diff --git a/config/trajectory/trajectory_files/trajectory.txt b/config/trajectory/trajectory_files/trajectory.txt new file mode 100644 index 0000000..c45fd15 --- /dev/null +++ b/config/trajectory/trajectory_files/trajectory.txt @@ -0,0 +1,25 @@ +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 +-3,0,3,-1.27409 diff --git a/config/trajectory/trajectory_files/trajectory_rx.txt b/config/trajectory/trajectory_files/trajectory_rx.txt new file mode 100644 index 0000000..9c22021 --- /dev/null +++ b/config/trajectory/trajectory_files/trajectory_rx.txt @@ -0,0 +1,25 @@ +-55,10,3,0.3 +-55,10,3,0.3 +-55,10,3,0.3 +-55,10,3,0.3 +-55,10,3,0.3 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b/scripts/config/custom_config.yaml @@ -6,11 +6,10 @@ mrs_uav_managers: state_estimators: [ "gps_garmin", "gps_baro", - "ground_truth", "rtk", ] - initial_state_estimator: "gps_baro" # will be used as the first state estimator + initial_state_estimator: "rtk" # will be used as the first state estimator agl_height_estimator: "garmin_agl" # only slightly filtered height for checking min height (not used in control feedback) uav_manager: @@ -38,8 +37,8 @@ mrs_uav_trackers: # mrs collision avoidance collision_avoidance: - enabled: false # disabling this will stop this UAV to react to others, but it will still transmit data to others - enabled_passively: false # disabling this will stop this uav to even transmit its data to others (only if enabled: false) - radius: 5.0 # [m] - correction: 3.0 # [m] + enabled: true # disabling this will stop this UAV to react to others, but it will still transmit data to others + enabled_passively: true # disabling this will stop this uav to even transmit its data to others (only if enabled: false) + radius: 2.5 # [m] + correction: 0.5 # [m] diff --git a/scripts/rx.sh b/scripts/rx.sh index cfc81b3..e5e6cfb 100755 --- a/scripts/rx.sh +++ b/scripts/rx.sh @@ -53,7 +53,7 @@ input=( ' 'uvdar_filter' 'waitForRos; roslaunch uvdar_core uvdar_kalman.launch output_frame:='"$UAV_NAME"'/stable_origin ' - 'uav_waypoint_flier' 'export '"$UAV_NAME"'; waitForControl; roslaunch example_waypoint_flier example_waypoint_flier.launch; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/fly_to_first_waypoint; export '"$UAV_NAME"'; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/start_waypoints_following;export '"$UAV_NAME"'; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/stop_waypoints_following; + 'Trajectory' 'history -s rosservice call /'"$UAV_NAME"'/control_manager/stop_trajectory_tracking; rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="trajectory_rx.txt"; ' 'record' 'waitForRos; history -s rosbag record -O ~/rosbags/marlon/exp1/test.bag /'"$UAV_NAME"'/uvdar/adaptive_logging_back /'"$UAV_NAME"'/uvdar/adaptive_logging_left /uav1/uvdar/adaptive_logging_right /'"$UAV_NAME"'/uvdar/blinkers_seen_back /'"$UAV_NAME"'/uvdar/blinkers_seen_left /'"$UAV_NAME"'/uvdar/blinkers_seen_right /'"$UAV_NAME"'/control_manager/control_reference --duration=30s; ' diff --git a/scripts/two_drones/session_marlon.yml b/scripts/two_drones/session_marlon.yml index 2b3012a..53e504f 100644 --- a/scripts/two_drones/session_marlon.yml +++ b/scripts/two_drones/session_marlon.yml @@ -79,12 +79,13 @@ windows: - 'export UAV_NAME=uav2; history -s rosservice call /$UAV_NAME/example_waypoint_flier/start_waypoints_following' - 'export UAV_NAME=uav2; history -s rosservice call /$UAV_NAME/example_waypoint_flier/stop_waypoints_following' - #- trajectories: - #layout: tiled - # panes: - # - export UAV_NAME=uav1; history -s rosservice call /'"$UAV_NAME"'/control_manager/start_trajectory_tracking; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="tx1/line.txt" loop:=true - # - export UAV_NAME=uav1; waitForControl; sleep 15; ~/catkin_ws/src/uvdar_core/scripts/trajectory_generation.py; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start - # - export UAV_NAME=uav3; history -s rosservice call /'"$UAV_NAME"'/control_manager/start_trajectory_tracking; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="two_tx/tx2_fly_by.txt" loop:=true + - trajectories: + layout: tiled + panes: + - export UAV_NAME=uav1; history -s rosservice call /'"$UAV_NAME"'/control_manager/start_trajectory_tracking; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="trajectory_rx.txt" + - export UAV_NAME=uav2; history -s rosservice call /'"$UAV_NAME"'/control_manager/start_trajectory_tracking; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="trajectory_tx.txt" + + #- export UAV_NAME=uav3; history -s rosservice call /'"$UAV_NAME"'/control_manager/start_trajectory_tracking; history -s rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="two_tx/tx2_fly_by.txt" loop:=true - rviz: layout: even-vertical panes: diff --git a/scripts/tx.sh b/scripts/tx.sh index 3cc8ab7..29faf89 100755 --- a/scripts/tx.sh +++ b/scripts/tx.sh @@ -53,7 +53,7 @@ input=( ' 'load_sequence' 'waitForRos; history -s rosservice call /'"$UAV_NAME"'/uvdar_led_manager_node/select_sequences [0,1,2,3]; history -s rosservice call /'"$UAV_NAME"'/uvdar_led_manager_node/load_sequences; ' - 'uav_waypoint_flier' 'export '"$UAV_NAME"'; waitForControl; roslaunch example_waypoint_flier example_waypoint_flier.launch; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/fly_to_first_waypoint; export '"$UAV_NAME"'; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/start_waypoints_following;export '"$UAV_NAME"'; history -s rosservice call /'"$UAV_NAME"'/example_waypoint_flier/stop_waypoints_following; + 'Trajectory' 'history -s rosservice call /'"$UAV_NAME"'/control_manager/stop_trajectory_tracking; rosservice call /'"$UAV_NAME"'/control_manager/goto_trajectory_start; history -s roslaunch uvdar_core load_trajectory.launch file:="trajectory_tx.txt"; ' # do NOT modify the command list below 'EstimDiag' 'waitForHw; rostopic echo /'"$UAV_NAME"'/estimation_manager/diagnostics diff --git a/trajectory.txt b/trajectory.txt new file mode 100644 index 0000000..7e24719 --- /dev/null +++ b/trajectory.txt @@ -0,0 +1,485 @@ +-3,0,3,0.0 +-3,0,3,0.0 +-3,0,3,0.0 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+ "def generate_trajectory(start, end, T=0.2):\n", + "\n", + " x0, y0, z0, h0 = start\n", + " x1, y1, z1, h1 = end\n", + " \n", + " # Calculate the number of samples\n", + " distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2 + (z1 - z0)**2)\n", + " num_samples = int(np.ceil(distance / T)) + 1\n", + " \n", + " # Generate linearly spaced points between start and end\n", + " x = np.linspace(x0, x1, num_samples)\n", + " y = np.linspace(y0, y1, num_samples)\n", + " z = np.linspace(z0, z1, num_samples)\n", + " heading = np.linspace(h0, h1, num_samples)\n", + " \n", + " # Format the trajectory as a list of tuples\n", + " trajectory = [(xi, yi, zi, hi) for xi, yi, zi, hi in zip(x, y, z, heading)]\n", + " \n", + " return trajectory\n", + "\n", + "def save_trajectory(trajectory, filename):\n", + " with