def get_target(self):
"""
Figure out where to move towards.
We're trying to move towards the mid-point of two other robots,
while also keeping them at a maximal distance.
"""
(a, b) = self.get_target_bots()
mid = midpoint(a.location, b.location)
dist_mid = self.dist_from(mid)
dist_a = self.dist_from_bot(a)
dist_b = self.dist_from_bot(b)
# Attempt to maximise distances, while reducing distance to mid
best = mid
score = self.score(mid)
def in_line():
for i in xrange(-50, 50, 5):
i /= 10
v = length_towards(i, mid)
there = self.location + v
yield there
def other():
"""Perpedicular to the mid point"""
v = mid - self.location
ref = Vector(v.y, v.x) + self.location
for i in xrange(-50, 50, 5):
i /= 10
v = length_towards(i, ref)
there = self.location + v
yield there
def rand():
"""Consider some random places"""
for i in xrange(100):
x = randint(0, self._arena.width)
y = randint(0, self._arena.height)
v = Vector(x, y)
there = self.location + v
yield there
def possibles():
for alg in [in_line, other, rand]:
for pos in alg():
yield pos
for pos in possibles():
if not self._arena.position_valid(pos):
continue
s = self.score(pos)
if s > score:
best = pos
return best
def score(self, position):
(a, b) = self.get_target_bots()
mid = midpoint(a.location, b.location)
dist_mid = abs(position - mid)
dist_a = abs(position - a.location)
dist_b = abs(position - b.location)
# Minimise dist_mid, Maximise dist_{a,b}
score = dist_a * 1.3 + dist_b - ( 1.8 * dist_mid )
return score
def test_midpoint2(self):
exp = Point(2, 2)
mid = midpoint(self._a, self._b)
util.assertEqual(exp, mid)
def test_midpoint(self):
exp = Point(0, 0)
mid = midpoint(self._a, self._c)
util.assertEqual(exp, mid)
