diff --git a/environment/static/dual_kuka_env.py b/environment/static/dual_kuka_env.py new file mode 100644 index 0000000..902f006 --- /dev/null +++ b/environment/static/dual_kuka_env.py @@ -0,0 +1,19 @@ +import pybullet as p +from environment.static_env import StaticEnv +from robot.multi_robot.dual_kuka_robot import DualKukaRobot + +class DualKukaEnv(StaticEnv): + + def __init__(self, objects, robot_config=None): + if robot_config is None: + robot = DualKukaRobot() + else: + robot = DualKukaRobot(**robot_config) + super(DualKukaEnv, self).__init__(objects, robot) + + def set_camera_angle(self): + p.resetDebugVisualizerCamera( + cameraDistance=1.57699, + cameraYaw=203.809, + cameraPitch=-30.335, + cameraTargetPosition=[0, 0, 0.7]) diff --git a/environment/static/kuka_2arm_env.py b/environment/static/kuka_2arm_env.py deleted file mode 100644 index 8fc5042..0000000 --- a/environment/static/kuka_2arm_env.py +++ /dev/null @@ -1,362 +0,0 @@ -import numpy as np -import pybullet as p -from time import sleep -import pybullet_data -import pickle -from environment.static_env import StaticEnv - - -class Kuka2Env(StaticEnv): - - RRT_EPS = 0.5 - kukaEndEffectorIndex = 6 - - def __init__(self, GUI=False, kuka_file="kuka_iiwa/model.urdf", map_file='data/static/kukas_14_3000.pkl'): - self.kuka_file = kuka_file - - self.collision_check_count = 0 - - with open(map_file, 'rb') as f: - self.problems = pickle.load(f) - self.order = list(range(len(self.problems))) - self.episode_i = 0 - - self.maps = {} - self.episode_i = 0 - self.collision_point = None - - if GUI: - p.connect(p.GUI, options='--background_color_red=1.0 --background_color_green=1.0 --background_color_blue=1.0') - else: - p.connect(p.DIRECT) - - p.setAdditionalSearchPath(pybullet_data.getDataPath()) - p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0) - - target = p.getDebugVisualizerCamera()[11] - p.resetDebugVisualizerCamera( - cameraDistance=1.57699, - cameraYaw=203.809, - cameraPitch=-30.335, - cameraTargetPosition=[0, 0, 0.7]) - - self.reset_env() - - def __str__(self): - return 'kuka'+str(self.config_dim) - - def reset_env(self, collision=True): - p.resetSimulation() - # p.loadURDF("plane.urdf", [0, 0, -1], useFixedBase=True) - if collision: - self.kukaId = p.loadURDF(self.kuka_file, [-0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True) - self.kukaId2 = p.loadURDF(self.kuka_file, [0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True) - else: - self.kukaId = p.loadURDF(self.kuka_file, [-0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, flags=p.URDF_IGNORE_COLLISION_SHAPES) - self.kukaId2 = p.loadURDF(self.kuka_file, [0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, flags=p.URDF_IGNORE_COLLISION_SHAPES) - p.performCollisionDetection() - self.config_dim = p.getNumJoints(self.kukaId) * 2 - self.pose_range = [(p.getJointInfo(self.kukaId, jointId)[8], p.getJointInfo(self.kukaId, jointId)[9]) for - jointId in - range(p.getNumJoints(self.kukaId))] * 2 - self.bound = np.array(self.pose_range).T.reshape(-1) - p.setGravity(0, 0, -10) - p.stepSimulation() - - def init_new_problem(self, index=None): - ''' - Initialize a new planning problem - ''' - - if index is None: - self.index = self.episode_i - else: - self.index = index - - obstacles, start, goal, path = self.problems[index] - - self.episode_i += 1 - self.episode_i = (self.episode_i) % len(self.order) - self.collision_check_count = 0 - self.collision_point = None - - self.reset_env() - - self.collision_point = None - - self.obstacles = obstacles - self.init_state = start - self.goal_state = goal - self.path = path - - for halfExtents, basePosition in obstacles: - self.create_voxel(halfExtents, basePosition) - - return self.get_problem() - - def get_problem(self, width=15, index=None): - if index is None: - problem = { - "map": np.array(self.obs_map(width)[1]).astype(float), - "init_state": self.init_state, - "goal_state": self.goal_state - } - self.maps[self.index] = problem - return problem - else: - return self.maps[index] - - def set_random_init_goal(self): - while True: - points = self.sample_n_points(n=2) - init, goal = points[0], points[1] - if np.sum(np.abs(init - goal)) != 0: - break - self.init_state, self.goal_state = init, goal - - - def obs_map(self, num): - resolution = 2./(num-1) - grid_pos = [np.linspace(-1., 1., num=num) for i in range(3)] - points_pos = np.meshgrid(*grid_pos) - points_pos = np.concatenate((points_pos[0].reshape(-1, 1), points_pos[1].reshape(-1, 1), points_pos[2].reshape(-1, 1)), - axis=-1) - points_obs = np.zeros(points_pos.shape[0]).astype(bool) - - for obstacle in self.obstacles: - obstacle_size, obstacle_base = obstacle - limit_low, limit_high = obstacle_base - obstacle_size, obstacle_base + obstacle_size - limit_low[2], limit_high[2] = limit_low[2] - 0.4, limit_high[2] - 0.4 # translate the point - bools = [] - for i in range(3): - obs_mask = np.zeros(num).astype(bool) - obs_mask[max(int((limit_low[i]+1)/resolution), 0):min((1+int((limit_high[i]+1)/resolution)), 1+int(2./resolution))] = True - bools.append(obs_mask) - current_obs = np.meshgrid(*bools) - current_obs = np.concatenate((current_obs[0].reshape(-1, 1), current_obs[1].reshape(-1, 1), current_obs[2].reshape(-1, 1)), - axis=-1) - points_obs = np.logical_or(points_obs, np.all(current_obs, axis=-1)) - return points_pos.reshape((num, num, num, -1)), points_obs.reshape((num, num, num)) - - def get_robot_points(self, config, end_point=True): - points = [] - self.set_config(config) - if end_point: - point = list(p.getLinkState(self.kukaId, 6)[0]) - points = points + point - point = list(p.getLinkState(self.kukaId2, 6)[0]) - points = points + point - else: - for effector in range(14): - if effector <= 6: - point = p.getLinkState(self.kukaId, effector)[0] - point = (point[0], point[1], point[2] - 0.4) - points.append(point) - else: - point = p.getLinkState(self.kukaId2, effector-7)[0] - point = (point[0], point[1], point[2] - 0.4) - points.append(point) - return points - - def set_config(self, config, kukaId=None, kukaId2=None): - if kukaId is None: - kukaId, kukaId2 = self.kukaId, self.kukaId2 - for i in range(len(config)): - if i <= 6: - p.resetJointState(kukaId, i, config[i]) - else: - p.resetJointState(kukaId2, i-7, config[i]) - - def create_voxel(self, halfExtents, basePosition): - groundColId = p.createCollisionShape(p.GEOM_BOX, halfExtents=halfExtents) - groundVisID = p.createVisualShape(shapeType=p.GEOM_BOX, - rgbaColor=np.random.uniform(0, 1, size=3).tolist() + [0.8], - specularColor=[0.4, .4, 0], - halfExtents=halfExtents) - groundId = p.createMultiBody(baseMass=0, - baseCollisionShapeIndex=groundColId, - baseVisualShapeIndex=groundVisID, - basePosition=basePosition) - return