A 3D-Printed Self-Learning Three-Linked-Sphere Robot for Autonomous Confined-Space Navigation.

ABSTRACT: Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D-printed three-linked-sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dict

SUBMITTER: Elder B 

PROVIDER: S-EPMC8963778 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature