On land, localization or ranging is typically performed by using electromagnetic waves. In an underwater environment, this becomes difficult, as electromagnetic waves dampen out fast. As a solution often acoustic waves are used to perform localization. Because of the complexity of underwater acoustic propagation, an acoustic propagation model is used of which the predicted output for different candidate locations is compared to the measurements. Methods to localize low-frequency sound sources with widely spaced receivers have been thoroughly studied in literature. More recently, also for high-frequency sources and receivers with a line-array structure successful localization has been demonstrated. In this thesis, the typical methods used to achieve this are gathered and typical challenges of these methods are identified. Based on these challenges, a new method is proposed, in an attempt to reduce the effects caused by these challenges and thus improve the localization performance. Also, Gibbs sampling is used to perform Bayesian inversion on the localization problem and simultaneously estimate environmental parameters. The results show that a high-frequency acoustic source at a range of 1500-2000m in an ocean environment with a depth of ∼ 200 meters can be localized by using the proposed method, including an accurately estimated tilt of the receiver array.

In this thesis we will walk you through the design choices we made in designing a battery management system (BMS) for the Zebro robot. This specific BMS is designed as a subcomponent of an autonomous charging system. The main goal of this BMS is to safely charge and discharge the systems battery, while providing the system with relevant data on the battery status. The BMS consists of multiple subsystems, the most important being a charger, a cell balancer, a power management system and a main control unit tying it all togheter.