groundId - - def sample_n_points(self, n, need_negative=False): - if need_negative: - negative = [] - samples = [] - for i in range(n): - while True: - sample = self.uniform_sample() - if self._point_in_free_space(sample): - samples.append(sample) - break - elif need_negative: - negative.append(sample) - if not need_negative: - return samples - else: - return samples, negative - - def uniform_sample(self, n=1): - ''' - Uniformlly sample in the configuration space - ''' - sample = np.random.uniform(np.array(self.pose_range)[:, 0], np.array(self.pose_range)[:, 1], size=(n, self.config_dim)) - if n==1: - return sample.reshape(-1) - else: - return sample - - def distance(self, from_state, to_state): - ''' - Distance metric - ''' - - to_state = np.maximum(to_state, np.array(self.pose_range)[:, 0]) - to_state = np.minimum(to_state, np.array(self.pose_range)[:, 1]) - diff = np.abs(to_state - from_state) - - return np.sqrt(np.sum(diff ** 2, axis=-1)) - - def interpolate(self, from_state, to_state, ratio): - diff = to_state - from_state - - new_state = from_state + diff * ratio - new_state = np.maximum(new_state, np.array(self.pose_range)[:, 0]) - new_state = np.minimum(new_state, np.array(self.pose_range)[:, 1]) - - return new_state - - def in_goal_region(self, state): - ''' - Return whether a state(configuration) is in the goal region - ''' - return self.distance(state, self.goal_state) < self.RRT_EPS and \ - self._state_fp(state) - - def plot(self, path, make_gif=False): - path = np.array(path) - self.reset_env(collision=False) - - for halfExtents, basePosition in self.obstacles: - self.create_voxel(halfExtents, basePosition) - - self.set_config(path[0]) - - target_kukaId = p.loadURDF(self.kuka_file, [-0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, - flags=p.URDF_IGNORE_COLLISION_SHAPES) - target_kukaId2 = p.loadURDF(self.kuka_file, [0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, - flags=p.URDF_IGNORE_COLLISION_SHAPES) - self.set_config(path[-1], target_kukaId, target_kukaId2) - - p.setGravity(0, 0, -10) - p.configureDebugVisualizer(p.COV_ENABLE_GUI,0) - - p.stepSimulation() - - prev_pos1 = p.getLinkState(self.kukaId, self.kukaEndEffectorIndex)[0] - final_pos1 = p.getLinkState(target_kukaId, self.kukaEndEffectorIndex)[0] - prev_pos2 = p.getLinkState(self.kukaId2, self.kukaEndEffectorIndex)[0] - final_pos2 = p.getLinkState(target_kukaId2, self.kukaEndEffectorIndex)[0] - - if make_gif: - for _ in range(100): - p.stepSimulation() - sleep(0.1) - - gifs = [] - current_state_idx = 0 - - while True: - current_state = path[current_state_idx] - disp = path[current_state_idx + 1] - path[current_state_idx] - - d = self.distance(path[current_state_idx], path[current_state_idx + 1]) - - new_kuka = p.loadURDF(self.kuka_file, [-0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, - flags=p.URDF_IGNORE_COLLISION_SHAPES) - new_kuka2 = p.loadURDF(self.kuka_file, [0.5, 0, 0], [0, 0, 0, 1], useFixedBase=True, - flags=p.URDF_IGNORE_COLLISION_SHAPES) - - for data in p.getVisualShapeData(target_kukaId): - color = list(data[-1]) - color[-1] = 0.5 - p.changeVisualShape(new_kuka, data[1], rgbaColor=color) - - for data in p.getVisualShapeData(target_kukaId2): - color = list(data[-1]) - color[-1] = 0.5 - p.changeVisualShape(new_kuka2, data[1], rgbaColor=color) - - K = int(np.ceil(d / 0.5)) - for k in range(0, K): - - c = path[current_state_idx] + k * 1. / K * disp - self.set_config(c, new_kuka, new_kuka2) - # p.performCollisionDetection() - # p.stepSimulation() - new_pos1 = p.getLinkState(new_kuka, self.kukaEndEffectorIndex)[0] - new_pos2 = p.getLinkState(new_kuka2, self.kukaEndEffectorIndex)[0] - - p.addUserDebugLine(prev_pos1, new_pos1, [1, 0, 0], 10, 0) - p.addUserDebugLine(prev_pos2, new_pos2, [1, 0, 0], 10, 0) - - prev_pos1, prev_pos2 = new_pos1, new_pos2 - p.loadURDF("sphere2red.urdf", new_pos1, globalScaling=0.05, flags=p.URDF_IGNORE_COLLISION_SHAPES) - p.loadURDF("sphere2red.urdf", new_pos2, globalScaling=0.05, flags=p.URDF_IGNORE_COLLISION_SHAPES) - - if make_gif: - gifs.append(p.getCameraImage(width=1080, height=720, lightDirection=[0, 0, -1], shadow=0, - renderer=p.ER_BULLET_HARDWARE_OPENGL)[2]) - - current_state_idx += 1 - if current_state_idx == len(path) - 1: - self.set_config(path[-1], new_kuka, new_kuka2) - p.addUserDebugLine(prev_pos1, final_pos1, [1, 0, 0], 10, 0) - p.addUserDebugLine(prev_pos2, final_pos2, [1, 0, 0], 10, 0) - p.loadURDF("sphere2red.urdf", final_pos1, globalScaling=0.05, flags=p.URDF_IGNORE_COLLISION_SHAPES) - p.loadURDF("sphere2red.urdf", final_pos2, globalScaling=0.05, flags=p.URDF_IGNORE_COLLISION_SHAPES) - break - - return gifs - - # =====================internal collision check module======================= - -# def _valid_state(self, state): -# return (state >= np.array(self.pose_range)[:, 0]).all() and \ -# (state <= np.array(self.pose_range)[:, 1]).all() - -# def _point_in_free_space(self, state): -# if not self._valid_state(state): -# return False - -# self.set_config(state) -# p.performCollisionDetection() -# if (len(p.getContactPoints(self.kukaId)) == 0) and (len(p.getContactPoints(self.kukaId2)) == 0): -# self.collision_check_count += 1 -# return True -# else: -# self.collision_check_count += 1 -# self.collision_point = state -# return False - - def _state_fp(self, state): - return self._point_in_free_space(state) - - def _edge_fp(self, state, new_state): - self.k = 0 - assert state.size == new_state.size - - if not self._valid_state(state) or not self._valid_state(new_state): - return False - if not self._state_fp(state) or not self._state_fp(new_state): - return False - - disp = new_state - state - - d = self.distance(state, new_state) - K = int(d / self.RRT_EPS) - for k in range(0, K): - c = state + k * 1. / K * disp - if not self._state_fp(c): - return False - return True diff --git a/examples/load_environment.ipynb b/examples/load_environment.ipynb index e0b2d3d..58aa4d8 100644 --- a/examples/load_environment.ipynb +++ b/examples/load_environment.ipynb @@ -13,12 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os \n", - "import sys\n", - "import inspect\n", - "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n", - "parentdir = os.path.dirname(currentdir)\n", - "sys.path.insert(0, parentdir)" + "%cd -q .." ] }, { @@ -31,6 +26,7 @@ "from environment.static.ur5_env import UR5Env\n", "from environment.static.kuka_env import KukaEnv\n", "from environment.static.extend_kuka_env import ExtendKukaEnv\n", + "from environment.static.dual_kuka_env import DualKukaEnv\n", "from objects.static.voxel import VoxelObject" ] }, @@ -55,7 +51,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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Z2fZkuxv4PHCHme0B7kiOcffXgc3ATuA7wP0TcUXJuQZXmGiuOz0aFl7H6SKcGPDztpMDzukinA5yTFrx4WqXKnKWi04uu/sPuPC1Y7df4DkbgY2XUVfqDI66P975UVqaWqpdjoxA88xF9AVc8JetGbTNXUj78puvcGUiH0xXFoyh7Xt28LzuHJgaU5ZcS7apjdAhOGcLHSKH1pkLaWidWu1SRc6i4B4S31TocjatMEmXfMtkyGQJk6AOKwJ78HHyotXxn69IDanT4B4ueMfmO+946w3efHePRt0p4cQBXbkNhrY7tM5apKtipebUR3CPU0gPp1gu8ZWnHydMblIk6eB+zka8BYUe/RKWmjNxg7syqK+w5176HjveeuOK/1wZPT9n/9zj0ztfZJjrx0SqaoIF9/iHtY1g6x8o8JVvPUYQTvhVkKl3oVfKYHuxWMT1ryepMem91vwsYz/9cd63u8j3z2QyTGmdzKI5C+hceQO3df6c7uJc40onDxGVi/EvXOP9gbW9/9C3/w3Kp4/SOH1udYoUGUa6g3uMRtZ2wYMLy+camD5lGssXLqXzmhtZvXQFKxZdzaz2mTTk83pDKwUKR96GcoFMEtpRRWBnLN6KvWfoeXu7gltqSkqD+/JH2KMLa6O5qYnZ02dy9fwldK68kZuWX8dV8xbTPmUqGcsoqNPIIYORzTjmkKmYyh4MbqKQoNBbtRJFhpO+4L7MgLTzdob7ERka8g3MnTGLm1Zcxy3X3sx1V61k4az5tLW0JucoqNOu7FCKnNChIZs0OmDxypJCANm8U4705qTUlvQE92UE5cVG15lMlpamZpbMXcjaVTfxoVVrWDhrLkvmLqK5qYmMTbD3cAWAUhjff3tKA+Qy8Qtj8A3m0IGc01uGot6blBqTjuC+xNC+UGBnszmmtk1h5eJl3HLdzSyes5Drr17F3BmzyOc0P10vCu2rObloHblTL9NKgQwRUbGXTGMbxShDKdPE0fa1FNr10WVSW2o/uC83tA0a8g3MmNLOsoVLWbPiem6+5gZWLllO+2TNT9ezYstc3lvzhxwK+iAqkYnK5E+8QTB9FYFlCd2w1pmU2hZc/JuJXEE1HNyX9gZkJmPkc3nmdczm2qtWcvM1N7BmxfUsnbeI5sZm8rka7rJcUWFQplzsJ7AcWC6+qqHjluSrHr8EB3rIeqmaZYqcp0ZTbOShbUBrcytL5y3ihuWr6bzmBlYvvYa5M2bT0txMNpO96PeQOmVGJpMjkx3+r4F7RGMOFs5qu8KFiXywGgzuDw5tM6N98lRWLLqaRbPnc+v1H+LGZdcya/pMmhoaNe0ho2KZbBLc575uHI8izCLdHFBqTo0F9/mhnc/mmDNjFnNmzOLmlTdw63Uf4prFy2ifPJVcNqeglktmGGYZLJM973U0eGMpMy0pkdpTQ8Edh3ZjvoGZ7TNYsehqPrz6Zj68eg1L5i2iJZmfVlDLmLH4X3Dxa8qSkbXhOIbhhLozoNSkmgjupoZGPnX7L7N21U1cs3gZi+YsYGrb5Iq/VCJjzyyTrFo6+3VmSXg7ThQG1StQ5AJqIrivnr+EP//cnwC6IlGunFIwOJo+5+5SQ0dGGIaUyrrLo9SWmrgkMJPJaHQtV5S7s6url8HQPntG5P07c5cC5+VdpzRlIjXlosFtZk1m9rKZ/cTMXjezP07a283sWTPbkzxOq3jOg2a218x2mdmd49kBkUtRKIa8fTD+dJv4ftv+/n/+/mbZHD98/Rj9uu5dashIRtxF4DZ3vwG4EbjLzG4BHgC2uvsyYGtyjJmtAtYDq4G7gIfMTIuppaYcOFHg2OkCHkW4R/FjFCbb+/uGcfRULweO91e7ZJEhFw1ujw3e1zKfbA6sAzYl7ZuAe5L9dcAT7l5093eAvcDaMa1a5DKFkRMGZTwKicIg2cJ4iwKiKBzagnKJcrlc7ZJFhoxojtvMsma2HTgKPOvuLwGz3P0QQPI4Mzl9HrC/4uldSdu53/M+M9tmZtuOHTt2OX0QGTXDCcqFodAOwzLR4BYEREE5bg9KhMUeGnOa45baMaLgdvfQ3W8E5gNrzezaDzh9uHcYz3vVu/vD7t7p7p0dHR0jq1ZkjMyd3kxLpkBpoJegPEAYlAjKJcJyiSAoEgQlysV+Cj0nmTUlz/xZU6pdssiQUS0HdPfTZvY94rnrI2Y2x90Pmdkc4tE4xCPsytupzQcOjkWxImOlrTnPrdfN45vfe5NMNk8mmxu69D0Ky7hHhEEJjwLuvHUVTQ01sXJWBBjZqpIOM5ua7DcDnwDeBLYAG5LTNgBPJftbgPVm1mhmS4BlwMtjXbjI5TAz7v7YcmZPa6I00Euxv5tC7ykKvScp9ndTLPQQlIpMnjyFNSsXVrtckbOMZBgxB9iUrAzJAJvd/Wkz+yGw2czuBfYBnwZw99fNbDOwEwiA+91dVzBIzWltbmLG7AUcPN5HaaCHwdsuuMeXvE+eMZ+26fPpL0VMr3axIhUuGtzu/lPgpmHaTwC3X+A5G4GNl12dyDjqL4b021QWrLyVgb7T9Jw6hEcRuXwTk9rn0NDcRlAucvhUkQUdLdUuV2SIJu6kbh04MUA5ypFvbKKxdQpTZy2J31p3hpYJukcUSlpRIrWlJi55F6mGtw4XYOi2rhksk4kfk30svu3rW4d08Y3UFgW31KXInaOny/E6VU/uTTI0sI4vd8cdB7r7A4JQl7xL7VBwS10qliLeO9xLFJSTi28CoijAw/Dsi3KCMl1He9iz9x0GBgYoFotEkUJcqktz3FKXPAop9J4iYBJRFJDN5oc+Ccc9ii93D+MrKI8ce4/f/u0v0DF9KmbGgw8+yJo1a6rdBaljCm6pSydPHGXHc39J26KP0dqxlHxjC5lsHstk8CgiCsuEQYm+E/s4+JMtFLpP09t9CoBCoVDl6qXeKbilLu3fv599u35MZu92miZ10NDaTuPk2WSzecKwTLH7MKX+0xR7j0EUnHU/bk2VSLUpuKUuvfrqq7h7PKo+dYC+Uwcw2zH09XM/OGHwuLW1ldmzZ1/RWkXOpeCWulMsFnnllVfOa/+gT7mJ576d5uZmdFM0qTatKpG6s2vXLl577bVRP08frSe1QiNuqSvuzgsvvECxWAQuHsb6rEmpRRpxS10ZGBjg+9//frXLELksCm6pO42NjcClTX1oBC61QMEtdaWpqYl7772XKVOmkM3Gn2E9GMaVoTz4Ke+ZTIZ8Pk9bWxsrV65k+fLlQ88TqRbNcUtdMTNuu+02