Abstract
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.
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Leeper, A., Hsiao, K., Chu, E., Salisbury, J.K. (2014). Using Near-Field Stereo Vision for Robotic Grasping in Cluttered Environments. In: Khatib, O., Kumar, V., Sukhatme, G. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28572-1_18
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DOI: https://doi.org/10.1007/978-3-642-28572-1_18
Publisher Name: Springer, Berlin, Heidelberg
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