diff --git a/examples/quadcopter_obstacle_avoidance/Dockerfile b/examples/quadcopter_obstacle_avoidance/Dockerfile new file mode 100644 index 0000000..5f8e05e --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/Dockerfile @@ -0,0 +1,22 @@ +FROM python:3.10-slim + +ENV PYTHONDONTWRITEBYTECODE=1 +ENV PYTHONUNBUFFERED=1 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + libgl1-mesa-glx \ + libgl1-mesa-dri \ + libosmesa6 \ + libglfw3 \ + libglew2.2 \ + x11-apps \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . +RUN chmod +x start.sh + +CMD ["./start.sh"] \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/README.md b/examples/quadcopter_obstacle_avoidance/README.md new file mode 100644 index 0000000..75df4a7 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/README.md @@ -0,0 +1,51 @@ + + +### `README.md` + +# Autonomous Quadcopter Obstacle Avoidance (Ticket 31-55) + +**Team Members:** +* [Mohammed Almaswary (517097)] +* [Teammate Name] + +## Project Description +This project implements a distributed autonomous navigation system for a quadcopter in a 3D simulated environment. The system decomposes complex robotics tasks into three specialized nodes using the **ZeroMQ (ZMQ)** messaging protocol for inter-process communication. + +### Architecture +1. **Simulator Node (`simulator_node.py`):** Acts as the ZeroMQ Server. Manages the MuJoCo physics engine, rendering, and sensor telemetry. +2. **Controller Node (`controller_node.py`):** The "Brain" of the system. Performs global RRT path planning, spline smoothing, and local path tracking via Pure Pursuit. It executes cascaded MPC (Altitude) and PD (Lateral) control loops to generate motor commands. +3. **Plotter Node (`plotter_node.py`):** A dedicated telemetry dashboard that listens to the network, visualizes the RRT search tree in real-time, and generates performance analytics upon simulation completion. + +## Prerequisites +* Python 3.10+ +* [Docker Desktop](https://www.docker.com/products/docker-desktop/) (Recommended for grading) + +## How to Run + +### Option 1: Local Execution (Windows) +1. Ensure all dependencies are installed: + ```bash + pip install -r requirements.txt + ``` +2. Run the provided batch file to orchestrate all three nodes simultaneously: + ```bash + .\start.bat + ``` + +### Option 2: Docker Deployment (Linux / Evaluator) +This approach provides a fully isolated environment, satisfying the project's dependency management requirement. + +1. **Build the container:** + ```bash + docker build -t quadcopter-rrt-mujoco . + ``` +2. **Run with GUI forwarding:** + + *Linux/Ubuntu:* + ```bash + xhost +local:docker + docker run -it --rm --net=host --env="DISPLAY=\$DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" quadcopter-rrt-mujoco + ``` + *Mac/Windows:* Ensure an X-Server (XQuartz/VcXsrv) is running and configure the `DISPLAY` variable as `host.docker.internal:0`. + + diff --git a/examples/quadcopter_obstacle_avoidance/controller_node.py b/examples/quadcopter_obstacle_avoidance/controller_node.py new file mode 100644 index 0000000..cdcef88 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/controller_node.py @@ -0,0 +1,241 @@ +import zmq +import time +import math +import random +import numpy as np +import matplotlib.pyplot as plt +from scipy.linalg