Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments
| Autor: | J. Kenneth Salisbury, Eric Chu, Kaijen Hsiao, Adam Leeper |
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| Rok vydání: | 2014 |
| Předmět: | |
| Zdroj: | Experimental Robotics ISBN: 9783642285714 ISER |
| DOI: | 10.1007/978-3-642-28572-1_18 |
| Popis: | Robotic grasping in unstructured environments requires the ability to adjust and recover when a pre-planned grasp faces imminent failure. Even for a single object, modeling uncertainties due to occluded surfaces, sensor noise and calibration errors can cause grasp failure; cluttered environments exacerbate the problem. In this work, we propose a simple but robust approach to both pre-touch grasp adjustment and grasp planning for unknown objects in clutter, using a small-baseline stereo camera attached to the gripper of the robot. By employing a 3D sensor from the perspective of the gripper we gain information about the object and nearby obstacles immediately prior to grasping that is not available during head-sensor-based grasp planning. We use a feature-based cost function on local 3D data to evaluate the feasibility of a proposed grasp. In cases where only minor adjustments are needed, our algorithm uses gradient descent on a cost function based on local features to find optimal grasps near the original grasp. In cases where no suitable grasp is found, the robot can search for a significantly different grasp pose rather than blindly attempting a doomed grasp. We present experimental results to validate our approach by grasping a wide range of unknown objects in cluttered scenes. Our results show that reactive pre-touch adjustment can correct for a fair amount of uncertainty in the measured position and shape of the objects, or the presence of nearby obstacles. |
| Databáze: | OpenAIRE |
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