Knowledge and capabilities representation for visually guided robotic bin picking

The paper presents an implementation of knowledge representation including the capabilities of the system, based on ontologies for a Visually Guided Bin Picking Task. The ontology based approach was used to define the work environment, the robot, the machine vision sys- tem, and the capabilities that are needed to be performed by the robotic system, to perform the bin-picking task. The work proposes a novel application framework that is able to locate the object to pick from the bin and place it in a cell from a kit. For that, the framework, delivers the task implementation (PDDL) files that should be executed by the robot. The method used to detect the objects is based on Chamfer Match (CM) and Oriented Chamfer Match (OCM) which take advantage of the image edge map. To complete the pose estimation problem the robot manipulator is equipped with a laser range finder that can measure the object height. The robotic system was validated experimentally with simulation. using the V-REP environment interfacing with ROS, where the knowledge representation and reasoning framework is implemented. The system showed its capability to correctly pick and place a specific object. Moreover, the ontology based approach was very useful to define the task, the actions to be performed by the robot, based on its capabilities.info:eu-repo/semantics/publishedVersio