Deep Learning Based Robot for Automatically Picking up Garbage on the Grass
Jinqiang Bai, Shiguo Lian, Zhaoxiang Liu, Kai Wang, Dijun Liu
- Year
- 2019
- Access
- Open access
Abstract
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.
Keywords
Related papers
Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
Keyi Shen, Glen Chou
2026
Artificial Intelligence enhanced smart welding islands: Foundation models revolutionizing manufacturing
Xiwei Wu, Wei Wu, Qiqi Chen +6 more
Robotics and Computer-Integrated Manufacturing · 2026
A deep reinforcement learning and a dynamic graph neural network-based scheduling agent to control a multi-task robot
Hedi Boukamcha, Anas Neumann, Monia Rekik +3 more
Robotics and Computer-Integrated Manufacturing · 2026
LLM Agent-driven Automated DFA Assessment with Fine-tuning and AAS-based RAG
Jiaxin Liu, Xiaofeng Zhou, Suyang Yu +5 more
Robotics and Computer-Integrated Manufacturing · 2026