Deep Learning Based Robot for Automatically Picking up Garbage on the Grass
This addresses the labor-intensive task of garbage cleaning in parks or schools, offering incremental improvements in automation.
The paper tackles the problem of automating garbage collection on grass using a robot with deep learning for garbage recognition and navigation, achieving 95% recognition accuracy and comparable cleaning efficiency to traditional methods without path planning.
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.