436 lines
23 KiB
Python
436 lines
23 KiB
Python
"""
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YOPO ROS NODE:
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Subscribe odometry and depth messages, perform network inference, solve trajectory, and publish control commands.
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Used to replace test_yopo_ros.py and yopo_planner_node.cpp.
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If you encounter issues (such as unsmooth) with this script, try using the following instead:
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$ cd ~/YOPO/run
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$ conda activate yopo
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$ python test_yopo_ros.py --trial=1 --epoch=0 --iter=0
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$ cd ~/YOPO/flightlib/build
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$ ./yopo_planner_node
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"""
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import rospy
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import std_msgs.msg
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from nav_msgs.msg import Odometry
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from geometry_msgs.msg import PoseStamped
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from sensor_msgs.msg import PointCloud2, PointField, Image
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from sensor_msgs import point_cloud2
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from cv_bridge import CvBridge
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from threading import Lock
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import numpy as np
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import cv2
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import os
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import torch
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import argparse
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import time
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from ruamel.yaml import YAML
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from scipy.spatial.transform import Rotation as R
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from flightpolicy.control_msg import PositionCommand
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from flightpolicy.yopo.yopo_policy import YopoPolicy
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from flightpolicy.yopo.primitive_utils import LatticeParam, LatticePrimitive, Poly5Solver, Polys5Solver, calculate_yaw
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try:
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from torch2trt import TRTModule
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except ImportError:
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print("tensorrt not found.")
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class YopoNet:
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def __init__(self, config, weight):
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self.config = config
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rospy.init_node('yopo_net', anonymous=False)
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# load params
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self.height = self.config['img_height']
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self.width = self.config['img_width']
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self.goal = np.array(self.config['goal'])
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self.env = self.config['env']
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self.use_trt = self.config['use_tensorrt']
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self.verbose = self.config['verbose']
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self.visualize = self.config['visualize']
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self.Rotation_bc = R.from_euler('ZYX', [0, self.config['pitch_angle_deg'], 0], degrees=True).as_matrix()
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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cfg = YAML().load(open(os.environ["FLIGHTMARE_PATH"] + "/flightlib/configs/traj_opt.yaml", 'r'))
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self.lattice_space = LatticeParam(cfg)
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self.lattice_primitive = LatticePrimitive(self.lattice_space)
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# variables
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self.bridge = CvBridge()
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self.odom = Odometry()
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self.odom_init = False
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self.last_yaw = 0.0
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self.ctrl_dt = 0.02
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self.ctrl_time = None
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self.desire_init = False
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self.arrive = False
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self.desire_pos = None
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self.desire_vel = None
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self.desire_acc = None
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self.optimal_poly_x = None
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self.optimal_poly_y = None
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self.optimal_poly_z = None
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self.lock = Lock()
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# eval
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self.time_forward = 0.0
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self.time_process = 0.0
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self.time_prepare = 0.0
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self.time_interpolation = 0.0
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self.time_visualize = 0.0
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self.count = 0
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# Load Network
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if self.use_trt:
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self.policy = TRTModule()
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self.policy.load_state_dict(torch.load(weight))
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else:
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saved_variables = torch.load(weight, map_location=self.device)
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saved_variables["data"]["lattice_space"] = self.lattice_space
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saved_variables["data"]["lattice_primitive"] = self.lattice_primitive
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self.policy = YopoPolicy(device=self.device, **saved_variables["data"])
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self.policy.load_state_dict(saved_variables["state_dict"], strict=False)
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self.policy.to(self.device)
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self.policy.set_training_mode(False)
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torch.set_grad_enabled(False)
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self.warm_up()
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# ros publisher
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odom_topic = self.config['odom_topic']
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depth_topic = self.config['depth_topic']
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self.lattice_traj_pub = rospy.Publisher("/yopo_net/lattice_trajs_visual", PointCloud2, queue_size=1)
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self.best_traj_pub = rospy.Publisher("/yopo_net/best_traj_visual", PointCloud2, queue_size=1)
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self.all_trajs_pub = rospy.Publisher("/yopo_net/trajs_visual", PointCloud2, queue_size=1)
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self.ctrl_pub = rospy.Publisher("/so3_control/pos_cmd", PositionCommand, queue_size=1)
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# ros subscriber
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self.odom_sub = rospy.Subscriber(odom_topic, Odometry, self.callback_odometry, queue_size=1)
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self.depth_sub = rospy.Subscriber(depth_topic, Image, self.callback_depth, queue_size=1)
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self.goal_sub = rospy.Subscriber("/move_base_simple/goal", PoseStamped, self.callback_set_goal, queue_size=1)
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# ros timer
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rospy.sleep(1.0) # wait connection...
