Autonomous detection and sorting of litter using deep learning and soft robotic grippers - Almanzor, Elijah; Anvo, Nzebo Richard; Thuruthel, Thomas George; Iida, Fumiya (Frontiers in Robotics and AI, 2022)
Road infrastructure is one of the most vital assets of any country. Keeping the road infrastructure clean and unpolluted is important for ensuring road safety and reducing environmental risk. However, roadside litter picking is an extremely laborious, expensive, monotonous and hazardous task. Automating the process would save taxpayers money and reduce the risk for users and the maintenance crew. This work presents LitterBot, an autonomous robotic system capable of detecting, localizing and classifying common roadside litter. We use learning-based object detection and segmentation algorithm trained on the TACO dataset for identifying and classifying garbage. We develop a robust manipulation framework by using soft robotic grippers and a real-time visual-servoing strategy. This enables the manipulator to pick up objects of variable sizes and shapes even in dynamic environments. Our results showcase how a deep model trained on an online dataset can be deployed in real-world applications with high accuracy by the appropriate design of a control framework around it.