Cooperative autonomous marine vehicles for adaptive passive acoustic monitoring

This research project will aim to develop a swarm of autonomous marine vehicles that will be able to perform acoustic monitoring of the near-shore environment. This will include both the design of the hardware of the robotic systems and the implementation of the intelligent software that will allow the vehicles to perform the task in a cooperative manner.
The robotic vehicles will make use of hydrophone sensors to passively monitor the marine environment, in order to detect and locate individual sources of noise below the surface of the sea (i.e. marine mammals, sub-marine vehicles, under-water turbines etc.).
Initially, a range of current methods used to implement intelligent navigation of swarm robots will be examined and evaluated based the amount of information required to be exchanged, as well as the behaviour of the robots during navigation. New methods will be created based on the results.
For the implementation of new methods, machine learning will be used, in an attempt to combine conventional artificial intelligence with swarm robotics. Based on the literature review done so far, it seems that this is a task that has not yet received enough attention (Brambilla et al., 2013). Therefore the ideas that will be produced can advance the field in new directions.
The swarm intelligence algorithms will be tested using simple simulated environments, to ensure that the basic ideas behind the cooperative behaviour are implemented properly. The behaviour of the robotic platforms will be simulated using robot simulation software. In the later stages of the project, the software will be tested on real robotic platforms.
During the project, there will be use of equipment and facilities provided by the National Oceanography Centre (NOC, 2017), as part of the NEXUSS program, that this project is part of.

Objectives:
Although the swarm robotics literature consists of many proposed navigation algorithms, not many have been tested in real environments (Brambilla et al., 2013) (Tan and Zheng, 2013). This project will aim to fill the gap by creating real platforms that will be able to demonstrate the capabilities of the algorithms used.
The creation of a general navigation algorithm for use in the development of other swarm robotics projects. This will be one of the first attempts in achieving something of this scale in the field of swarm robotics.

Challenges:
Platform Complexity/ Platform Quantity: For the platforms to be as cheap and easily assembled as possible, their hardware complexity will need to be limited. For this project, this can be compensated by using a larger number of platforms for the swarm. On the other hand this can increase the complexity of the navigation and communication tasks. Therefore, a proper ratio between hardware complexity and software complexity needs to be identified.
Distributed Processing/ Centralised Processing: Distributed processing suggests that each agent makes decisions for itself by itself, which can require complex software in order for the swarm to operate properly. On the other hand, centralised processing suggests that one of the agents is the leader and every other agent is subject to its commands. This can have its own challenges, like the case that something happens to the leader and the group is unable to finish the task. Identifying where the system should optimally lie in the distributed/centralised processing spectrum will be a big challenge of this project.

References:
Brambilla, M., Ferrante, E., Birattari, M. and Dorigo, M. (2013). Swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 7(1), pp.1-41.
Tan, Y. and Zheng, Z. (2013). Research Advance in Swarm Robotics. Defence Technology, 9(1), pp.18-39.
NOC. (2017). Home Page. Retrieved from National Oceanography Centre Official Website: https://noc.ac.uk/