ABSRACTMany of the robotic grasping researches have been focusing on stationary objects. And for dynamic moving objects, researchers have been using real time captured images to locate objects dynamically. However, this approach of controlling the grasping process is quite costly, implying a lot of resources and image processing.Therefore, it is indispensable to seek other method of simpler handling… In this paper, we are going to detail the requirements to manipulate a humanoid robot arm with 7 degree-of-freedom to grasp and handle any moving objects in the 3-D environment in presence or not of obstacles and without using the cameras. We use the OpenRAVE simulation environment, as well as, a robot arm instrumented with the Barrett hand. We also describe a randomized planning algorithm capable of planning. This algorithm is an extent of RRT-JT that combines exploration, using a Rapidly-exploring Random Tree, with exploitation, using Jacobian-based gradient descent, to instruct a 7-DoF WAM robotic arm, in order to grasp a moving target, while avoiding possible encountered obstacles . We present a simulation of a scenario that starts with tracking a moving mug then grasping it and finally placing the mug in a determined position, assuring a maximum rate of success in a reasonable time.
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 manipulating a 7-DoF WAM robotic arm equipped with the Barrett hand to grasp and handle any moving objects in the 3-D environment in the presence of obstacles and without using the cameras. We used the OpenRAVE simulation environment. We use an extension of RRT-JT algorithm that interleaves exploration using a Rapidly-exploring Random Tree with exploitation using Jacobian-based gradient descent to control the 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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