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self.timer_ctrl = rospy.Timer(rospy.Duration(self.ctrl_dt), self.control_pub)
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print("YOPO Net Node Ready!")
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rospy.spin()
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def callback_set_goal(self, data):
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self.goal = np.asarray([data.pose.position.x, data.pose.position.y, 2])
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self.arrive = False
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print(f"New Goal: ({data.pose.position.x:.1f}, {data.pose.position.y:.1f})")
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# the first frame
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def callback_odometry(self, data):
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self.odom = data
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if not self.desire_init:
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self.desire_pos = np.array((self.odom.pose.pose.position.x, self.odom.pose.pose.position.y, self.odom.pose.pose.position.z))
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self.desire_vel = np.array((self.odom.twist.twist.linear.x, self.odom.twist.twist.linear.y, self.odom.twist.twist.linear.z))
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self.desire_acc = np.array((0.0, 0.0, 0.0))
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ypr = R.from_quat([self.odom.pose.pose.orientation.x, self.odom.pose.pose.orientation.y,
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self.odom.pose.pose.orientation.z, self.odom.pose.pose.orientation.w]).as_euler('ZYX', degrees=False)
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self.last_yaw = ypr[0]
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self.odom_init = True
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pos = np.array((self.odom.pose.pose.position.x, self.odom.pose.pose.position.y, self.odom.pose.pose.position.z))
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if np.linalg.norm(pos - self.goal) < 4 and not self.arrive:
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print("Arrive!")
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self.arrive = True
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def process_odom(self):
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# Rwb -> Rwc -> Rcw
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Rotation_wb = R.from_quat([self.odom.pose.pose.orientation.x, self.odom.pose.pose.orientation.y,
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self.odom.pose.pose.orientation.z, self.odom.pose.pose.orientation.w]).as_matrix()
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self.Rotation_wc = np.dot(Rotation_wb, self.Rotation_bc)
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Rotation_cw = self.Rotation_wc.T
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# vel and acc
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vel_w = self.desire_vel
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vel_c = np.dot(Rotation_cw, vel_w)
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acc_w = self.desire_acc
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acc_c = np.dot(Rotation_cw, acc_w)
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# pose and goal_dir
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goal_w = (self.goal - self.desire_pos) / np.linalg.norm(self.goal - self.desire_pos)
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goal_c = np.dot(Rotation_cw, goal_w)
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vel_acc = np.concatenate((vel_c, acc_c), axis=0)
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vel_acc_norm = self.normalize_obs(vel_acc[np.newaxis, :])
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obs_norm = np.hstack((vel_acc_norm, goal_c[np.newaxis, :]))
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return obs_norm
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def callback_depth(self, data):
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if not self.odom_init:
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return
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# 1. Depth Image Process
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min_dis, max_dis = 0.03, 20.0
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scale = {'435': 0.001, 'flightmare': 1.0}.get(self.env, 1.0)
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try:
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depth = self.bridge.imgmsg_to_cv2(data, "32FC1")
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except:
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print("CV_bridge ERROR: Possible solutions may be found at https://github.com/TJU-Aerial-Robotics/YOPO/issues/2")
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time0 = time.time()
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if depth.shape[0] != self.height or depth.shape[1] != self.width:
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depth = cv2.resize(depth, (self.width, self.height), interpolation=cv2.INTER_NEAREST)
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depth = np.minimum(depth * scale, max_dis) / max_dis
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# interpolated the nan value (experiment shows that treating nan directly as 0 produces similar results)
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nan_mask = np.isnan(depth) | (depth < min_dis)
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interpolated_image = cv2.inpaint(np.uint8(depth * 255), np.uint8(nan_mask), 1, cv2.INPAINT_NS)
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interpolated_image = interpolated_image.astype(np.float32) / 255.0
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depth = interpolated_image.reshape([1, 1, self.height, self.width])
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# cv2.imshow("1", depth[0][0])
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# cv2.waitKey(1)
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# 2. YOPO Network Inference
