Application of particle filtering in predictable dynamic tubular environments

Abstract

Robust localization is essential for successful navigation of autonomous mobile robots in any environment. Advanced techniques such as simultaneous localization and mapping (SLAM) also consider reliable localization as a critical component. The major existing localization algorithms like Particle Filtering, however, are, based on the assumption that the environment is static which does not hold true in some practical applications of mobile robots.

In this thesis, we explore a rather fledgling area of wireless robotic capsule endoscopy where dynamism is an inherent feature of the environment. Since the last decade, intense research has spurred in the field of capsule robots used for inspecting the entire gastrointestinal (GI) tract for various diseases, such as obscure gastrointestinal bleeding (OGIB), tumors, cancer, Crohn’s disease, and celiac disease. It is envisioned that the next generation of these micro-robots will traverse the GI tract with greater autonomy. In such self-propelling tiny robots, the incorporation of mini-surgical tools and artificial intelligence is anticipated. Capsule robot localization and mapping of the environment inside the human body will be a key essential for successful autonomous navigation.