Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles
暂无分享,去创建一个
Pieter Abbeel | Kenneth Y. Goldberg | Jur P. van den Berg | Sachin Patil | John Schulman | Alex X. Lee | Yan Duan | Zoe McCarthy | J. Schulman | P. Abbeel | Yan Duan | Ken Goldberg | S. Patil | Zoe McCarthy | J. V. D. Berg
[1] John N. Tsitsiklis,et al. The Complexity of Markov Decision Processes , 1987, Math. Oper. Res..
[2] John Canny,et al. The complexity of robot motion planning , 1988 .
[3] S. Sathiya Keerthi,et al. A fast procedure for computing the distance between complex objects in three-dimensional space , 1988, IEEE J. Robotics Autom..
[4] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[5] Dimitri P. Bertsekas,et al. Dynamic Programming and Optimal Control, Two Volume Set , 1995 .
[6] Alan D. Christiansen,et al. Comparing two algorithms for automatic planning by robots in stochastic environments , 1995, Robotica.
[7] Jeffrey K. Uhlmann,et al. New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.
[8] Leslie Pack Kaelbling,et al. Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..
[9] Sebastian Thrun,et al. Monte Carlo POMDPs , 1999, NIPS.
[10] Gino van den Bergen. Proximity Queries and Penetration Depth Computation on 3D Game Objects , 2001 .
[11] Marko Bacic,et al. Model predictive control , 2003 .
[12] J. L. Roux. An Introduction to the Kalman Filter , 2003 .
[13] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[14] Dominique Gruyer,et al. A fast Monte Carlo algorithm for collision probability estimation , 2008, 2008 10th International Conference on Control, Automation, Robotics and Vision.
[15] David Hsu,et al. SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces , 2008, Robotics: Science and Systems.
[16] John T. Betts,et al. Practical Methods for Optimal Control and Estimation Using Nonlinear Programming , 2009 .
[17] N. Roy,et al. The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance , 2009, Int. J. Robotics Res..
[18] William D. Smart,et al. A Scalable Method for Solving High-Dimensional Continuous POMDPs Using Local Approximation , 2010, UAI.
[19] Leslie Pack Kaelbling,et al. Belief space planning assuming maximum likelihood observations , 2010, Robotics: Science and Systems.
[20] Quoc V. Le,et al. Low-cost accelerometers for robotic manipulator perception , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[21] Kris K. Hauser,et al. Randomized Belief-Space Replanning in Partially-Observable Continuous Spaces , 2010, WAFR.
[22] Blake Hannaford,et al. Raven: Developing a Surgical Robot from a Concept to a Transatlantic Teleoperation Experiment , 2011 .
[23] Pieter Abbeel,et al. LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2010, Int. J. Robotics Res..
[24] Leslie Pack Kaelbling,et al. Efficient Planning in Non-Gaussian Belief Spaces and Its Application to Robot Grasping , 2011, ISRR.
[25] C. Tomlin,et al. Closed-loop belief space planning for linear, Gaussian systems , 2011, 2011 IEEE International Conference on Robotics and Automation.
[26] P. Abbeel,et al. LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information , 2011 .
[27] Nicholas Roy,et al. Rapidly-exploring Random Belief Trees for motion planning under uncertainty , 2011, 2011 IEEE International Conference on Robotics and Automation.
[28] Ron Alterovitz,et al. Motion planning under uncertainty using iterative local optimization in belief space , 2012, Int. J. Robotics Res..
[29] Pieter Abbeel,et al. Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization , 2013, Robotics: Science and Systems.
[30] David Q. Mayne,et al. Model predictive control: Recent developments and future promise , 2014, Autom..