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# input prepare
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time1 = time.time()
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depth_input = torch.from_numpy(depth).to(self.device, non_blocking=True) # (non_blocking: copying speed 3x)
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obs = self.process_odom()
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obs_input = self.prepare_input_observation(obs)
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obs_input = obs_input.to(self.device, non_blocking=True)
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# torch.cuda.synchronize()
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time2 = time.time()
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# Forward (TensorRT: inference speed increased by 5x)
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with torch.no_grad():
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network_output = self.policy(depth_input, obs_input)
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network_output = network_output.cpu().numpy() # torch.cuda.synchronize() is not needed here
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time3 = time.time()
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# Replacing PyTorch operation on CUDA with NumPy operation on CPU (speed increased by 10x)
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endstate_pred, score_pred = self.process_output(network_output, return_all_preds=self.visualize)
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# Vectorization: transform the prediction(P V A in body frame) to the world frame with the attitude (without the position)
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endstate_c = endstate_pred.T.reshape(-1, 3, 3)
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endstate_w = np.matmul(self.Rotation_wc, endstate_c)
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# endstate_w = endstate_w.reshape(-1, 9).T
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action_id = np.argmin(score_pred) if self.visualize else 0
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with self.lock: # Python3.8: threads are scheduled using time slices, add the lock to ensure safety
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self.optimal_poly_x = Poly5Solver(self.desire_pos[0], self.desire_vel[0], self.desire_acc[0],
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endstate_w[action_id, 0, 0] + self.desire_pos[0], endstate_w[action_id, 0, 1], endstate_w[action_id, 0, 2], self.lattice_space.segment_time)
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self.optimal_poly_y = Poly5Solver(self.desire_pos[1], self.desire_vel[1], self.desire_acc[1],
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endstate_w[action_id, 1, 0] + self.desire_pos[1], endstate_w[action_id, 1, 1], endstate_w[action_id, 1, 2], self.lattice_space.segment_time)
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self.optimal_poly_z = Poly5Solver(self.desire_pos[2], self.desire_vel[2], self.desire_acc[2],
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endstate_w[action_id, 2, 0] + self.desire_pos[2], endstate_w[action_id, 2, 1], endstate_w[action_id, 2, 2], self.lattice_space.segment_time)
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self.ctrl_time = 0.0
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time4 = time.time()
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self.visualize_trajectory(score_pred, endstate_w)
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time5 = time.time()
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if self.verbose:
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self.time_interpolation = self.time_interpolation + (time1 - time0)
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self.time_prepare = self.time_prepare + (time2 - time1)
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self.time_forward = self.time_forward + (time3 - time2)
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self.time_process = self.time_process + (time4 - time3)
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self.time_visualize = self.time_visualize + (time5 - time4)
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self.count = self.count + 1
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print(f"Time Consuming:"
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f"depth-interpolation: {1000 * self.time_interpolation / self.count:.2f}ms;"
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f"data-prepare: {1000 * self.time_prepare / self.count:.2f}ms; "
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f"network-inference: {1000 * self.time_forward / self.count:.2f}ms; "
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f"post-process: {1000 * self.time_process / self.count:.2f}ms;"
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f"visualize-trajectory: {1000 * self.time_visualize / self.count:.2f}ms")
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def control_pub(self, _timer):
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if self.ctrl_time is None or self.ctrl_time > self.lattice_space.segment_time:
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return
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if self.arrive:
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self.desire_init = False # ready for next rollout
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return
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with self.lock: # Python3.8: threads are scheduled using time slices, add the lock to ensure safety and publish frequency
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self.ctrl_time += self.ctrl_dt
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control_msg = PositionCommand()
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control_msg.header.stamp = rospy.Time.now()
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control_msg.trajectory_flag = control_msg.TRAJECTORY_STATUS_READY
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control_msg.position.x = self.optimal_poly_x.get_position(self.ctrl_time)
