This paper reports an attempt to model the system dynamics and estimate both the unknown internal control input and the state of a recently developed marine autonomous vehicle, the Jaiabot. Although the Jaiabot has shown promise in many applications, process and sensor noise necessitates state estimation and noise filtering. In this work, we present the first surge and heading linear dynamical model for Jaiabots derived from real data collected during field testing. An adaptive input estimation algorithm is implemented to accurately estimate the control input and hence the state. For validation, this approach is compared to the classical Kalman filter, highlighting its advantages in handling unknown control inputs.

JaiaBots are supposed to work in fleets to collect information in aquatic operations. Jaia Robotics, an organization growing micro-sized autonomous underwater autos (AUVs) for information assortment, introduced that it introduced in over $1 million in seed funding. The corporate goals for JaiaBot to be a low-cost, multivehicle system that may be person configured for a variety of capabilities. The software program for the corporate's robots is totally open supply and accessible utilizing gpl and lgpl licenses. JaiaBots are micro-sized, autonomous, hybrid floor and subsurface autos.