Tree Root Detection and Cutting Control of a Pipeline Operation Robot Using Vision and Force Sensing in Sewer Pipelines

Traditional manual inspection and maintenance methods for urban drainage pipelines suffer from low efficiency and high safety risks. This paper proposes a vision and force sensing-based autonomous tree roots detection and cutting control method for a pipeline operation robot in sewer pipelines. The robot is equipped with a depth camera and utilizes the YOLOv11 model to identify pipeline defects, achieving target recognition. The robot is also equipped with a 5-degree-of-freedom robotic arm. We established the kinematic and dynamic models of the robotic arm, designed a trajectory planning method, and verified its feasibility in a simulation environment. Finally, we conducted experimental studies. The results show that the robot system achieves a recognition rate of over 60% for tree roots, with depth accuracy at the millimeter level. The angular control deviation of the robotic arm motor is within 1°. This research provides a foundation for tree root cutting and other types of obstacle detection and cutting in underwater pipelines.