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control_msg.position.y = self.optimal_poly_y.get_position(self.ctrl_time)
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control_msg.position.z = self.optimal_poly_z.get_position(self.ctrl_time)
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control_msg.velocity.x = self.optimal_poly_x.get_velocity(self.ctrl_time)
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control_msg.velocity.y = self.optimal_poly_y.get_velocity(self.ctrl_time)
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control_msg.velocity.z = self.optimal_poly_z.get_velocity(self.ctrl_time)
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control_msg.acceleration.x = self.optimal_poly_x.get_acceleration(self.ctrl_time)
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control_msg.acceleration.y = self.optimal_poly_y.get_acceleration(self.ctrl_time)
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control_msg.acceleration.z = self.optimal_poly_z.get_acceleration(self.ctrl_time)
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self.desire_pos = np.array([control_msg.position.x, control_msg.position.y, control_msg.position.z])
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self.desire_vel = np.array([control_msg.velocity.x, control_msg.velocity.y, control_msg.velocity.z])
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self.desire_acc = np.array([control_msg.acceleration.x, control_msg.acceleration.y, control_msg.acceleration.z])
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goal_dir = self.goal - self.desire_pos
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yaw, yaw_dot = calculate_yaw(self.desire_vel, goal_dir, self.last_yaw, self.ctrl_dt)
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self.last_yaw = yaw
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control_msg.yaw = yaw
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control_msg.yaw_dot = yaw_dot
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self.desire_init = True
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self.ctrl_pub.publish(control_msg)
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def process_output(self, network_output, return_all_preds=False):
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if network_output.shape[0] != 1:
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raise ValueError("batch of output values must be 1 in test!")
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network_output = network_output.reshape(10, self.lattice_space.horizon_num * self.lattice_space.vertical_num)
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endstate_pred = network_output[0:9, :]
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score_pred = network_output[9, :]
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if not return_all_preds:
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action_id = np.argmin(score_pred)
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lattice_id = self.lattice_space.horizon_num * self.lattice_space.vertical_num - 1 - action_id
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endstate_prediction = self.pred_to_endstate(endstate_pred[:, action_id], lattice_id)
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endstate_prediction = endstate_prediction[:, np.newaxis]
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score_prediction = score_pred[action_id]
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else:
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endstate_prediction = np.zeros_like(endstate_pred)
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score_prediction = score_pred
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for i in range(self.lattice_space.horizon_num * self.lattice_space.vertical_num):
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lattice_id = self.lattice_space.horizon_num * self.lattice_space.vertical_num - 1 - i
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endstate_prediction[:, i] = self.pred_to_endstate(endstate_pred[:, i], lattice_id)
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return endstate_prediction, score_prediction
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def prepare_input_observation(self, obs):
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"""
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convert the observation from body frame to primitive frame,
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and then concatenate it with the depth features (to ensure the translational invariance)
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"""
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if obs.shape[0] != 1:
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raise ValueError("batch of input observations must be 1 in test!")
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obs_return = np.ones((obs.shape[0], obs.shape[1], self.lattice_space.vertical_num, self.lattice_space.horizon_num), dtype=np.float32)
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id = 0
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obs_reshaped = obs.reshape(3, 3)
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for i in range(self.lattice_space.vertical_num - 1, -1, -1):
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for j in range(self.lattice_space.horizon_num - 1, -1, -1):
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Rbp = self.lattice_primitive.getRotation(id)
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obs_return_reshaped = np.dot(obs_reshaped, Rbp)
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obs_return[:, :, i, j] = obs_return_reshaped.reshape(9)
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id = id + 1
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return torch.from_numpy(obs_return)
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def pred_to_endstate(self, endstate_pred: np.ndarray, id: int):
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"""
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Transform the predicted state to the body frame.
