Grasping Novel Objects with Real-Time Obstacle Avoidance

This paper proposes a new approach to grasp novel objects while avoiding real-time obstacles. The general idea is to perform grasping of novel objects and do collision avoidance at the same time. There are two main contributions. Firstly, a fast and robust method of real-time grasp detection is presented based on morphological image processing and machine learning. Secondly, we integrate our robotic grasping algorithms with some existing collision prediction strategies. It is really helpful to grasp objects on the condition that a robot is surrounded by obstacles. Additionally, it is very practical, runs in real-time and can be easily adaptable with respect to different robots and working conditions. We demonstrate our approaches using the Kinect sensor and the Baxter robot with a series of experiments.

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