# and comprises the error margin, the angle margin for neighbouring triangles
# and the minimum area, in that order.
# If no configuration is set, a default configuration is used.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.05])
afftool.setAffordanceConfig('Lean', [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel("hpp-affordance-corba", "darpa", "planning", r)
afftool.visualiseAffordances('Support', r, SupportColour)
# If affordance configuration is changed, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r)
# Next, change the configuration settings for affordance type 'Support'.
# Notice that only the minimum area is changed.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.1])
# Now, reanalyse the scene with updated requirements
afftool.analyseAll()
# And visualise. Note that the below function may be used to visualise one
# object or all objects, depending on its parameters
afftool.visualiseAffordances('Support', r, SupportColour)
# Now fewer support surfaces were found due to the stricter requirement.
afftool.deleteAffordances(r)
# Go back to default values for affordance configuration for all affordance types
afftool.resetAffordanceConfig()
afftool.analyseAll()
afftool.visualiseAffordances('Support', r, SupportColour)
# This procedure may be repeated, and functions that take only one
afftool = AffordanceTool()
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.05])
afftool.setAffordanceConfig('Lean', [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel("hpp-affordance-corba", "darpa", "planning", r)
afftool.loadObstacleModel("hpp-affordance-corba", "box", "box1", r)
afftool.visualiseAffordances('Support', r, SupportColour)
# If an object is translated or rotated, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r, 'box1/base_link_0')
# Next, move the obstacle and recompute its position (updates viewer)
ps.moveObstacle('box1/base_link_0', [0, 1, 0.25, 1, 0, 0, 0])
r.computeObjectPosition()
# Now, reanalyse the object that was moved
afftool.analyseObject('box1/base_link_0')
# And visualise. Note that the below function may be used to visualise one
# object or all objects, depending on its parameters
afftool.visualiseAffordances('Support', r, SupportColour, 'box1/base_link_0')
afftool.visualiseAffordances('Lean', r, LeanColour, 'box1/base_link_0')
# Create more obstacles to further demonstrate the analysis process:
# Some Lean affordances are no more valid due to the orientation of
# the obstacle
afftool.loadObstacleModel("hpp-affordance-corba", "box", "box2", r)
# Setting initial and goal configurations
q_init = rbprmBuilder.getCurrentConfig ();
q_init [0:3] = [-2, 0, 0.63]; rbprmBuilder.setCurrentConfig (q_init); r (q_init)
q_goal = q_init [::]
q_goal [0:3] = [3, 0, 0.63]; r (q_goal)
ps.setInitialConfig (q_init)
ps.addGoalConfig (q_goal)
from hpp.corbaserver.affordance.affordance import AffordanceTool
afftool = AffordanceTool ()
afftool.loadObstacleModel (packageName, "darpa", "planning", r)
afftool.loadObstacleModel ("hpp-ompl-benchmark", "cubicles_robot", "robo", r)
afftool.visualiseAffordances('Support', r, [0.25, 0.5, 0.5])
afftool.deleteAffordances(r,'robo/base_link_0')
ps.moveObstacle('robo/base_link_0', [0,-0.75,0, 0.5,0.5,0.5,0.5])
r.computeObjectPosition()
afftool.analyseObject('robo/base_link_0')
afftool.visualiseAffordances('Support', r, [0.75, 0.75, 0.1], 'robo/base_link_0')
# Choosing RBPRM shooter and path validation methods.
# Note that the standard RRT algorithm is used.
ps.client.problem.selectConFigurationShooter("RbprmShooter")
ps.client.problem.selectPathValidation("RbprmPathValidation",0.05)
# Solve the problem
t = ps.solve ()
# Playing the computed path
from hpp.gepetto import PathPlayer
# and comprises the error margin, the angle margin for neighbouring triangles
# and the minimum area, in that order.
# If no configuration is set, a default configuration is used.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.05])
afftool.setAffordanceConfig('Lean', [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel ("hpp-affordance-corba", "darpa", "planning", r)
afftool.visualiseAffordances('Support', r, SupportColour)
# If affordance configuration is changed, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r)
# Next, change the configuration settings for affordance type 'Support'.
# Notice that only the minimum area is changed.
afftool.setAffordanceConfig('Support', [0.3, 0.3, 0.1])
# Now, reanalyse the scene with updated requirements
afftool.analyseAll()
# And visualise. Note that the below function may be used to visualise one
# object or all objects, depending on its parameters
afftool.visualiseAffordances('Support', r, SupportColour)
# Now fewer support surfaces were found due to the stricter requirement.
afftool.deleteAffordances(r)
# Go back to default values for affordance configuration for all affordance types
afftool.resetAffordanceConfig()
afftool.analyseAll()
afftool.visualiseAffordances('Support', r, SupportColour)
# This procedure may be repeated, and functions that take only one
afftool = AffordanceTool()
afftool.setAffordanceConfig("Support", [0.3, 0.3, 0.05])
afftool.setAffordanceConfig("Lean", [0.1, 0.3, 0.05])
# Load obstacle models and visualise affordances. When loading an obstacle,
# the affordance analysis is done automatically.
afftool.loadObstacleModel("hpp-affordance-corba", "darpa", "planning", r)
afftool.loadObstacleModel("hpp-affordance-corba", "box", "box1", r)
afftool.visualiseAffordances("Support", r, SupportColour)
# If an object is translated or rotated, the affordance analysis must
# be relaunched.
# First, delete the already created affordance objects (this function also
# deletes them from viewer)
afftool.deleteAffordances(r, "box1/base_link_0")
# Next, move the obstacle and recompute its position (updates viewer)
ps.moveObstacle("box1/base_link_0", [0, 1, 0.25, 1, 0, 0, 0])
r.computeObjectPosition()
# Now, reanalyse the object that was moved
afftool.analyseObject("box1/base_link_0")
# And visualise. Note that the below function may be used to visualise one
# object or all objects, depending on its parameters
afftool.visualiseAffordances("Support", r, SupportColour, "box1/base_link_0")
afftool.visualiseAffordances("Lean", r, LeanColour, "box1/base_link_0")
# Create more obstacles to further demonstrate the analysis process:
# Some Lean affordances are no more valid due to the orientation of
# the obstacle
afftool.loadObstacleModel("hpp-affordance-corba", "box", "box2", r)
