## 1 Introduction  
The EU Horizon 2020 FET project subCULTron aims to develop a heterogeneous swarm of marine robots and deploy it on an unsupervised mission of long-term marine monitoring and exploration in the challenging environment of the lagoon surrounding Venice, Italy [1, 2]. The system consists of multiple robotic agents of three distinct types, and was envisioned as an artificial marine ecosystem. The three agent types are as follows: five over-actuated autonomous surface vehicles (ASV) called aPads or artificial lily pads; a small swarm of highly mobile aFish or artificial fish; and 120 underwater sensor nodes with limited mobility called aMussels, or artificial mussels. This paper focuses on an algorithm developed primarily for interaction between the aPad and aMussel agent types. The aPad (Figure 1) is a modular robotic platform with powerful batteries, an X-shaped thruster configuration which makes it highly maneuverable, and the highest computational power in the swarm. Equipped with mechanical docking stations and inductive charging coils, one of the aPad's main roles and most important abilities is to transport and wirelessly charge up to four other swarm agents at a time. With its wireless router, two antennas, and an acoustic modem, it also plays an important role in the swarm's communication structures and possible underwater-swarm-to-surface-observer information transfer. The aMussel (Figure 2) carries a wide selection of sensors, including temperature, pressure, turbidity, ambient light, and dissolved oxygen. It has very limited movement capabilities, equipped only with a variable buoyancy system consisting of a piston and diaphragm which allows it to float to the water's surface, sink to the sea bed, or stay hovering at a set depth. For deployment and relocation to specific points in its environment, the aMussel requires the help of the aPad. Once deployed, the numerous aMussels use the miniaturized acoustic modems mounted on their top caps to function as an underwater acoustic sensor network (UASN) on a mission of long-term data collection and environmental observation. To achieve long-term autonomy necessary for the completion of its monitoring mission, energy sharing within the subCULTron heterogeneous robotic swarm has been a key part of the design and development of the system as a whole. The primary goal is to have the aPad surface platform that is quite energy-rich transfer some of its energy to the much more limited aMussel, using specially designed hardware and software solutions. The initial implementation of the energy sharing system, as well as the results of some early proof-of-concept experiments, have been described in [4]. Autonomous underwater vehicle (AUV) docking is a concept often explored in the literature, and an important part of marine operations. Recently, Ref. [5] gave an overview of autonomous docking of AUVs with an underwater station, highlighting and examining the most frequently used mechanical docking concepts as well as navigation, guidance, and control methodologies. Adaptive and robust solutions are sought after, due to the need for the systems to perform well in a variety of conditions in the field. Fuzzy logic is frequently used for this purpose, especially as a mechanism to handle unknown disturbances such as water currents, for example in [6, 7]. Several reinforcement learning strategies are studied in the context of underwater AUV docking, and compared with classical  
Figure 1: Final version of the aPad platform ready for deployment on-site (**left**) and an aPad fully loaded with four docked aMussels (**right**). Figure 2: The aMussel underwater robot and sensor node. Note in particular the inductive charging coils on the narrow neck below the top cap segment [3]. and optimal control techniques in [8]. Computer vision plays a large role in many docking approaches, such as in [9], where an AUV homes in on four markers placed around a docking station using a system of two cameras, combining binocular and monocular vision positioning methods.