Zs+ezWOPPcbOnTvp6uqir68PM8PMhtZpt7S0sHDhQlatWsX69etZsWIFmUxGwS1Vp+CWupPJZLj22mvZuHEjhUKBw4cPs3v3bg4cOECxWGTp0qUALF++nNmzZ9Pc3KwVJVJTFNxSt8yMlpYWli5dOhTWImmgOW4RkZRRcIuIpIyCW0QkZRTcIiIpM+LgNrOsmb1mZk8nx+1m9qyZ7Ukep1Wc+6CZ7TWzXWZ253gULiJSr0Yz4v594I2K4weAre6+DNiaHGNmq4D1wGrgLuAhM9PCVxGRMTKi4Daz+cAvAl+qaF4HbEr2NwH3VLQ/4e5Fd38H2AusHZtyRURkpCPuLwB/AFR+9Mcsdz8EkDzOTNrnAfsrzutK2s5iZveZ2TYz23bs2LFRFy4iUq8uGtxm9kvAUXc//87zF3jKMG3n3ZnH3R92905379SN6UVERm4kV05+FPikmd0NNAGTzewR4IiZzXH3Q2Y2BzianN8FLKh4/nzg4FgWLSJSzy464nb3B919vrsvJn7T8Xl3/3VgC7AhOW0D8FSyvwVYb2aNZrYEWAa8POaVi4jUqcu5V8nngc1mdi+wD/g0gLu/bmabgZ1AANzv7uFlVyoiIsAog9vdvwd8L9k/Adx+gfM2AhsvszYRERmGrpwUEUkZBbeISMoouEVEUkbBLSKSMgpuEZGUUXCLiKSMgltEJGUU3CIiKaPgFhFJGQW3iEjKKLhFRFJGwS0ikjIKbhGRlFFwi4ikjIJbRCRlFNwiIimj4BYRSRkFt4hIyii4RURSRsEtIpIyCm4RkZQZUXCb2btm9jMz225m25K2djN71sz2JI/TKs5/0Mz2mtkuM7tzvIoXEalHoxlxf9zdb3T3zuT4AWCruy8DtibHmNkqYD2wGrgLeMjMsmNYs4hIXbucqZJ1wKZkfxNwT0X7E+5edPd3gL3A2sv4OSIiUmGkwe3Ad83sFTO7L2mb5e6HAJLHmUn7PGB/xXO7kjYRERkDuRGe91F3P2hmM4FnzezNDzjXhmnz806KfwHcB7Bw4cIRliEiIiMacbv7weTxKPAN4qmPI2Y2ByB5PJqc3gUsqHj6fODgMN/zYXfvdPfOjo6OS++BiEiduWhwm1mrmU0a3Ad+HtgBbAE2JKdtAJ5K9rcA682s0cyWAMuAl8e6cBGRejWSqZJZwDfMbPD8x9z9O2b2Y2Czmd0L7AM+DeDur5vZZmAnEAD3u3s4LtWLiNShiwa3u78N3DBM+wng9gs8ZyOw8bKrExGR8+jKSRGRlFFwi4ikjIJbRCRlFNwiIimj4BYRSRkFt4hIyii4RURSRsEtIpIyCm4RkZRRcIuIpIyCW0QkZRTcIiIpo+AWEUkZBbeISMoouEVEUkbBLSKSMgpuEZGUUXCLiKSMgltEJGUU3CIiKaPgFhFJmREFt5lNNbMnzexNM3vDzD5iZu1m9qyZ7Ukep1Wc/6CZ7TWzXWZ25/iVLyJSf0Y64v4L4Dvufg1wA/AG8ACw1d2XAVuTY8xsFbAeWA3cBTxkZtmxLlxEpF5dNLjNbDLwL4AvA7h7yd1PA+uATclpm4B7kv11wBPuXnT3d4C9wNqxLlxEpF6NZMS9FDgGfNXMXjOzL5lZKzDL3Q8BJI8zk/PnAfsrnt+VtImIyBgYSXDngDXAX7r7TUAfybTIBdgwbX7eSWb3mdk2M9t27NixERUrIiIjC+4uoMvdX0qOnyQO8iNmNgcgeTxacf6CiufPBw6e+03d/WF373T3zo6OjkutX0Sk7lw0uN39MLDfzFYkTbcDO4EtwIakbQPwVLK/BVhvZo1mtgRYBrw8plWLiNSx3AjP+w/Ao2bWALwN/BZx6G82s3uBfcCnAdz9dTPbTBzuAXC/u4djXrmISJ0aUXC7+3agc5gv3X6B8zcCGy+jLhERuQBdOSkikjIKbhGRlFFwi4ikjIJbRCRlFNwiIimj4BYRSRkFt4hIyii4RURSRsEtIpIyCm4RkZRRcIuIpIyCW0QkZRTcIiIpo+AWEUkZBbeISMoouEVEUkbBLSKSMgpuEZGUUXCLiKSMgltEJGUU3CIiKaPgFhFJmYsGt5mtMLPtFVu3mX3OzNrN7Fkz25M8Tqt4zoNmttfMdpnZnePbBRGR+nLR4Hb3Xe5+o7vfCNwM9APfAB4Atrr7MmBrcoyZrQLWA6uBu4CHzCw7TvWLiNSd0U6V3A685e7vAeuATUn7JuCeZH8d8IS7F939HWAvsHYsihURkdEH93rg8WR/lrsfAkgeZybt84D9Fc/pStrOYmb3mdk2M9t27NixUZYhIlK/RhzcZtYAfBL4+sVOHabNz2twf9jdO929s6OjY6RliIjUvdGMuH8BeNXdjyTHR8xsDkDyeDRp7wIWVDxvPnDwcgsVEZHYaIL7M7w/TQKwBdiQ7G8AnqpoX29mjWa2BFgGvHy5hYqISCw3kpPMrAW4A/j3Fc2fBzab2b3APuDTAO7+upltBnYCAXC/u4djWrWISB0bUXC7ez8w/Zy2E8SrTIY7fyOw8bKrExGR85j7ee8bXvkizHqAXdWu4wqbARyvdhFXUL31F+qvz+rv2Frk7sOu3BjRiPsK2OXundUu4koys2311Od66y/UX5/V3ytH9yoREUkZBbeISMrUSnA/XO0CqqDe+lxv/YX667P6e4XUxJuTIiIycrUy4hYRkRGqenCb2V3Jfbv3mtkD1a5nLJjZAjN7wczeMLPXzez3k/YJfQ9zM8ua2Wtm9nRyPNH7O9XMnjSzN5M/649M5D6b2X9MXs87zOxxM2uaaP01s6+Y2VEz21HRNuo+mtnNZvaz5Gv/18yGu4fTpXP3qm1AFngLWAo0AD8BVlWzpjHq1yJ4P6gAAALNSURBVBxgTbI/CdgNrAL+F/BA0v4A8D+T/VVJ3xuBJcn/k2y1+3EJ/f5PwGPA08nxRO/vJuDfJfsNwNSJ2mfiO3y+AzQnx5uB35xo/QX+BbAG2FHRNuo+Et/m4yPEN917BviFsayz2iPutcBed3/b3UvAE8T38041dz/k7q8m+z3AG8Qv/Al7D3Mzmw/8IvCliuaJ3N/JxH/Jvwzg7iV3P80E7jPxdR/NZpYDWohvHjeh+uvu/wScPKd5VH1Mbro32d1/6HGK/03Fc8ZEtYN7RPfuTjMzWwzcBLzEZd7DvMZ9AfgDIKpom8j9XQocA76aTA99ycxamaB9dvcDwP8mvi/RIeCMu3+XCdrfc4y2j/OS/XPbx0y1g3tE9+5OKzNrA/4O+Jy7d3/QqcO0peb/g5n9EnDU3V8Z6VOGaUtNfxM54n9S/6W73wT0kXx83wWkus/JvO464imBuUCrmf36Bz1lmLbU9HeELtTHce97tYN7wt6728zyxKH9qLv/fdI8Ue9h/lHgk2b2LvF0121m9ggTt78Q96HL3V9Kjp8kDvKJ2udPAO+4+zF3LwN/D9zKxO1vpdH2sSvZP7d9zFQ7uH8MLDOzJckn7Kwnvp93qiXvIH8ZeMPd/7ziSxPyHubu/qC7z3f3xcR/hs+7+68zQfsL4O6Hgf1mtiJpup34VsYTtc/7gFvMrCV5fd9O/N7NRO1vpVH1MZlO6TGzW5L/V/+24jljowbexb2beNXFW8AfVrueMerTx4j/afRTYHuy3U18a9ytwJ7ksb3iOX+Y/D/YxRi/A32F+/6veH9VyYTuL3AjsC35c/4mMG0i9xn4Y+BNYAfwNeLVFBOqv8QfFnMIKBOPnO+9lD4Cncn/p7eAL5Jc7DhWm66cFBFJmWpPlYiIyCgpuEVEUkbBLSKSMgpuEZGUUXCLiKSMgltEJGUU3CIiKaPgFhFJmf8P0z/xFYtMfPoAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -103,7 +111,7 @@ } ], "source": [ - "for Env in [SnakeEnv, UR5Env, KukaEnv, ExtendKukaEnv]:\n", + "for Env in [SnakeEnv, UR5Env, KukaEnv, ExtendKukaEnv, DualKukaEnv]:\n", " env = Env(objects=[VoxelObject(base_orientation=[0, 0, 0, 1], base_position=[1, 1, 1], half_extents=[0.2, 0.2, 0.2])])\n", " env.load(reconnect=False, GUI=True, light_height_z=0.1)\n", " plt.clf()\n", diff --git a/examples/object_follow_trajectory.ipynb b/examples/object_follow_trajectory.ipynb index 7390b95..378b4e2 100644 --- a/examples/object_follow_trajectory.ipynb +++ b/examples/object_follow_trajectory.ipynb @@ -89,13 +89,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" diff --git a/examples/robot_follow_trajectory.ipynb b/examples/robot_follow_trajectory.ipynb index 0eb7dfc..af50097 100644 --- a/examples/robot_follow_trajectory.ipynb +++ b/examples/robot_follow_trajectory.ipynb @@ -13,12 +13,7 @@ "metadata": {}, "outputs": [], "source": [ - "import os \n", - "import sys\n", - "import inspect\n", - "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n", - "parentdir = os.path.dirname(currentdir)\n", - "sys.path.insert(0, parentdir)" + "%cd -q .." ] }, { @@ -31,6 +26,10 @@ "from robot.extend_kuka_robot import ExtendKukaRobot\n", "from robot.snake_robot import SnakeRobot\n", "from robot.ur5_robot import UR5Robot\n", + "from robot.multi_robot.dual_kuka_robot import DualKukaRobot\n", + "\n", + "\n", + "\n", "from robot.abstract_robot import DynamicRobotFactory\n", "from objects.trajectory import WaypointLinearTrajectory" ] @@ -46,20 +45,21 @@ " gifs = []\n", " p.resetSimulation()\n", " p.setAdditionalSearchPath(pybullet_data.getDataPath())\n", - " plane = p.createCollisionShape(p.GEOM_PLANE)\n", - " p.createMultiBody(0, plane) \n", " p.configureDebugVisualizer(p.COV_ENABLE_GUI, 0, lightPosition = [0, 0, 0.1])\n", + " if isinstance(robot, SnakeRobot):\n", + " plane = p.createCollisionShape(p.GEOM_PLANE)\n", + " p.createMultiBody(0, plane) \n", " p.resetDebugVisualizerCamera(\n", " cameraDistance=distance,\n", - " cameraYaw=10,\n", - " cameraPitch=-45,\n", - " cameraTargetPosition=[0, 0, 0]) \n", + " cameraYaw=0,\n", + " cameraPitch=-60,\n", + " cameraTargetPosition=[0, -0.5, 1]) \n", " robot.load()\n", " for timestep in np.linspace(0, len(robot.trajectory.waypoints)-1, 100):\n", " robot.set_config_at_time(timestep)\n", " p.performCollisionDetection()\n", " sleep(0.1)\n", - " gifs.append(p.getCameraImage(width=1100, height=900, lightDirection=[1, 1, 1], shadow=1,\n", + " gifs.append(p.getCameraImage(width=360, height=360, lightDirection=[1, 1, 1], shadow=1,\n", " renderer=p.ER_BULLET_HARDWARE_OPENGL)[2]) \n", " return gifs" ] @@ -68,21 +68,95 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pybullet as p\n", "import pybullet_data\n", "import numpy as