| Chance-constrained optimal path planning with obstacles L Blackmore, M Ono, BC Williams IEEE Transactions on Robotics 27 (6), 1080-1094, 2011 | 466 | 2011 |
| A probabilistic particle-control approximation of chance-constrained stochastic predictive control L Blackmore, M Ono, A Bektassov, BC Williams IEEE transactions on Robotics 26 (3), 502-517, 2010 | 416 | 2010 |
| Chance-constrained dynamic programming with application to risk-aware robotic space exploration M Ono, M Pavone, Y Kuwata, J Balaram Autonomous Robots 39, 555-571, 2015 | 350 | 2015 |
| Iterative risk allocation: A new approach to robust model predictive control with a joint chance constraint M Ono, BC Williams 2008 47th IEEE Conference on Decision and Control, 3427-3432, 2008 | 222 | 2008 |
| Convex chance constrained predictive control without sampling L Blackmore, M Ono AIAA guidance, navigation, and control conference, 5876, 2009 | 205 | 2009 |
| Spoc: Deep learning-based terrain classification for mars rover missions B Rothrock, R Kennedy, C Cunningham, J Papon, M Heverly, M Ono AIAA SPACE 2016, 5539, 2016 | 182 | 2016 |
| Safe exploration and optimization of constrained mdps using gaussian processes A Wachi, Y Sui, Y Yue, M Ono Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 163 | 2018 |
| Real-time pricing mechanism for electricity market with built-in incentive for participation T Namerikawa, N Okubo, R Sato, Y Okawa, M Ono IEEE Transactions on Smart Grid 6 (6), 2714-2724, 2015 | 145 | 2015 |
| Risk-aware planetary rover operation: Autonomous terrain classification and path planning M Ono, TJ Fuchs, A Steffy, M Maimone, J Yen 2015 IEEE aerospace conference, 1-10, 2015 | 137 | 2015 |
| An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure. M Ono, BC Williams AAAI, 1376-1382, 2008 | 99 | 2008 |
| Autonomous terrain classification with co-and self-training approach K Otsu, M Ono, TJ Fuchs, I Baldwin, T Kubota IEEE Robotics and Automation Letters 1 (2), 814-819, 2016 | 97 | 2016 |
| Probabilistic planning for continuous dynamic systems under bounded risk M Ono, BC Williams, L Blackmore Journal of Artificial Intelligence Research 46, 511-577, 2013 | 93 | 2013 |
| Ai4mars: A dataset for terrain-aware autonomous driving on mars RM Swan, D Atha, HA Leopold, M Gildner, S Oij, C Chiu, M Ono Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 84 | 2021 |
| Science goals and mission architecture of the Europa lander mission concept KP Hand, CB Phillips, A Murray, JB Garvin, EH Maize, RG Gibbs, ... The Planetary Science Journal 3 (1), 22, 2022 | 76 | 2022 |
| Chance constrained finite horizon optimal control with nonconvex constraints M Ono, L Blackmore, BC Williams Proceedings of the 2010 American control conference, 1145-1152, 2010 | 69 | 2010 |
| Collision-free encoding for chance-constrained nonconvex path planning M da Silva Arantes, CFM Toledo, BC Williams, M Ono IEEE Transactions on Robotics 35 (2), 433-448, 2019 | 60 | 2019 |
| Vision-based estimation of driving energy for planetary rovers using deep learning and terramechanics S Higa, Y Iwashita, K Otsu, M Ono, O Lamarre, A Didier, M Hoffmann IEEE Robotics and Automation Letters 4 (4), 3876-3883, 2019 | 56 | 2019 |
| Fast approximate clearance evaluation for rovers with articulated suspension systems K Otsu, G Matheron, S Ghosh, O Toupet, M Ono Journal of Field Robotics 37 (5), 768-785, 2020 | 55 | 2020 |
| Locally-adaptive slip prediction for planetary rovers using gaussian processes C Cunningham, M Ono, I Nesnas, J Yen, WL Whittaker 2017 IEEE international conference on robotics and automation (ICRA), 5487-5494, 2017 | 52 | 2017 |
| Robust, optimal predictive control of jump markov linear systems using particles L Blackmore, A Bektassov, M Ono, BC Williams Hybrid Systems: Computation and Control: 10th International Workshop, HSCC …, 2007 | 50 | 2007 |
