Motivation

Current research and development work in the field of underwater vehicles makes it possible for specialized vehicles, i.e. small floating robots, to be used for various tasks in the maritime sector. They are mainly used for tasks that cannot be performed efficiently by humans, e.g. as helpers in exploring the underwater world, in maintenance work in the harbor area or in observing and documenting the underwater world. By deviding complex missions, such as monitoring a behavior of a fish, into subtasks performed by a swarm of robots instead of a single one, an increase in time and energy efficiency can be achieved. In addition, the use of a swarm makes it possible to acts autounomously and decentralizes with a limited communication effort.

Goals and Contributions

The overarching goal of a swarm, regardless of whether it is an agent, robot or animal, is to survive together. In the case of autonomous underwater vehicles, this means that none of the robots should fail in terms of energy storage. If the energy storage, i.e. a battery, is empty, the robot is switched off, sinks into the water and is unlikely to be found again - the overall goal is missed. To ensure that the swarm can operate autonomously for as long as possible, ongoing research will focus on energy-aware cooperation between individuals in the swarm to extend the life of all devices and minimize failures. To achieve these goals, we will implement intelligent mission management algorithms that allow the swarm to make independent and autonomous decisions. To this end, we will create a simulation environment to test the algorithms and validate the expected results. In the simulation we can choose different load models for the power consumption and different swarm sizes. For the simulation environment, different mathematical models, i.e. the energy storage and the individual consumers, have to be created and identified using real measurement data. Proof-of-concept experiments with a limited number of underwater vehicles will be used to validate the simulation results. As a result, the algorithms for intelligent mission management need to be implemented in such a way that they can cope with all the uncertainties of a real-world environment that could not be modelled.