import block_diag +from scipy.interpolate import CubicSpline + +# --- Constants & Environment --- +DRONE_MASS = 1.325 +HOVER_CTRL = 3.2495625 +GRAVITY = 9.80665 +TOTAL_HOVER_THRUST = DRONE_MASS * GRAVITY +GLOBAL_TARGET_Z = 1.0 +SIM_DURATION = 20 + +PILLAR_GEOM = np.array([ + [1.5, 0.0, 0.30], [3.0, 1.5, 0.30], [3.0, -1.5, 0.30], + [5.0, 0.0, 0.35], [6.0, 2.0, 0.30], [6.0, -2.0, 0.30], +]) +SAFETY_MARGIN = 0.36 +START_POINT = (0.0, 0.0) +GOAL_POINT = (10.0, 0.0) +X_MIN, X_MAX, Y_MIN, Y_MAX = -1.0, 11.0, -4.0, 4.0 + +# --- RRT & Spline Functions (Collapsed for brevity, exactly the same as before) --- +class Node: + def __init__(self, x, y): self.x, self.y, self.parent = x, y, None + +def get_distance(n1, n2): return math.hypot(n1.x - n2.x, n1.y - n2.y) + +def is_collision_free(n1, n2): + A, B = np.array([n1.x, n1.y]), np.array([n2.x, n2.y]) + for cx, cy, r in PILLAR_GEOM: + C = np.array([cx, cy]) + v, w = B - A, C - A + c1 = np.dot(w, v) + if c1 <= 0: dist = np.linalg.norm(C - A) + else: + c2 = np.dot(v, v) + if c2 <= c1: dist = np.linalg.norm(C - B) + else: + b = c1 / c2 + dist = np.linalg.norm(C - (A + b * v)) + if dist < (r + SAFETY_MARGIN): return False + return True + +def run_rrt(start_raw, goal_raw): + print("[Controller] Running RRT...") + node_list = [Node(*start_raw)] + goal_node = Node(*goal_raw) + for _ in range(10000): + rnd = Node(goal_node.x, goal_node.y) if random.random() < 0.1 else Node(random.uniform(X_MIN, X_MAX), random.uniform(Y_MIN, Y_MAX)) + nearest = node_list[np.argmin([get_distance(n, rnd) for n in node_list])] + theta = math.atan2(rnd.y - nearest.y, rnd.x - nearest.x) + new_node = Node(nearest.x + 0.4 * math.cos(theta), nearest.y + 0.4 * math.sin(theta)) + new_node.parent = nearest + + if is_collision_free(nearest, new_node): + node_list.append(new_node) + if get_distance(new_node, goal_node) <= 0.4 and is_collision_free(new_node, goal_node): + goal_node.parent = new_node + path = [] + curr = goal_node + while curr: + path.append([curr.x, curr.y]) + curr = curr.parent + + # --- THIS IS THE FIX: Return BOTH the path and the tree --- + return np.array(path[::-1]), node_list + + raise RuntimeError("RRT Failed") + +def prune_path(path_coords): + pruned = [path_coords[0]] + curr_idx = 0 + while curr_idx < len(path_coords) - 1: + n1 = Node(*path_coords[curr_idx]) + furthest = curr_idx + 1 + for j in range(len(path_coords) - 1, curr_idx, -1): + if is_collision_free(n1, Node(*path_coords[j])): + furthest = j + break + pruned.append(path_coords[furthest]) + curr_idx = furthest + return np.array(pruned) + +# --- MPC Design --- +def design_mpc_gain(A_c, B_c, Q_diag, R_diag, N=20, dt=0.01): + A_d = np.eye(A_c.shape[0]) + A_c * dt + B_d = B_c * dt + n_x, n_u = A_c.shape[0], B_c.shape[1] + P_x, H_u = np.zeros((n_x * N, n_x)), np.zeros((n_x * N, n_u * N)) + A_pow = np.eye(n_x) + for i in range(N): + A_pow = A_pow @ A_d + P_x[n_x * i : n_x * i + n_x, :] = A_pow + for j in range(i + 1): + H_u[n_x * i : n_x * i + n_x, n_u * j : n_u * j + n_u] = np.linalg.matrix_power(A_d, i - j) @ B_d + Q_bar, R_bar = block_diag(*[np.diag(Q_diag)] * N), block_diag(*[np.diag(R_diag)] * N) + H_T_Q = H_u.T @ Q_bar + return (np.linalg.inv(H_T_Q @ H_u + R_bar) @ H_T_Q @ P_x)[0:n_u, :] + +K_mpc_z = design_mpc_gain(np.array([[0.0, 1.0], [0.0, 