The H5 Humanoid Robot Playing Interactive Chess
|Given a task-level command such as "put the book
on the shelf", humanoid robots should come equipped with software able
to automatically generate a feasible motion trajectory to accomplish
the task safely and efficiently.
Inverse kinematics can for computing a goal configuration for the
robot arm. Then, efficient path planning techniques can be used to
search the configuration space (C-space) of the arm directly for a
collision-free path connecting the inital and the goal configurations.
Due to the high-dimensionality of the space, brute-force search
algorithms are infeasible. As an alternative, we have conducted a
series of experiments utilizing randomized path planning techniques.
In particular, we have developed the RRT-Connect
planner based on Rapidly-Exploring
Random Trees (RRTs) for quickly solving single-query path
planning problems in high-dimensional spaces.
A user can click on a object and command the robot to reposition
it interactively. All of the motions for the robot to grasp, move,
and release the object are computed "on-the-fly", with no
preprocessing of the environment. For most tasks, collision-free
motions for a 7-DOF arm can be generated in a few seconds.
This problem formulation is suitable for manipulation tasks
involving relatively lightweight objects that can be grasped by a
single arm. We are currently extending the technique to handle
multi-arm manipulation tasks.
Full-body, Dynamically-Stable Motion Planning
|In order to be fully-general, motion planning algorithms for humanoid
robots must take into account all available degrees of freedom. We
have developed algorithms for computing stable collision-free motions
for humanoid robots given full-body posture goals
(ICRA2001). The technique is not restricted to
biped robots, but can be applied to any legged robot.
Given a robot's internal model of the environment and a
statically-stable desired posture, we use a randomized path planner to
search the configuration space of the full body of the robot for a
collision-free path that also satisifies the balance constraints.
Instead of sampling the entire configuration space, the subspace
corresponding to statically-stable postures of a given support
configuration (single-leg or dual-leg). Balance constraints are
imposed on incremental search motions in order to maintain the overall
dynamic stability of the computed trajectories.
Avoiding an obstacle while
on a single leg.
Click to view full-size animation (383 KB)>
Retrieving an object from
beneath a chair while
balancing on both legs.
Click to view full-size animation (375 KB)>
Several complicated examples involving humanoid robots of more than 30
DOF were computed in less than 10 minutes on an SGI O2 workstation.
The resulting output trajectories were verified on actual humanoid
robot hardware (humanoid
robot H6). Click the above images to view larger animations of the
motion planner output. The trajectories executed on the real robot
are included towards the end of video clip linked below on the right.
Single-leg balancing snapshot
Dual-leg balancing snapshot
Download H6 humanoid movie
(Warning! file size = 18 MB)>
Path Planning for Global Navigation
H5 Humanoid Robot
Navigating an unknown maze environment
Top view of final trajectory
See the Footstep
Papers and Videos
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