Determination of spatial configuration of an underwater swarm with minimum data
This addresses the localization challenge for underwater swarms in applications like environmental monitoring and surveillance, but it appears incremental as it builds on trilateration methods with adaptations for unknown beacon positions and limited data.
The paper tackles the problem of determining the relative spatial configuration of an underwater swarm of AUVs using minimal data from exchanged signals, proposing two trilateration-based methods that require multiple motion steps to resolve symmetries and optimize objective functions, with validation via simulation accounting for random errors.
The subject is the localization problem of an underwater swarm of autonomous underwater robots (AUV), in the frame of the HARNESS project; by localization, we mean the relative swarm configuration, i.e., the geometrical shape of the group. The result is achieved by using the signals that the robots exchange. The swarm is organized by rules and conceived to perform tasks, ranging from environmental monitoring to terrorism-attack surveillance. Two methods of determining the shape of the swarm, both based on trilateration calculation, are proposed. The first method focuses on the robot's speed. In this case, we use our knowledge of the speeds and distances between the machines, while the second method considers only distances and the orientation angles of the robots. Unlike a trilateration problem, we do not know the position of the beacons and this renders the problem a difficult one. Moreover, we have very few data. More than one step of motion is needed to resolve the multiple solutions found, owing to the symmetries of the system and optimization process of one or more objective functions leading to the final configuration. We subsequently checked our algorithm using a simulator taking into account random errors affecting the measurements