0.0]]), np.array([[0.0], [1.0 / DRONE_MASS]]), [350.0, 45.0], [0.02]) + +# --- Main Controller Loop --- + +def main(): + # 1. Plan Path + raw_path, rrt_tree = run_rrt(START_POINT, GOAL_POINT) + waypoints = prune_path(raw_path) + _bk_t = np.concatenate([[0.0], np.cumsum(np.linalg.norm(np.diff(waypoints, axis=0), axis=1))]) + cs_x, cs_y = CubicSpline(_bk_t, waypoints[:, 0]), CubicSpline(_bk_t, waypoints[:, 1]) + _t_fine = np.linspace(0.0, _bk_t[-1], 2000) + trajectory = np.column_stack([cs_x(_t_fine), cs_y(_t_fine)]) + cum_dist = np.concatenate([[0.0], np.cumsum(np.linalg.norm(np.diff(trajectory, axis=0), axis=1))]) + + # 2. Setup ZeroMQ Connections + print("[Controller] Connecting to Simulator (Port 5555) and Plotter (Port 5556)...") + context = zmq.Context() + + sim_socket = context.socket(zmq.REQ) + sim_socket.connect("tcp://localhost:5555") + + plot_socket = context.socket(zmq.PUSH) + plot_socket.connect("tcp://localhost:5556") + + # 3. Send initial map data to the Plotter Node + tree_lines = [] + for node in rrt_tree: + if node.parent is not None: + tree_lines.append([[node.x, node.y], [node.parent.x, node.parent.y]]) + + plot_socket.send_json({ + "type": "init", + "tree_lines": tree_lines, + "raw_path": raw_path.tolist(), + "waypoints": waypoints.tolist(), + "spline": trajectory.tolist() + }) + + _pursuit_s = 0.0 + takeoff_time = None + + # Kickstart the Simulator REQ/REP loop + sim_socket.send_json({"ctrl": [0.0, 0.0, 0.0, 0.0]}) + print("[Controller] Tracking started!") + + while True: + # Wait for state from simulator + state = sim_socket.recv_json() + + pos_x, pos_y, pos_z = state["qpos"][0:3] + + # Send telemetry to Plotter Node + plot_socket.send_json({ + "type": "telemetry", + "t": state["time"], "x": pos_x, "y": pos_y, "z": pos_z + }) + + if state["time"] > SIM_DURATION: + print("[Controller] Simulation Complete. Telling plotter to render...") + plot_socket.send_json({"type": "done"}) + break + + quat = state["qpos"][3:7] + vel_x, vel_y, vel_z = state["qvel"][0:3] + omega = state["qvel"][3:6] + + # Quat to Euler + sinr_cosp = 2 * (quat[0] * quat[1] + quat[2] * quat[3]) + cosr_cosp = 1 - 2 * (quat[1]**2 + quat[2]**2) + current_roll = math.atan2(sinr_cosp, cosr_cosp) + + sinp = math.sqrt(1 + 2 * (quat[0] * quat[2] - quat[1] * quat[3])) + cosp = math.sqrt(1 - 2 * (quat[0] * quat[2] - quat[1] * quat[3])) + current_pitch = 2 * math.atan2(sinp, cosp) - math.pi / 2 + + siny_cosp = 2 * (quat[0] * quat[3] + quat[1] * quat[2]) + cosy_cosp = 1 - 2 * (quat[2]**2 + quat[3]**2) + current_yaw = math.atan2(siny_cosp, cosy_cosp) + + # Pure Pursuit Logic + if pos_z < (GLOBAL_TARGET_Z * 0.90) and takeoff_time is None: + local_target_x, local_target_y = 0.0, 0.0 + elif takeoff_time is None: + takeoff_time = state["time"] + local_target_x, local_target_y = 0.0, 0.0 + elif (state["time"] - takeoff_time) < 2.0: + local_target_x, local_target_y = 0.0, 0.0 + else: + i_start = max(0, np.searchsorted(cum_dist, _pursuit_s) - 1) + i_end = min(len(trajectory) - 1, np.searchsorted(cum_dist, _pursuit_s + 4.0)) + best_s, best_d2 = _pursuit_s, np.inf + pos_xy = np.array([pos_x, pos_y]) + for i in range(i_start, i_end): + seg = trajectory[i + 1] - trajectory[i] + seg_l = np.dot(seg, seg) + t = 0.0 if seg_l < 1e-12 else np.clip(np.dot(pos_xy - trajectory[i], seg) / seg_l, 