-based predictive control for mobile robots in off-road conditions publication-title: Robot. Auton. Syst. contributor: fullname: Rodriguez – volume: 14 start-page: 199 year: 2004 end-page: 222 ident: b22 article-title: A tutorial on support vector regression publication-title: Stat. Comput. contributor: fullname: Scholkopf – year: 2006 ident: b1 article-title: Planning Algorithms contributor: fullname: LaValle – year: 2016 ident: b10 article-title: Soil embedding avoidance for planetary exploration rovers publication-title: 8th ISTVS Amercias Conference contributor: fullname: Iagnemma – year: 2004 ident: 10.1016/j.robot.2018.03.013_b5 contributor: fullname: Iagnemma – year: 2001 ident: 10.1016/j.robot.2018.03.013_b8 contributor: fullname: Wong – volume: 95 start-page: 207 year: 2017 ident: 10.1016/j.robot.2018.03.013_b16 article-title: Learning of skid-steered kinematic and dynamic models for motion planning publication-title: Robot. Auton. Syst. doi: 10.1016/j.robot.2017.05.014 contributor: fullname: Ordonez – volume: 69 start-page: 1 year: 2017 ident: 10.1016/j.robot.2018.03.013_b19 article-title: Generation of stochastic mobility maps for large-scale route planning of ground vehicles: a case study publication-title: J. Terramech. doi: 10.1016/j.jterra.2016.10.001 contributor: fullname: Gonzalez – volume: 18 start-page: 81 year: 2005 ident: 10.1016/j.robot.2018.03.013_b3 article-title: Obstacle detection and terrain classification for autonomous off-road navigation publication-title: Auton. Robots doi: 10.1023/B:AURO.0000047286.62481.1d contributor: fullname: Manduchi – year: 2014 ident: 10.1016/j.robot.2018.03.013_b15 contributor: fullname: Gonzalez – year: 2006 ident: 10.1016/j.robot.2018.03.013_b1 contributor: fullname: LaValle – volume: 11 start-page: 328 year: 1995 ident: 10.1016/j.robot.2018.03.013_b13 article-title: Inertial navigation systems for mobile robots publication-title: IEEE Trans. Robot. Autom. doi: 10.1109/70.388775 contributor: fullname: Barshan – year: 1995 ident: 10.1016/j.robot.2018.03.013_b23 contributor: fullname: Vapnik – ident: 10.1016/j.robot.2018.03.013_b26 doi: 10.1109/ICRA.2016.7487413 – volume: 29 start-page: 997 year: 2010 ident: 10.1016/j.robot.2018.03.013_b21 article-title: Non-parametric learning to aid path planning over slopes publication-title: Int. J. Robot. Res. doi: 10.1177/0278364910370241 contributor: fullname: Karumanchi – volume: 24 start-page: 205 year: 2007 ident: 10.1016/j.robot.2018.03.013_b6 article-title: Learning and prediction of slip from visual information publication-title: J. Field Robot. doi: 10.1002/rob.20179 contributor: fullname: Angelova – start-page: 1 year: 2017 ident: 10.1016/j.robot.2018.03.013_b7 article-title: Slippage estimation and compensation for planetary exploration rovers. State of the art and future challenges publication-title: J. Field Robot. contributor: fullname: Gonzalez – year: 2016 ident: 10.1016/j.robot.2018.03.013_b10 article-title: Soil embedding avoidance for planetary exploration rovers contributor: fullname: Gonzalez – volume: 73 year: 2017 ident: 10.1016/j.robot.2018.03.013_b11 article-title: Unsupervised detection of soil embedding events for planetary exploration rovers publication-title: J. Terramech. doi: 10.1016/j.jterra.2017.09.001 contributor: fullname: Bouguelia – ident: 10.1016/j.robot.2018.03.013_b9 – volume: 35 start-page: 231 year: 2018 ident: 10.1016/j.robot.2018.03.013_b12 article-title: Slippage and immobilization detection for planetary exploration rovers via machine learning and proprioceptive sensing publication-title: J. Field Robot. doi: 10.1002/rob.21736 contributor: fullname: Gonzalez – volume: 26 start-page: 33 year: 2009 ident: 10.1016/j.robot.2018.03.013_b14 article-title: Classification–based wheel slip detection and detector fusion for mobile robots on outdoor terrain publication-title: Auton. Robots doi: 10.1007/s10514-008-9105-8 