Trajectory planning

Trajectory planning

Navigation and mapping

Existing geographical information for trajectory planning

can be represented using knowledge-based frameworks.

However, mapping of the environment depends on the uncertainty

in the position of the vehicle when the obstacles

are observed and mapped. Navigation in unknown underwater

environments entails high levels of uncertainty.

Geolocating a vehicle on theWorld’s surface can be accurately

performed by using Global Positioning System (GPS)

receivers. But GPS does not work underwater as the radio

signals on which it depends cannot pass through water. Dead

reckoning is the standard technique used underwater and involves

use of Inertial Measurement Units (IMUs), a combination

of accelerometers and gyroscopes. Abbe error, magnification

of angular error over distance, can be detrimental

to dead reckoning. Inertial Navigation Systems (INS) integrate

accelerations and rates to provide a Kalman filtered

navigation solution. They use inputs from acoustic transmitters

and receivers to pin-point the platform’s location. However,

the range of these systems is limited to no more than a

few nautical miles thus restricting the vehicles autonomy. A

better approach involves aiding the INS with a Doppler Velocity

Log (DVL) sensor that measures the displacement rate

over the seabed. No matter how accurate the sensors are, the

errors in these systems grow with time and the platform becomes

progressively lost unless it is able to obtain external

references from acoustic devices or from a GPS receiver on

the surface. This uncertainty in the vehicle’s location gets

passed to the geolocation of the observed elements in the

environement during the mapping process.

A promising alternative is Simultaneous Localisation And

Mapping (SLAM). Using SLAM a platform maps the environment

and uses the map to localise itself in it. The map can

be georeferenced if the platform maintains an estimate of its

absolute position when the process of mapping is started.

A recent development has shown that it is possible to postprocess

and smooth the SLAM solution using the Rauch-

Tung-Striebel smoother (see Fig.7). This new technique,

coined SLAM-RTS, has been used to create better maps of

the environment and to help the trajectory planning systems

to accurately locate the sensed obstacles in the map (Patr´on

and Tena-Ruiz 2006).