A Topological Solution of Entanglement for Complex-shaped Parts in Robotic Bin-picking
This addresses the challenge of entanglement in bin-picking for robotics applications, offering a model-free and training-free solution, though it appears incremental as it builds on existing methods with a new topological approach.
The paper tackles the problem of picking single objects without entanglement in robotic bin-picking, especially for complex-shaped parts, by proposing a topology-based method that uses an entanglement map from depth images to select grasp positions. Experimental results show it exceeds previous learning-based work in success rates and avoids reliance on object models or training.
This paper addresses the problem of picking up only one object at a time avoiding any entanglement in bin-picking. To cope with a difficult case where the complex-shaped objects are heavily entangled together, we propose a topology-based method that can generate non-tangle grasp positions on a single depth image. The core technique is entanglement map, which is a feature map to measure the entanglement possibilities obtained from the input image. We use the entanglement map to select probable regions containing graspable objects. The optimum grasping pose is detected from the selected regions considering the collision between robot hand and objects. Experimental results show that our analytic method provides a more comprehensive and intuitive observation of entanglement and exceeds previous learning-based work in success rates. Especially, our topology-based method does not rely on any object models or time-consuming training process, so that it can be easily adapted to more complex bin-picking scenes.