0.0, 1.0) + pt = trajectory[i] + t * seg + d2 = np.dot(pos_xy - pt, pos_xy - pt) + if d2 < best_d2: + best_d2, best_s = d2, cum_dist[i] + t * math.sqrt(seg_l) + _pursuit_s = max(_pursuit_s, best_s) + s_target = min(_pursuit_s + 0.8, cum_dist[-1]) + local_target_x = float(np.interp(s_target, cum_dist, trajectory[:, 0])) + local_target_y = float(np.interp(s_target, cum_dist, trajectory[:, 1])) + + # Control Math + world_error_x, world_error_y = local_target_x - pos_x, local_target_y - pos_y + body_error_x = world_error_x * math.cos(current_yaw) + world_error_y * math.sin(current_yaw) + body_error_y = -world_error_x * math.sin(current_yaw) + world_error_y * math.cos(current_yaw) + body_vel_x = vel_x * math.cos(current_yaw) + vel_y * math.sin(current_yaw) + body_vel_y = -vel_x * math.sin(current_yaw) + vel_y * math.cos(current_yaw) + + u_z_delta = -(K_mpc_z @ np.array([pos_z - GLOBAL_TARGET_Z, vel_z])).item() + thrust = (TOTAL_HOVER_THRUST + u_z_delta) / max(math.cos(current_roll) * math.cos(current_pitch), 0.75) + + a_des_x, a_des_y = 3.0 * body_error_x - 3.2 * body_vel_x, 3.0 * body_error_y - 3.2 * body_vel_y + t_pitch = np.clip(a_des_x / GRAVITY, -0.22, 0.22) + t_roll = np.clip(-a_des_y / GRAVITY, -0.22, 0.22) + + torque_pitch = 26.0 * (t_pitch - current_pitch) - 6.0 * omega[1] + torque_roll = 26.0 * (t_roll - current_roll) - 6.0 * omega[0] + torque_yaw = 18.0 * (0.0 - current_yaw) - 4.5 * omega[2] + + f_base = thrust / 4.0 + m1 = f_base - torque_roll + torque_pitch - torque_yaw + m2 = f_base + torque_roll + torque_pitch + torque_yaw + m3 = f_base + torque_roll - torque_pitch - torque_yaw + m4 = f_base - torque_roll - torque_pitch + torque_yaw + + ctrl_scale = TOTAL_HOVER_THRUST / (4.0 * HOVER_CTRL) + ctrl = np.clip(np.array([m1, m2, m3, m4]) / ctrl_scale, 0.0, 13.0).tolist() + + # Send new motor commands back to simulator + sim_socket.send_json({"ctrl": ctrl}) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/plotter_node.py b/examples/quadcopter_obstacle_avoidance/plotter_node.py new file mode 100644 index 0000000..d8f8340 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/plotter_node.py @@ -0,0 +1,100 @@ +import zmq +import numpy as np +import matplotlib.pyplot as plt +from pathlib import Path + +# Environment Constants for Plotting +GLOBAL_TARGET_Z = 1.0 +PILLAR_GEOM = np.array([ + [1.5, 0.0, 0.20], [3.0, 1.5, 0.20], [3.0, -1.5, 0.20], + [5.0, 0.0, 0.25], [6.0, 2.0, 0.20], [6.0, -2.0, 0.20], +]) +SAFETY_MARGIN = 0.36 +START_POINT = (0.0, 0.0) +GOAL_POINT = (10.0, 0.0) +X_MIN, X_MAX, Y_MIN, Y_MAX = -1.0, 11.0, -4.0, 4.0 + +def main(): + print("[Plotter] Starting Telemetry Listener on Port 5556...") + context = zmq.Context() + socket = context.socket(zmq.PULL) + socket.bind("tcp://*:5556") + + tree_lines, raw_path, waypoints, spline = [], [], [], [] + t_hist, x_hist, y_hist, z_hist = [], [], [], [] + + print("[Plotter] Waiting for Controller data...") + + while True: + msg = socket.recv_json() + + # 1. Receive the planned paths at startup + if msg["type"] == "init": + print("[Plotter] Received map and trajectory data.") + tree_lines = msg["tree_lines"] + raw_path = np.array(msg["raw_path"]) + waypoints = np.array(msg["waypoints"]) + spline = np.array(msg["spline"]) + + # 2. Receive live drone positions during flight + elif msg["type"] == "telemetry": + t_hist.append(msg["t"]) + x_hist.append(msg["x"]) + y_hist.append(msg["y"]) + z_hist.append(msg["z"]) + + # 3. Receive the shutdown signal and draw the plot + elif msg["type"] == "done": + print("[Plotter] Simulation complete. Generating beautiful graphs...") + break + + # --- Draw the Matplotlib Graphs --- + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) + + # Left Graph: 2D Map + for line in tree_lines: + ax1.plot([line[0][0], line[1][0]], [line[0][1], line[1][1]], color="grey", alpha=0.2, linewidth=0.5) + + ax1.plot(x_hist, y_hist, label="Drone Flight Path", color="dodgerblue", linewidth=2.5) + ax1.plot(spline[:, 0], spline[:, 1], color="black", linestyle="--", linewidth=1.5, label="Spline Smooth Path") + ax1.plot(raw_path[:, 0], raw_path[:, 1], color="orange", linestyle=":", linewidth=1.5, label="Raw RRT Path", alpha=0.6) + ax1.plot(waypoints[:, 0], waypoints[:, 1], "g^--", markersize=6, linewidth=1.0, label="Pruned Backbone", alpha=0.8) + + ax1.scatter(START_POINT[0], START_POINT[1], color="green", marker="o", s=100, label="Start", zorder=5) + ax1.scatter(GOAL_POINT[0], GOAL_POINT[1], color="orange", marker="*", s=150, label="Goal", zorder=5) + + for i, pillar in enumerate(PILLAR_GEOM): + cx, cy, r = pillar + ax1.add_patch(plt.Circle((cx, cy), r, color="lightcoral", alpha=0.85, label="Pillar" if i == 0 else "")) + ax1.add_patch(plt.Circle((cx, cy), r + SAFETY_MARGIN, color="lightcoral", fill=False, linestyle=":", linewidth=1.2, alpha=0.5, label="Safety margin" if i == 0 else "")) + ax1.text(cx, cy, str(i + 1), ha="center", va="center", fontsize=8, fontweight="bold", color="white") + + ax1.set_title("Top-Down Flight Path (RRT & ZeroMQ)") + ax1.set_xlabel("X (m)") + ax1.set_ylabel("Y (m)") + ax1.set_xlim(X_MIN, X_MAX) + ax1.set_ylim(Y_MIN, Y_MAX) + ax1.set_aspect("equal") + ax1.grid(True, linestyle="--", alpha=0.5) + ax1.legend(loc="upper left", fontsize=7.5) + + # Right Graph: Altitude + ax2.plot(t_hist, z_hist, label="Drone Altitude", color="purple") + ax2.axhline(y=GLOBAL_TARGET_Z, color="darkred", linestyle="--", label="Target Z") + ax2.set_title("Vertical Tracking Profile") + ax2.set_xlabel("Time (s)") + ax2.set_ylabel("Altitude (m)") + ax2.set_ylim(0, 1.5) + ax2.grid(True, linestyle="--") + ax2.legend() + + plt.tight_layout() + + out_path = Path(__file__).parent / "rrt_zmq_distributed_results.png" + plt.savefig(out_path, dpi=150, bbox_inches="tight", facecolor=fig.get_facecolor()) + print(f"[Plotter] Results image saved to -> {out_path}") + + plt.show() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/requirements.txt b/examples/quadcopter_obstacle_avoidance/requirements.txt new file mode 100644 index 0000000..3753269 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/requirements.txt @@ -0,0 +1,5 @@ +mujoco==3.1.6 +numpy +scipy +matplotlib +pyzmq \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/rrt_zmq_distributed_results.png b/examples/quadcopter_obstacle_avoidance/rrt_zmq_distributed_results.png new file mode 100644 index 0000000..b48e840 Binary files /dev/null and b/examples/quadcopter_obstacle_avoidance/rrt_zmq_distributed_results.png differ diff --git a/examples/quadcopter_obstacle_avoidance/simulator_node.py b/examples/quadcopter_obstacle_avoidance/simulator_node.py