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"""
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delta_yaw = endstate_pred[0] * self.lattice_primitive.yaw_diff
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delta_pitch = endstate_pred[1] * self.lattice_primitive.pitch_diff
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radio = endstate_pred[2] * self.lattice_space.radio_range + self.lattice_space.radio_range
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yaw, pitch = self.lattice_primitive.getAngleLattice(id)
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endstate_x = np.cos(pitch + delta_pitch) * np.cos(yaw + delta_yaw) * radio
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endstate_y = np.cos(pitch + delta_pitch) * np.sin(yaw + delta_yaw) * radio
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endstate_z = np.sin(pitch + delta_pitch) * radio
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endstate_p = np.array((endstate_x, endstate_y, endstate_z))
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endstate_vp = endstate_pred[3:6] * self.lattice_space.vel_max
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endstate_ap = endstate_pred[6:9] * self.lattice_space.acc_max
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Rpb = self.lattice_primitive.getRotation(id).T
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endstate_vb = np.matmul(endstate_vp, Rpb)
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endstate_ab = np.matmul(endstate_ap, Rpb)
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endstate = np.concatenate((endstate_p, endstate_vb, endstate_ab))
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endstate[[0, 1, 2, 3, 4, 5, 6, 7, 8]] = endstate[[0, 3, 6, 1, 4, 7, 2, 5, 8]]
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return endstate
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def normalize_obs(self, vel_acc):
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vel_norm = vel_acc[:, 0:3] / self.lattice_space.vel_max
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acc_norm = vel_acc[:, 3:6] / self.lattice_space.acc_max
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return np.hstack((vel_norm, acc_norm))
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def visualize_trajectory(self, pred_score, pred_endstate):
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dt = self.lattice_space.segment_time / 20.0
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# best predicted trajectory
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if self.best_traj_pub.get_num_connections() > 0:
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t_values = np.arange(0, self.lattice_space.segment_time, dt)
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points_array = np.stack((
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self.optimal_poly_x.get_position(t_values),
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self.optimal_poly_y.get_position(t_values),
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self.optimal_poly_z.get_position(t_values)
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), axis=-1)
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header = std_msgs.msg.Header()
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header.stamp = rospy.Time.now()
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header.frame_id = 'world'
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point_cloud_msg = point_cloud2.create_cloud_xyz32(header, points_array)
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self.best_traj_pub.publish(point_cloud_msg)
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# lattice primitive
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if self.visualize and self.lattice_traj_pub.get_num_connections() > 0:
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lattice_endstate = self.lattice_primitive.lattice_pos_node
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lattice_endstate = np.dot(lattice_endstate, self.Rotation_wc.T)
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zero_state = np.zeros_like(lattice_endstate)
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lattice_poly_x = Polys5Solver(self.desire_pos[0], self.desire_vel[0], self.desire_acc[0],
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lattice_endstate[:, 0] + self.desire_pos[0], zero_state[:, 0], zero_state[:, 0], self.lattice_space.segment_time)
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lattice_poly_y = Polys5Solver(self.desire_pos[1], self.desire_vel[1], self.desire_acc[1],
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lattice_endstate[:, 1] + self.desire_pos[1], zero_state[:, 1], zero_state[:, 1], self.lattice_space.segment_time)
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lattice_poly_z = Polys5Solver(self.desire_pos[2], self.desire_vel[2], self.desire_acc[2],
