Development of an earthworm-based soft robot for colon sampling

Front Robot AI. 2024 Feb 7:11:1309220. doi: 10.3389/frobt.2024.1309220. eCollection 2024.

Abstract

Colorectal cancer as a major disease that poses a serious threat to human health continues to rise in incidence. And the timely colon examinations are crucial for the prevention, diagnosis, and treatment of this disease. Clinically, gastroscopy is used as a universal means of examination, prevention and diagnosis of this disease, but this detection method is not patient-friendly and can easily cause damage to the intestinal mucosa. Soft robots as an emerging technology offer a promising approach to examining, diagnosing, and treating intestinal diseases due to their high flexibility and patient-friendly interaction. However, existing research on intestinal soft robots mainly focuses on controlled movement and observation within the colon or colon-like environments, lacking additional functionalities such as sample collection from the intestine. Here, we designed and developed an earthworm-like soft robot specifically for colon sampling. It consists of a robot body with an earthworm-like structure for movement in the narrow and soft pipe-environments, and a sampling part with a flexible arm structure resembling an elephant trunk for bidirectional bending sampling. This soft robot is capable of flexible movement and sample collection within an colon-like environment. By successfully demonstrating the feasibility of utilizing soft robots for colon sampling, this work introduces a novel method for non-destructive inspection and sampling in the colon. It represents a significant advancement in the field of medical robotics, offering a potential solution for more efficient and accurate examination and diagnosis of intestinal diseases, specifically for colorectal cancer.

Keywords: colon sampling; colorectal cancer; earthworm-like; soft robot; soft sampler.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Natural Science Foundation of China, grant number 61903157 and number 61833007.