Masahiro Ono
Masahiro Ono
NASA Jet Propulsion Laboratory, California Institute of Technology
Verified email at
Cited by
Cited by
Chance-constrained optimal path planning with obstacles
L Blackmore, M Ono, BC Williams
IEEE Transactions on Robotics 27 (6), 1080-1094, 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
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
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
Convex chance constrained predictive control without sampling
L Blackmore, M Ono
AIAA guidance, navigation, and control conference, 5876, 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
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
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
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
An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure.
M Ono, BC Williams
AAAI, 1376-1382, 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
Probabilistic planning for continuous dynamic systems under bounded risk
M Ono, BC Williams, L Blackmore
Journal of Artificial Intelligence Research 46, 511-577, 2013
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
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
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
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
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
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
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
Data-driven surface traversability analysis for Mars 2020 landing site selection
M Ono, B Rothrock, E Almeida, A Ansar, R Otero, A Huertas, M Heverly
2016 IEEE Aerospace Conference, 1-12, 2016
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