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lattice_endstate[:, 2] + self.desire_pos[2], zero_state[:, 2], zero_state[:, 2], self.lattice_space.segment_time)
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t_values = np.arange(0, self.lattice_space.segment_time, dt)
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points_array = np.stack((
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lattice_poly_x.get_position(t_values),
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lattice_poly_y.get_position(t_values),
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lattice_poly_z.get_position(t_values)
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), axis=-1)
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header = std_msgs.msg.Header()
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header.stamp = rospy.Time.now()
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header.frame_id = 'world'
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point_cloud_msg = point_cloud2.create_cloud_xyz32(header, points_array)
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self.lattice_traj_pub.publish(point_cloud_msg)
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|
# all predicted trajectories
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|
if self.visualize and self.all_trajs_pub.get_num_connections() > 0:
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all_poly_x = Polys5Solver(self.desire_pos[0], self.desire_vel[0], self.desire_acc[0],
|
|
pred_endstate[:, 0, 0] + self.desire_pos[0], pred_endstate[:, 0, 1], pred_endstate[:, 0, 2], self.lattice_space.segment_time)
|
|
all_poly_y = Polys5Solver(self.desire_pos[1], self.desire_vel[1], self.desire_acc[1],
|
|
pred_endstate[:, 1, 0] + self.desire_pos[1], pred_endstate[:, 1, 1], pred_endstate[:, 1, 2], self.lattice_space.segment_time)
|
|
all_poly_z = Polys5Solver(self.desire_pos[2], self.desire_vel[2], self.desire_acc[2],
|
|
pred_endstate[:, 2, 0] + self.desire_pos[2], pred_endstate[:, 2, 1], pred_endstate[:, 2, 2], self.lattice_space.segment_time)
|
|
t_values = np.arange(0, self.lattice_space.segment_time, dt)
|
|
points_array = np.stack((
|
|
all_poly_x.get_position(t_values),
|
|
all_poly_y.get_position(t_values),
|
|
all_poly_z.get_position(t_values)
|
|
), axis=-1)
|
|
scores = np.repeat(pred_score, t_values.size)
|
|
points_array = np.column_stack((points_array, scores))
|
|
header = std_msgs.msg.Header()
|
|
header.stamp = rospy.Time.now()
|
|
header.frame_id = 'world'
|
|
fields = [PointField('x', 0, PointField.FLOAT32, 1), PointField('y', 4, PointField.FLOAT32, 1),
|
|
PointField('z', 8, PointField.FLOAT32, 1), PointField('intensity', 12, PointField.FLOAT32, 1)]
|
|
point_cloud_msg = point_cloud2.create_cloud(header, fields, points_array)
|
|
self.all_trajs_pub.publish(point_cloud_msg)
|
|
|
|
def warm_up(self):
|
|
depth = np.zeros(shape=[1, 1, self.height, self.width], dtype=np.float32)
|
|
obs = np.zeros(shape=[1, 9], dtype=np.float32)
|
|
obs_input = self.prepare_input_observation(obs)
|
|
network_output = self.policy(torch.from_numpy(depth).to(self.device), obs_input.to(self.device))
|
|
self.process_output(network_output.cpu().numpy(), return_all_preds=True)
|
|
|
|
|
|
def parser():
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--use_tensorrt", type=int, default=0, help="use tensorrt or not")
|
|
parser.add_argument("--trial", type=int, default=1, help="trial number")
|
|
parser.add_argument("--epoch", type=int, default=0, help="epoch number")
|
|
parser.add_argument("--iter", type=int, default=0, help="iter number")
|
|
parser.add_argument("--trt_file", type=str, default='yopo_trt.pth', help="tensorrt filename")
|
|
return parser
|
|
|
|
|
|
# In realworld flight: visualize=False; use_tensorrt=True, and ensure the pitch_angle consistent with your platform
|
|
# When modifying the pitch_angle, there's no need to re-collect and re-train, as all predictions are in the camera coordinate system
|
|
# Change the flight speed at traj_opt.yaml and there's no need to re-collect and re-train
|
|
def main():
|
|
args = parser().parse_args()
|
|
rsg_root = os.path.dirname(os.path.abspath(__file__))
|
|
weight = args.trt_file if args.use_tensorrt else f"{rsg_root}/saved/YOPO_{args.trial}/Policy/epoch{args.epoch}_iter{args.iter}.pth"
|
|
print("load weight from:", weight)
|
|
|
|
settings = {'use_tensorrt': args.use_tensorrt,
|
|
'img_height': 96,
|
|
'img_width': 160,
|
|
'goal': [20, 20, 2], # the goal
|
|
'env': 'flightmare', # use Realsense D435 or Flightmare Simulator ('435' or 'flightmare')
|
|
'pitch_angle_deg': -5, # pitch of camera, ensure consistent with the simulator or your platform (no need to re-collect and re-train when modifying)
|
|
'odom_topic': '/juliett/ground_truth/odom',
|
|
'depth_topic': '/depth_image',
|
|
'verbose': False, # print the latency?
|
|
'visualize': True # visualize all predictions? set False in real flight
|
|
}
|
|
YopoNet(settings, weight)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|