MATERIALES Y MÉTODOS Material vegetal

One key drawback of using an AUV over human dive surveys is semantic understand- ing. A scientific diver can quickly see whether the selected site meets the scientific goals. If the goal is to “explore a kelp forest known to be roughly in this area”, it is entirely possible for the AUV to capture an entire mission of sand adjacent to the forest, due to the lack of semantic feedback. The waste of actual mission time is compounded with the time required to set up the mission, reaching the sea floor, recover the vehicle, charge and reset ready for redeployment.

Potential applications of onboard classification include:

• Including semantic goals in an AUV mission plan, such that the AUV can

automatically abort the mission if they are not met (e.g. define a geographic path as normal, and note that the dive is intended to be on a kelp forest).

• Using an acoustic modem to provide feedback on dive progress. While the

bandwidth of an acoustic modem is too low to transmit images back to the ship during a survey, it would be possible to transmit summary statistics for semantic content in the dive as it progresses. This would allow researchers to abort a mission manually if desired, or even plan and deploy multiple AUVs

7.2 Extensions, Applications and Future Work

in succession, as the area is explored.

• Adaptation of the path plan based on semantic content. In an unexplored

area, it is common to instruct the AUV to survey in a straight line for several kilometres (known as a “transect”). Subsequent dives may then be planned to cover areas of interest in more detail. With onboard classification, the AUV could autonomously add this extra coverage while doing the transect (some relevant work mapping hydrothermal vents was performed in [53]).

• New types of purely semantic missions could be defined, such as following the

boundary between sand and kelp forest.

Given the expense of running the AUV from a research vessel, onboard classification has the potential to significantly reduce the cost of running AUV surveys by making individual missions more effective, and greatly reducing the risk of wasted missions that capture irrelevant information (such as large tracts of sand).

The term “adaptive planning” is used in two main contexts with AUVs. Inter-

mission planning (such as in [74, 11]) seeks to find the optimal location and plan for a new mission, based on some general model of the habitat. This model may be derived from classification on previous dive images, ship-borne multibeam surveys over the area, satellite data, or other means.

Adaptive planning in this work refers to intra-mission planning, where the aim is

to adapt a dive mission on-the-fly in response to the data stream being collected by the AUV.

In [11], the benefit of an AUV adapting a mission in situ is studied, given an unknown exploration environment, and limited communication ability with the operators. A method for following the boundary between two habitats is proposed, and tested on simulated data (with the assumption of a sensor that can accurately differentiate

Chapter 7 Conclusion

between habitat types). In [94], a proposal and field tests are documented, for a strategy that reacts to sidescan sonar data being collected by an AUV, aimed at maximising the quality of the data, rather than the semantic content. In [72], an adaptive sampling method for an AUV testing water column data is designed and field tested.

Very little work has been done in the field of adaptive planning based on real time classification. In [38], the authors note that they were unable to find any literature reporting real-time habitat classification in AUVs. The literature in the adaptive planning/sampling field typically either performs simulations assuming an accurate habitat classifier [11], or extends to field tests with very simple sensor driven tasks, such as following thermal gradients [72, 93].

The most recent relevant work is documented in [46], which builds a semantic per- ception model of the environment using topic modelling, and then plans a path to maximise semantic information acquisition. This was tested in the field using

Aqua, an amphibious AUV to follow a diver, and conduct sea floor exploration. The

scarcity of field tested algorithms among benthic following AUVs is perhaps due to the complexity of detecting benthic habitats using imagery (in both a computational and a model accuracy sense).

Earlier, field trials were conducted with the StarbugAUV in [38], using whole image

classification (using features extracted from automatically detected keypoints). The classifier made a limited hierarchical decision, first choosing between (fish, sea grass) and (sand, rocks), and then choosing the sub-category. Performance is reported as around 80-90% accuracy at the whole image level, on the training set of 800

images. They then implement the algorithm in real-time on the Starbug AUV,

computing features on 96 keypoints in each image (at a computational cost of 0.64s per image). The algorithm is then tested in a purely observational sense - the

7.2 Extensions, Applications and Future Work

real-time classification has no impact on the mission plan of the AUV.