The use of flexible and autonomous robotic systems is the solution for the automation in dynamic and unstructured industrial environments. This context requires the robot to be aware of its surroundings throughout the whole manipulation task, also after accomplishing the gripping action. This work introduces the deep post gripping perception framework, which includes the post gripping perception abilities realized with the help of deep learning techniques, especially unsupervised learning methods. These abilities help the robot to execute a stable and precise placing of the gripped items depending on the required task. We describe the development of the framework based on an established literature review. The result of the work is a modular design of the framework with the help of three functional components used to build planning, monitoring and verifying modules.

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