Underwater Robots - Gianluca Antonelli.pdf

σ p,d( t ) , σ s,d( t )
CLIK
9.2 Kinematic Control of AUVs 229
η r,1 , 1 , ˙η r,1 , 1
η r,1 ,n, ˙η r,1 ,n
vehicle 1
vehicle n
η 1 , 1 , ˙η 1 , 1
η 1 ,n, ˙η 1 ,n
Fig. 9.1. Sketch ofthe centralized kinematic control strategy for platoons
The structure ofsuch acentralized platoon control algorithm is sketched
in Figure 9.1. Itmust be remarked that each vehicle is equipped with a
dynamic low level controller in charge of following the reference trajectory.
This structure can be implemented on aplatoon of AUVs without modifying
their dynamic controllers that can be tuned, thus, at their best performance.
The dynamic lowlevel controller is not affected by the algorithm implemented
in the CLIK. It is sufficient that itimplements akind of input interpolation
to match the different sampling frequency of the two loops.
In view of the underwater communicationlimitations, it must be remarked
that only one-way communication channel must be strictly set-up; communication
among robots, thus, is not required. Moreover, the communication
channel is in charge of updating the vehicles’ references; itisnot, thus, inside
the control loop with obvious advantages in terms of stability and performance.
Ahigh-level supervisor might benefit from sensor measurements. Those
can be vehicle-fixed sensors (e.g., sonar measurements or video camera) or
other distributed sensors (e.g., baseline acoustic measurements). These variables
can be acquired from different sources atdifferent sampling frequencies
and merged using aKalman filtering approach. In this case, however, communication
from each vehicle to the centralized algorithm may berequired.
Task Function for Platoon Average Position
An example of task function for platoon formation control is one expressing
the mean value of all the vehicles’ positions as asynthetic data about the
platoon location. In this case, the 3-dimensional task function σ a is simply
given by: