NUS CS5247

Dynamically-stable Dynamically-stable Motion Planning for Motion Planning for Humanoid RobotsHumanoid Robots

PresenterPresenterShen zhongShen zhongGuan FengGuan Feng

07/11/200307/11/2003

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Paper information Authors:

James Kuffner, Jr., Satoshi Kagami, Masayuki Inaba and Hirochika Inoue

Address:

Dept. of Mechano-Informatics, The university of Tokyo

http://www.jsk.t.u-tokyo.ac.jp/~kuffner/humanoid

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

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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

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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

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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

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Difficulties High dimensions – 30 or

more Maintain overall static

and dynamic stability

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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

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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

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Some notations Robot (A) with p links Li (i=1,…,p) is in workspace W. The ith link has

mass ci relative to Cartesian frame Fi.

A configuration of the robot is denoted by the set P={T1,T2,…,Tp} n denotes the number of DOFs A configuration q is defined in C- configuration space The set of obstacles are labeled by B Cfree 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 Cvalid denotes the subset of configurations that are both collision-free

and statically-stable τ : I → C denotes a motion trajectory, τ(t0)=qinitial, τ(t1)=qgoal

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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,qinit,qgoal)→ τ

Modified RRT-Connect: try to connect two search trees aggressively

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Path Search

q qnew

qtarget

qinit

qnear

ε

n

iiii qqqq

1)target()near(targetnear ||),(

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Path Search

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Path Search

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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.

freeCq

stableCq

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Statically-stable postures generation

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Statically-stable postures generation

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Statically-stable postures generation

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Experiments

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

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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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Thank you !