NUS CS5247 Dynamically-stable Motion Planning for Humanoid Robots Presenter Shen zhong Guan Feng 07/11/2003

NUS CS52472 Paper information  Authors: James Kuffner, Jr., Satoshi Kagami, Masayuki Inaba and Hirochika Inoue  Address: Dept. of Mechano-Informatics, The university of Tokyo

NUS CS52473 Outline  Introduction of motion planning  Motivation  Robot model and problem  Path search  Statically-stable postures generation  Experiments  Discussions

NUS CS52474 Introduction  Complete algorithms exist for general class of problem, but their computational complexity limits their use to low-dimensional configuration spaces  Path planning methods using randomization are incomplete  The goal is to develop randomized methods  Converge quickly  Simple enough to yield constant behavior  Maintain robot static and dynamic stability

NUS CS52475 Motivation  Develop a simulation environment to provide high- level software control for humanoid robot The software automatically computes object grasping and manipulation trajectories through a combination of inverse kinematics and randomized holonomic path planning

NUS CS52476 Motivation  One part of the software is to design an algorithm for computing stable collision-free motions for humanoid robots given full-body posture goals

NUS CS52477 Difficulties  High dimensions – 30 or more  Maintain overall static and dynamic stability

NUS CS52478 Solutions proposed  Randomized planner RRT-Connect: An efficient approach to single-query path planning. In proc.IEEE Int’l Conf. on Robotics and Automation (ICRA2000), San Francisco  Utilize Rapidly-exploring Random Trees (RRTs) and try to connect two search trees aggressively  Filter the returned path to maintains dynamic balance constraints

NUS CS52479 Robot Model and Assumptions  An approximate model of surrounding environment can be acquired using stereo vision or other means  The robot is currently balanced on either one or both feet  Supporting feet does not move during the planned motion  A statically-stable full-body goal posture is given

NUS CS Some notations  Robot (A) with p links L i (i=1,…,p) is in workspace W. The ith link has mass c i relative to Cartesian frame F i.  A configuration of the robot is denoted by the set P={T 1,T 2,…,T p }  n denotes the number of DOFs  A configuration q is defined in C- configuration space  The set of obstacles are labeled by B  C free denotes the space of collision-free configurations  X(q) denotes the vector representing the global position of the center of mass of A  A configuration is statistically-stable if the projection of X(q) along the gravity vector lies within the area of support SP  C valid denotes the subset of configurations that are both collision-free and statically-stable  τ : I → C denotes a motion trajectory, τ(t 0 )=q initial, τ(t 1 )=q goal

NUS CS Path Search  Path planner S.Kagami, F.Kanehiro, Y.Tamiya, M.Inaba and H.Inoue, Autobalancer: an online dynamic balance compensation scheme for humanoid robots, March 2000 Planner(A,B,q init,q goal )→ τ  Modified RRT-Connect: try to connect two search trees aggressively

NUS CS Path Search q q new q target q init q near ε

NUS CS Path Search

NUS CS Path Search

NUS CS Statically-stable postures generation  Many configurations are collision free but unstable.  Many configurations q can be generated and stored in advance.  Using collision detection algorithm.  computing X(q) and verify that its projection along g is contained within the boundary of SP.

NUS CS Statically-stable postures generation

NUS CS Statically-stable postures generation

NUS CS Statically-stable postures generation

NUS CS Experiments 270 MHz SGI O2 (R12000) workstation DOF: 30 or more

NUS CS Discussion and limitations  The planner, having task-level planning algorithm, is limited to body posture goals and fixed position for either one or both feet.  Reduction of computation time  Efficient collision-detection software  More stable samples  Analysis of coverage of Cvalid and the convergence.

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