contributor: fullname: Iagnemma – year: 2012 ident: 10.1016/j.robot.2018.03.013_b24 contributor: fullname: Murphy – year: 2015 ident: 10.1016/j.robot.2018.03.013_b17 contributor: fullname: Marsland – volume: 14 start-page: 199 year: 2004 ident: 10.1016/j.robot.2018.03.013_b22 article-title: A tutorial on support vector regression publication-title: Stat. Comput. doi: 10.1023/B:STCO.0000035301.49549.88 contributor: fullname: Smola – volume: 59 start-page: 711 year: 2011 ident: 10.1016/j.robot.2018.03.013_b25 article-title: Robust tube-based predictive control for mobile robots in off-road conditions publication-title: Robot. Auton. Syst. doi: 10.1016/j.robot.2011.05.006 contributor: fullname: Gonzalez – volume: 41 start-page: 311 year: 2017 ident: 10.1016/j.robot.2018.03.013_b20 article-title: Stochastic mobility prediction of ground vehicles over large spatial regions: A geostatistical approach publication-title: Auton. Robots doi: 10.1007/s10514-015-9527-z contributor: fullname: Gonzalez – start-page: 45 year: 2006 ident: 10.1016/j.robot.2018.03.013_b2 article-title: Surface navigation and mobility intelligence on the Mars Exploration Rovers contributor: fullname: Maimone – volume: 75 start-page: 67 year: 2007 ident: 10.1016/j.robot.2018.03.013_b4 article-title: Computer vision on mars publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-007-0046-z contributor: fullname: Matthies – year: 2006 ident: 10.1016/j.robot.2018.03.013_b18 contributor: fullname: Rasmussen |
| SSID | ssj0003573 |
| Score | 2.4464347 |
| Snippet | This paper presents a new approach for predicting slippage associated with individual wheels in off-road mobile robots. More specifically, machine learning... |
| SourceID | hal crossref elsevier |
| SourceType | Open Access Repository Aggregation Database Publisher |
| StartPage | 85 |
| SubjectTerms | Automatic Engineering Sciences Gaussian process regression Inertial measurement unit (IMU) Machine learning regression Mars science laboratory (MSL) wheel Slip |
| Title | Slippage prediction for off-road mobile robots via machine learning regression and proprioceptive sensing |
| URI | https://dx.doi.org/10.1016/j.robot.2018.03.013 https://hal.univ-grenoble-alpes.fr/hal-01904487 |
| Volume | 105 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://sdu.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lj9MwELba3QscEE-xvGQhbiGrxEka51hBUUGIA12kvUVO7JTsbpMqfRz49cz4kRQqECBxiSpLdVzP15mx9c03hLxKS1kFSsU-hJvUj6OK-5zL0o8KyE5DGZYhx0Lh-SL9dMnfzuLZaOSkKoax_2ppGANbY-XsX1i7nxQG4DPYHJ5gdXj-kd0XN_UanQRW_8u67KmEbVX5XSukt2oL8ARe1xbtduPta-GtNKFSuQ4SS69TS0OPbayOQLvu6lYTYPbK2yDn3Qa8XtobJnN6z2K3xUoJ5NZuDvTQtQh_8w0Ckr6z_oxtjnr01KLENdSGyd9dtz1kxbJRq5UrXLs5vKUIec9otVdnR-Uz5g6ShQAN0y_0XBkPzFNI-TPdJHhw0UFy4GRNjx8brk1_xaNAYO4krs71ZiKDj2stW1P3-pPC9gLXgcsA94ZROx6TUwZLALd5On0_u_zQh_YoMZQFt24nY6UJg0ev-lWqM_7qLu11EnNxl9yxpw86NbC5R0aquU9uH2hSPiC1AxAdAEQBQNQBiBoAUQMgCgCiFkDUAYgOAKKACPojgKgF0EPy5d3s4s3ct_04_DLibOtDqi4FmGJSJFnFWKaKuBCMQRaYaF3dQCUSGxZWrMyEUmXGBA8DOGCXCYRjyCsfkZOmbdRjQmVUyUCirBarYlkkXIg4U5MgSplSlUrPyGu3dfnayK7kjo94lesfmONO50GUw06fkYnb3txmjiYjzAEPv__iSzBG_wrUWp9PP-Y4hiILMRzn9-GTf539Kbk1_BeekZNtt1PPyXgjdy8ssr4DxxShpg |
| link.rule.ids | 230,315,782,786,887,27933,27934 |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Slippage+prediction+for+off-road+mobile+robots+via+machine+learning+regression+and+proprioceptive+sensing&rft.jtitle=Robotics+and+autonomous+systems&rft.au=Gonzalez%2C+Ramon&rft.au=Fiacchini%2C+Mirko&rft.au=Iagnemma%2C+Karl&rft.date=2018-07-01&rft.pub=Elsevier+B.V&rft.issn=0921-8890&rft.eissn=1872-793X&rft.volume=105&rft.spage=85&rft.epage=93&rft_id=info:doi/10.1016%2Fj.robot.2018.03.013&rft.externalDocID=S0921889018300174 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0921-8890&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0921-8890&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0921-8890&client=summon |