Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object without Cameras
Keywords:
Grasping, moving object, trajectory planning, robot hand, obstacles.Abstract
Many of researchers working on robotic grasping
tasks assume a stationary or fixed object, others have focused
on dynamic moving objects using cameras to record images of
the moving object and then they treated their images to estimate
the position to grasp it. This method is quite difficult, requiring
a lot of computing, image processing… Hence, it should be
sought more simple handling method. Moreover, the majorities
of robotic arms available for humanoid applications are
complex to control and yet expensive. In this paper, we are
going to detail the requirements to manupilating a humanoid
robot arm with 7 degree-of-freedom to grasp and handle any
moving objects in the 3-D environment in the presence or not of
obstacles and without using the cameras. We used the
OpenRAVE simulation environment and a robot arm equipped
with the Barrett hand. We also describe a randomized planning
algorithm capable of planning. This algorithm is an extension of
RRT-JT that interleaves exploration using a Rapidly-exploring
Random Tree with exploitation using Jacobian-based gradient
descent to control a 7-DoF WAM robotic arm to avoid the
obstacles, track a moving object, and grasp planning. We
present results in which a moving mug is tracked, stably
grasped with a maximum rate of success in a reasonable time
and picked up by the Barret hand to a desired position.
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