Domestic waste detection and grasping points for robotic picking up
This work addresses waste management automation for recycling applications, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of detecting and grasping domestic waste for robotic recycling by developing an AI system that uses Mask-RCNN on a new dataset to locate waste in indoor and outdoor environments, achieving classification into five groups to improve recycling strategies.
This paper presents an AI system applied to location and robotic grasping. Experimental setup is based on a parameter study to train a deep-learning network based on Mask-RCNN to perform waste location in indoor and outdoor environment, using five different classes and generating a new waste dataset. Initially the AI system obtain the RGBD data of the environment, followed by the detection of objects using the neural network. Later, the 3D object shape is computed using the network result and the depth channel. Finally, the shape is used to compute grasping for a robot arm with a two-finger gripper. The objective is to classify the waste in groups to improve a recycling strategy.