Tutorials ========= .. toctree:: :maxdepth: 1 01-building-arena.ipynb 02-recording-animal-trajectory.ipynb 02a-orientation-background.ipynb 02b-orientation-3markers.ipynb 02c-orientation-2markers.ipynb 03-rendering-along-trajectory.ipynb 04-image-processing.ipynb 04-comparing.ipynb 04-optic-flow.ipynb 04-error-propagation.ipynb 05-classification.ipynb Average place-code vector homing -------------------------------- Homing with an average skyline vector consist of deriving the skyline \ or an approximation of it from the visual information. For example, \ ultra violet light is mostly present in the sky, and thus by summing \ ultra violet light along the elevation one may obtain the skyline. \ This approximation was inspired by the visual system of insect and has \ been succesffuly applied to model of homing (Basten and mallott), and robots (Thomas Stone). \ Once the skyline have been optained, the center of mass of it is calcualted. \ The center of mass of the skyline is a vector lying in the equatorial \ plane of the visual system (due to the sumation along the elevation). \ The center of mass of the skyline was succeffully applied in simulation \ and robots (Hafner, Mangan). The center of mass of the skyline, also refered as average skyline \ vector, at the goal and current location are compared by simple difference. \ The difference gives the homing vector, i.e. a vector proportional to \ the velocity of the agent. Our agent needs to have a function to convert its current state to a motion. \ This function, velocity, can be added as follow: .. literalinclude:: examples/asv_homing_grid.py :lines: 12-30 On a grid ~~~~~~~~~ By restricting the agent motion on a grid, we can used a database containing \ images rendered at pre defined location (the grid nodes). .. literalinclude:: examples/asv_homing_grid.py :lines: 36 And initialise the senses of our virtual agent .. literalinclude:: examples/asv_homing_grid.py :lines: 37 Now we have to initialise an agent moving on a grid (i.e. a GridAgent) .. literalinclude:: examples/asv_homing_grid.py :lines: 39 at an initial position .. literalinclude:: examples/asv_homing_grid.py :lines: 42-44 a mode of motion corresponding to the grid used in the database .. literalinclude:: examples/asv_homing_grid.py :lines: 47-51 Finally our agent is ready to fly for a number of step or until its velocity is null. .. literalinclude:: examples/asv_homing_grid.py :lines: 54-55