1st Workshop BOOK - project RHEA

Robotics and associated High technologies and Equipment for Agriculture
The final decision on field spraying depends heavily on weather conditions.
Consequently, the system includes a set of decision rules based on the expected
wind, temperatures and rainfall at treatment time and in the following 24 h (Fig. 4).
5. Step 4: Unit distribution & path planning
Numerous planning methods for obtaining the routes that allow an efficient
operation of agricultural vehicles have been proposed (Stoll, 2003; Jin and Tang,
2010; Taix et al., 2006). These methods take into account, for only one vehicle,
different factors: the strategy of operation, the surrounding areas, the geometry of
the field, field-specific data (size, slope, obstacles in the field), specific restriction
on the machinery (p.e. turning radius), or the operation direction. The change of
the direction in the headlands is an important issue because of the long time
needed for this operation. Stoll (2003) has calculated the turning paths keeping in
mind the effective width, the minimum turning radius, the driving speed and the
turning acceleration of the vehicle. It also includes additional time to consider the
change of the direction in the turning.
Path planning taking into account the previous factors is a very complex process
that requires rather sophisticated tools in order to search the optimal solution. The
problem becomes even more complex when a fleet of robots is used to perform
the herbicide treatment. The problem can be enunciated as follows (Conesa-Muñoz
and Ribeiro, 2011): Given a set of robots with certain features (i.e., herbicide
loading capacity, motion characteristics, width of the spraying boom), a field with
specific dimensions, a crop growing in rows and a map of the weed patches, the
aim is to find the subset of robots and associated paths that ensure the whole
cover of the weed with the minimum cost (Fig. 5). The solution of this problem can
be faced with a genetic algorithm approach where the fitness function considers
several of the factors above explained (Conesa-Muñoz and Ribeiro, 2011).
6 Step 5: Online decisions
Although the prescription map provides basic information of the field areas that
should be sprayed, this information needs to be contrasted with that obtained at
spraying time with cameras or sensors that detect weed presence and discriminate
different weed types. Once the detected weed patch has been considered as a
suitable target for spraying, a fast-response controller will regulate discharge of the
different herbicides in each individual nozzle (Fig. 6). Decision making could be
made using a set of decision rules similar to those used in the long-term decision
module.
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