Online motion selection for semi-optimal stabilization using reverse-time tree

This paper presents a general method for creating an approximately optimal online stabilization system. An optimal stabilization system is an ideal online system that can calculate each optimal motion leading to a stable mechanical goal state depending on the current state. We propose a system that selects each semi-optimal motion according to the current state from a reverse-time tree. To create the reverse-time tree, we applied rapid semi-optimal motion planning method (RASMO) to a reverse-time search from a stable state. We also developed an online motion selection technique. To validate the proposed method, we simulated the stabilization of a double inverted pendulum. When we used an optimization criteria, time optimal, the system quickly stabilized the pendulum's posture and velocity. When we used higher resolution RASMO, the time approached the optimal time. The general framework proposed here is applicable to a variety of machines.

[1]  Satoshi Kagami,et al.  High-speed planning and reducing memory usage of a precomputed search tree using pruning , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  J. Bobrow,et al.  Time-Optimal Control of Robotic Manipulators Along Specified Paths , 1985 .

[3]  Shigeki Sugano,et al.  Rapid Short-Time Path Planning for Phase Space , 2011, J. Robotics Mechatronics.

[4]  Steven Dubowsky,et al.  On computing the global time-optimal motions of robotic manipulators in the presence of obstacles , 1991, IEEE Trans. Robotics Autom..

[5]  Friedrich Pfeiffer,et al.  A concept for manipulator trajectory planning , 1987, IEEE J. Robotics Autom..

[6]  Mark W. Spong,et al.  The swing up control problem for the Acrobot , 1995 .

[7]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[8]  Gary Boone,et al.  Minimum-time control of the Acrobot , 1997, Proceedings of International Conference on Robotics and Automation.

[9]  R. Murray,et al.  Nonlinear controllers for non-integrable systems: the Acrobot example , 1990, 1990 American Control Conference.

[10]  Wolfgang Maass,et al.  Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs , 2007, ECML.

[11]  Kazuhito Yokoi,et al.  Planning 3-D Collision-Free Dynamic Robotic Motion Through Iterative Reshaping , 2008, IEEE Transactions on Robotics.

[12]  Andrew W. Moore,et al.  Variable Resolution Discretization in Optimal Control , 2002, Machine Learning.

[13]  Shigeki Sugano,et al.  Reinforcement learning of a continuous motor sequence with hidden states , 2007, Adv. Robotics.

[14]  Satoshi Kagami,et al.  High-speed planning and reducing memory usage of a precomputed search tree using pruning , 2009, IROS.

[15]  Shurong Li,et al.  Switching-time computation for time-optimal trajectory planning of wheeled mobile robots , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[16]  Jun-ichi Imura,et al.  Graph Based Model Predictive Control of a Planar Bipedal Robot , 2006 .

[17]  M. Diehl,et al.  Time-energy optimal path tracking for robots: a numerically efficient optimization approach , 2008, 2008 10th IEEE International Workshop on Advanced Motion Control.

[18]  Mark W. Spong,et al.  Mechanical Design and Control of the Pendubot , 1995 .

[19]  Rogelio Lozano,et al.  Energy based control of the Pendubot , 2000, IEEE Trans. Autom. Control..

[20]  John M. Hollerbach,et al.  Planning of Minimum- Time Trajectories for Robot Arms , 1986 .

[22]  Yoshihiko Nakamura,et al.  Efficient parallel dynamics computation of human figures , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[23]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[24]  Helge J. Ritter,et al.  On-line planning of time-optimal, jerk-limited trajectories , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Jean-Claude Latombe,et al.  Randomized Kinodynamic Motion Planning with Moving Obstacles , 2002, Int. J. Robotics Res..

[26]  James E. Bobrow,et al.  Optimal Robot Path Planning Using the Minimum-Time Criterion , 2022 .

[27]  John M. Hollerbach,et al.  Planning a minimum-time trajectories for robot arms , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[28]  Steven M. LaValle,et al.  Rapidly-Exploring Random Trees: Progress and Prospects , 2000 .

[29]  Arthur Richards,et al.  Rapid updating for path-planning using nonlinear branch-and-bound , 2010, 2010 IEEE International Conference on Robotics and Automation.

[30]  J Imura,et al.  Approximately bisimilar discrete abstractions of nonlinear systems using variable-resolution quantizers , 2010, Proceedings of the 2010 American Control Conference.

[31]  Helge J. Ritter,et al.  Planning a Dynamic Trajectory via Path Finding in Discretized Phase Space , 1986, WOPPLOT.

[32]  Shigeki Sugano,et al.  Semi-optimal motion control for nonholonomic systems with a passive joint , 2011, SICE Annual Conference 2011.

[33]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..

[34]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[35]  Lydia E. Kavraki,et al.  Guided Expansive Spaces Trees: a search strategy for motion- and cost-constrained state spaces , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[36]  Bruce Randall Donald,et al.  Kinodynamic motion planning , 1993, JACM.

[37]  Kang G. Shin,et al.  Minimum-time control of robotic manipulators with geometric path constraints , 1985 .

[38]  Jean-Claude Latombe,et al.  Nonholonomic multibody mobile robots: Controllability and motion planning in the presence of obstacles , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[39]  N. McKay,et al.  A dynamic programming approach to trajectory planning of robotic manipulators , 1986 .

[40]  Junichiro Yoshimoto,et al.  Acrobot control by learning the switching of multiple controllers , 2005, Artificial Life and Robotics.