Deep Learning Based Robot for Automatically Picking up Garbage on the Grass
This paper presents a novel garbage pickup robot which operates on the grass. The robot is able to detect the garbage accurately and autonomously by using a deep neural network for garbage recognition. In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around. With the garbage recognition and automatic navigation functions, the robot can clean garbage on the ground in places like parks or schools efficiently and autonomously. Experimental results show that the garbage recognition accuracy can reach as high as 95%, and even without path planning, the navigation strategy can reach almost the same cleaning efficiency with traditional methods. Thus, the proposed robot can serve as a good assistance to relieve dustman's physical labor on garbage cleaning tasks.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Garbage Segmentation and Attribute Analysis by Robotic Dogs
Efficient waste management and recycling heavily rely on garbage exploration and identification. In this study, we propose GSA2Seg (Garbage Segmentation and Attribute Analysis), a novel visual approach that utilizes quad…
AttributeManagementNavigateRetrievalMulti-species Seagrass Detection and Classification from Underwater Images
Underwater surveys conducted using divers or robots equipped with customized camera payloads can generate a large number of images. Manual review of these images to extract ecological data is prohibitive in terms of time…
ClassificationGeneral ClassificationRobot-based facade spatial assembly optimization
Robotic involvement in construction is still in its initial stages compared to other industries. Conventionally, the facade panel picking position is done manually by trial and error. The designer chooses a place to pick…
Decision MakingPositionMulti-vision-based Picking Point Localisation of Target Fruit for Harvesting Robots
This paper presents multi-vision-based localisation strategies for harvesting robots. Identifying picking points accurately is essential for robotic harvesting because insecure grasping can lead to economic loss through …
Ensemble LearningTeam Applied Robotics: A closer look at our robotic picking system
This paper describes the vision based robotic picking system that was developed by our team, Team Applied Robotics, for the Amazon Picking Challenge 2016. This competition challenged teams to develop a robotic system tha…
Objectobject-detectionObject Detection