np\n", - "p.connect(p.GUI)\n", - "for Robot, distance in zip([KukaRobot, ExtendKukaRobot, SnakeRobot, UR5Robot], [2, 4, 2, 1]):\n", + "from utils.utils import save_gif\n", + "from IPython.display import HTML\n", + "import base64\n", + "p.connect(p.GUI, options='--background_color_red=1.0 --background_color_green=1.0 --background_color_blue=1.0')\n", + "for Robot, distance in zip([KukaRobot, ExtendKukaRobot, SnakeRobot, UR5Robot, DualKukaRobot], [0.5, 1.5, 5, 0.3, 0.5]):\n", " plt.clf()\n", " plt.close('all')\n", " robot = Robot()\n", " DynamicRobot = DynamicRobotFactory.create_dynamic_robot_class(Robot)\n", - " dynamic_robot = DynamicRobot(trajectory=WaypointLinearTrajectory(np.random.uniform(low=robot.limits_low, high=robot.limits_high, size=(10, robot.config_dim))))\n", - " create_traj(dynamic_robot, distance)\n", + " p.resetSimulation()\n", + " robot.load()\n", + " # generate collision-free trajectory\n", + " while True:\n", + " traj = WaypointLinearTrajectory(np.random.uniform(low=robot.limits_low, high=robot.limits_high, size=(3, robot.config_dim))) \n", + " if robot._edge_fp(traj.waypoints[0], traj.waypoints[1]) and robot._edge_fp(traj.waypoints[1], traj.waypoints[2]):\n", + " break\n", + " dynamic_robot = DynamicRobot(trajectory=traj)\n", + " gifs = create_traj(dynamic_robot, distance)\n", + " save_gif(gifs, f'data/visualization/Dynamic{Robot.__name__}.gif')\n", + " b64 = base64.b64encode(open(f'data/visualization/Dynamic{Robot.__name__}.gif', 'rb').read()).decode('ascii')\n", + " display(HTML(f'')) \n", "p.disconnect() " ] } diff --git a/robot/abstract_robot.py b/robot/abstract_robot.py index 57f08f7..e590261 100644 --- a/robot/abstract_robot.py +++ b/robot/abstract_robot.py @@ -112,8 +112,8 @@ def distance(self, from_state, to_state): ''' Distance metric ''' - to_state = np.maximum(to_state, np.array(self.pose_range)[:, 0]) - to_state = np.minimum(to_state, np.array(self.pose_range)[:, 1]) + to_state = np.maximum(to_state, np.array(self.limits_low)) + to_state = np.minimum(to_state, np.array(self.limits_high)) diff = np.abs(to_state - from_state) return np.sqrt(np.sum(diff ** 2, axis=-1)) diff --git a/robot/grouping.py b/robot/grouping.py index ea91713..247214d 100644 --- a/robot/grouping.py +++ b/robot/grouping.py @@ -19,6 +19,7 @@ def __init__(self, robots, grouping_mask_fn=None, **kwargs): assert np.all([isinstance(robot, IndividualRobot) for robot in robots]) self.robots = robots self.grouping_mask_fn = grouping_mask_fn + self.collision_eps = min([robot.collision_eps for robot in self.robots]) limits_low, limits_high = self._get_limits() super(RobotGroup, self).__init__(limits_low=limits_low, limits_high=limits_high, **kwargs) diff --git a/utils/utils.py b/utils/utils.py index fd50deb..75e3caa 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -1,7 +1,7 @@ import numpy as np from PIL import Image -def save_gif(imgs, gif_name): +def save_gif(imgs, gif_name, duration=50): # Setup the 4 dimensional array a_frames = [] for img in imgs: @@ -9,4 +9,4 @@ def save_gif(imgs, gif_name): a = np.stack(a_frames) ims = [Image.fromarray(a_frame) for a_frame in a] - ims[0].save(gif_name, save_all=True, append_images=ims[1:], loop=0, duration=50) \ No newline at end of file + ims[0].save(gif_name, save_all=True, append_images=ims[1:], loop=0, duration=duration) \ No newline at end of file