open(filename, 'w') as file:\n", + " for point in trajectory:\n", + " line = ','.join(map(str, point)) + '\\n'\n", + " file.write(line)\n", + "\n", + "start_point = (-40, -40, 3, 0.0)\n", + "end_point = (-32, -32, 3, 0.0)\n", + "trajectory = generate_trajectory(start_point, end_point, T=0.2)\n", + "filename = 'trajectory.txt'\n", + "save_trajectory(trajectory, filename)\n", + "\n", + "print(f\"Trajectory saved to {filename}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def generate_trajectory(waypoints, hover_times, T=0.2):\n", + " trajectory = []\n", + " for i in range(len(waypoints) - 1):\n", + " x0, y0, z0, h0 = waypoints[i]\n", + " x1, y1, z1, h1 = waypoints[i+1]\n", + " hover_time = hover_times[i]\n", + " \n", + " # Calculate number of samples for the hover period\n", + " num_hover_samples = int(hover_time / T)\n", + " trajectory.extend([(x0, y0, z0, h0)] * num_hover_samples)\n", + " \n", + " # Interpolate to next waypoint\n", + " distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2 + (z1 - z0)**2)\n", + " num_travel_samples = int(np.ceil(distance / T))\n", + " if num_travel_samples > 0:\n", + " x = np.linspace(x0, x1, num_travel_samples + 1)\n", + " y = np.linspace(y0, y1, num_travel_samples + 1)\n", + " z = np.linspace(z0, z1, num_travel_samples + 1)\n", + " heading = np.linspace(h0, h1, num_travel_samples + 1)\n", + " trajectory.extend([(xi, yi, zi, hi) for xi, yi, zi, hi in zip(x[1:], y[1:], z[1:], heading[1:])])\n", + " \n", + " # Add hover time at the last waypoint\n", + " x_last, y_last, z_last, h_last = waypoints[-1]\n", + " num_final_hover_samples = int(hover_times[-1] / T)\n", + " trajectory.extend([(x_last, y_last, z_last, h_last)] * num_final_hover_samples)\n", + " \n", + " return trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "#RX -1.27409\n", + "#TX -0.7853982\n", + "\n", + "#waypoints = [(0, 0, 3, -0.7853982), (3,0,3, -0.7853982)]\n", + "waypoints = [(-55, 15, 3, 0.785), (-55, 20, 3, 0.785),(-55,25,3,0.785),(-55,30,3,0.785),(-55,35,3,0.785),(-55,40,3,0.785)]\n", + "hover_times = [30,30,30,30,30,30] \n", + "trajectory = generate_trajectory(waypoints, hover_times, T=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "def plot_trajectory(trajectory):\n", + " fig = plt.figure()\n", + " ax = fig.add_subplot(111, projection='3d')\n", + " x, y, z, _ = zip(*trajectory) # Ignore headings for plotting\n", + "\n", + " ax.scatter(x[0], y[0], z[0], color='red', s=100) \n", + "\n", + " ax.plot(x, y, z, marker='o', linestyle='-')\n", + " ax.set_xlabel('X Position')\n", + " ax.set_ylabel('Y Position')\n", + " ax.set_zlabel('Z Position')\n", + " plt.title('3D Trajectory Visualization')\n", + " plt.show()\n", + "\n", + "plot_trajectory(trajectory)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trajectory saved to /home/rivermar/workspace/src/uvdar_core/config/trajectory/trajectory_files/trajectory_tx.txt.\n" + ] + } + ], + "source": [ + "import os\n", + "file_location = os.path.expanduser('~/workspace/src/uvdar_core/config/trajectory/trajectory_files/trajectory_tx.txt')\n", + "\n", + "def save_trajectory(trajectory, filename):\n", + " with open(filename, 'w') as file:\n", + " for point in trajectory:\n", + " line = ','.join(map(str, point)) + '\\n'\n", + " file.write(line)\n", + "\n", + "save_trajectory(trajectory, file_location)\n", + "\n", + "print(f\"Trajectory saved to {file_location}.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}