def calcAction(self):
# TODO: make this settable
soonThreshold = 50.0
# TODO: consider re-factoring using BrainConditional (or something like it)
# TODO: this is hardwired to only avoid static obstacles
if soonThreshold < self.percepts.myTimeToCollision() or (self.percepts.myNextCollider() and Inf != self.percepts.nextCollider.mass):
# No collision danger
time = self.percepts.getTime()
# How many milliseconds to wait after a potential collision was detected before
# resuming with the default controller. TODO: consider making a settable class variable.
delay = 0.5
if self.timeLastCollisionDetected < 0 or delay < time - self.timeLastCollisionDetected:
self.defaultBrain.calcAction()
self.action = self.defaultBrain.action
else:
# Just continue with last action.
# TODO: consider some time discounted blend of default controller and
# avoidance vector.
pass
return
self.timeLastCollisionDetected = self.percepts.getTime()
# Collision danger present so need to take evasive action.
self.rp = vec.normalize(self.percepts.myNextCollisionPoint() - self.percepts.myPosition(), self.rp)
self.tmp = util2D.perpendicularTo(self.rp, self.percepts.myNextCollider().normalTo(self.percepts.getMe(), self.tmp), self.tmp)[0]
assert util.isAlmostZero(vec.dot(self.rp, self.tmp))
self.action.setDirection(self.tmp)
# TODO: modulate the speed based on time until collision and whatever the defaultControler
# set it to.
self.action.setSpeed(1.0)
def calcAction(self):
# TODO: make this settable
soonThreshold = 50.0
# TODO: consider re-factoring using BrainConditional (or something like it)
# TODO: this is hardwired to only avoid static obstacles
if soonThreshold < self.percepts.myTimeToCollision() or (self.percepts.myNextCollider() and Inf != self.percepts.nextCollider.mass):
# No collision danger
time = self.percepts.getTime()
# How many milliseconds to wait after a potential collision was detected before
# resuming with the default controller. TODO: consider making a settable class variable.
delay = 0.5
if self.timeLastCollisionDetected < 0 or delay < time - self.timeLastCollisionDetected:
self.defaultBrain.calcAction()
self.action = self.defaultBrain.action
else:
# Just continue with last action.
# TODO: consider some time discounted blend of default controller and
# avoidance vector.
pass
return
self.timeLastCollisionDetected = self.percepts.getTime()
# Collision danger present so need to take evasive action.
self.rp = vec.normalize(self.percepts.myNextCollisionPoint() - self.percepts.myPosition(), self.rp)
self.tmp = util2D.perpendicularTo(self.rp, self.percepts.myNextCollider().normalTo(self.percepts.getMe(), self.tmp), self.tmp)[0]
assert util.isAlmostZero(vec.dot(self.rp, self.tmp))
self.action.setDirection(self.tmp)
# TODO: modulate the speed based on time until collision and whatever the defaultControler
# set it to.
self.action.setSpeed(1.0)
def resolveCollisions(self):
e = 0.75 # coefficient of restitution
minTagInterval = 3.0 # minimum time allowed between re-tagging
now = timer.getTime()
loopCount = 0
while True:
isCollision = False
for i in self.gs.characters:
for j in self.gs.characters:
if i == j:
continue
if not i.isColliding(j):
continue
isCollision = True
# We have to keep computing these in case they changed in a previous collision
uc = i.getVelocity()
mc = i.mass
self.t = i.normalTo(j, self.t)
self.n = util2D.perpendicularTo(self.t, self.n, self.nTmp)[0]
uct = dot(uc, self.t)
ucn = dot(uc, self.n)
mo = j.mass
uo = j.getVelocity()
uot = dot(uo, self.t)
uon = dot(uo, self.n)
k = (uct - uot)/(mc + mo)
vct = uct - (1 + e) * mo * k
vot = uot + (1 + e) * mc * k
i.setVelocity(vec.scale(self.t, vct, self.vt) + vec.scale(self.n, ucn, self.vn))
j.setVelocity(vec.scale(self.t, vot, self.vt) + vec.scale(self.n, uon, self.vn))
# TODO: limit re-tagging restriction to previously tagged character only
if j.tagged and minTagInterval < now - self.gs.lastTagTime:
self.gs.setTagged(i, j, now)
elif i.tagged and minTagInterval < now - self.gs.lastTagTime:
self.gs.setTagged(j, i, now)
for j in self.gs.nonCharacterObstacles:
if not i.isColliding(j):
continue
isCollision = True
uc = i.getVelocity()
self.t = i.normalTo(j, self.t)
self.n = util2D.perpendicularTo(self.t, self.n, self.nTmp)[0]
uct = dot(uc, self.t)
ucn = dot(uc, self.n)
vct = -e * uct
i.setVelocity(vec.scale(self.t, vct, self.vt) + vec.scale(self.n, ucn, self.vn))
if not isCollision:
break
assert loopCount < 1000, "something probably went wrong"
loopCount += 1
def resolveCollisions(self):
e = 0.75 # coefficient of restitution
minTagInterval = 3.0 # minimum time allowed between re-tagging
now = timer.getTime()
loopCount = 0
while True:
isCollision = False
for i in self.gs.characters:
for j in self.gs.characters:
if i == j:
continue
if not i.isColliding(j):
continue
isCollision = True
# We have to keep computing these in case they changed in a previous collision
uc = i.getVelocity()
mc = i.mass
self.t = i.normalTo(j, self.t)
self.n = util2D.perpendicularTo(self.t, self.n, self.nTmp)[0]
uct = dot(uc, self.t)
ucn = dot(uc, self.n)
mo = j.mass
uo = j.getVelocity()
uot = dot(uo, self.t)
uon = dot(uo, self.n)
k = (uct - uot)/(mc + mo)
vct = uct - (1 + e) * mo * k
vot = uot + (1 + e) * mc * k
i.setVelocity(vec.scale(self.t, vct, self.vt) + vec.scale(self.n, ucn, self.vn))
j.setVelocity(vec.scale(self.t, vot, self.vt) + vec.scale(self.n, uon, self.vn))
# TODO: limit re-tagging restriction to previously tagged character only
if j.tagged and minTagInterval < now - self.gs.lastTagTime:
self.gs.setTagged(i, j, now)
elif i.tagged and minTagInterval < now - self.gs.lastTagTime:
self.gs.setTagged(j, i, now)
for j in self.gs.nonCharacterObstacles:
if not i.isColliding(j):
continue
isCollision = True
uc = i.getVelocity()
self.t = i.normalTo(j, self.t)
self.n = util2D.perpendicularTo(self.t, self.n, self.nTmp)[0]
uct = dot(uc, self.t)
ucn = dot(uc, self.n)
vct = -e * uct
i.setVelocity(vec.scale(self.t, vct, self.vt) + vec.scale(self.n, ucn, self.vn))
if not isCollision:
break
assert loopCount < 1000, "something probably went wrong"
loopCount += 1