new file mode 100644 index 0000000..79b93b4 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/simulator_node.py @@ -0,0 +1,53 @@ +import time +import zmq +import mujoco +import mujoco.viewer + +MODEL_PATH = "skydio_x2/scene.xml" + +def main(): + print("[Simulator] Setting up ZeroMQ Server...") + context = zmq.Context() + socket = context.socket(zmq.REP) + socket.bind("tcp://*:5555") + + print("[Simulator] Loading MuJoCo Model...") + model = mujoco.MjModel.from_xml_path(MODEL_PATH) + data = mujoco.MjData(model) + model.opt.timestep = 0.01 + + # Start drone slightly in the air + data.qpos[0:3] = [0.0, 0.0, 0.1] + mujoco.mj_forward(model, data) + + print("[Simulator] Launching Viewer...") + with mujoco.viewer.launch_passive(model, data) as viewer: + while viewer.is_running(): + step_start = time.time() + + # 1. Wait for Controller to send motor commands + msg = socket.recv_json() + if "ctrl" in msg: + data.ctrl[0:4] = msg["ctrl"] + + # 2. Step Physics + mujoco.mj_step(model, data) + + # 3. Extract Sensor State + state = { + "time": data.time, + "qpos": data.qpos.tolist(), + "qvel": data.qvel.tolist() + } + + # 4. Reply with new state + socket.send_json(state) + + # Keep GUI responsive + viewer.sync() + time_until_next = model.opt.timestep - (time.time() - step_start) + if time_until_next > 0: + time.sleep(time_until_next) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/CHANGELOG.md b/examples/quadcopter_obstacle_avoidance/skydio_x2/CHANGELOG.md new file mode 100644 index 0000000..5484d62 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/CHANGELOG.md @@ -0,0 +1,6 @@ +# Changelog – Skydio X2 Description + +All notable changes to this model will be documented in this file. + +## [2023-09-11] +- Initial release. diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/LICENSE b/examples/quadcopter_obstacle_avoidance/skydio_x2/LICENSE new file mode 100644 index 0000000..34e4082 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2022 Shadow Robot Company Ltd + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/README.md b/examples/quadcopter_obstacle_avoidance/skydio_x2/README.md new file mode 100644 index 0000000..c1eaa92 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/README.md @@ -0,0 +1,20 @@ +# Skydio X2 Description (MJCF) + +> [!IMPORTANT] +> Requires MuJoCo 2.2.2 or later. + +## Changelog + +See [CHANGELOG.md](./CHANGELOG.md) for a full history of changes. + +## Overview + +This package contains a simplified robot description (MJCF) of the [Skydio X2](https://www.skydio.com/skydio-x2) drone developed by [Skydio](https://www.skydio.com/). The assets were provided directly by Skydio under the [Apache 2.0 License](LICENSE). + +

+ +

+ +## License + +This model is released under an [Apache-2.0 License](LICENSE). diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly.obj b/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly.obj new file mode 100644 index 0000000..96ab26c --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly.obj @@ -0,0 +1,15465 @@ +mtllib phong3SG.mtl +usemtl phong3SG +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837400 3.19430100 +v 15.46903800 7.63837300 -1.50274400 +v 15.46903800 7.63837300 -1.50274400 +v 15.46903800 7.63837300 -1.50274400 +v 15.46903800 7.63837300 -1.50274400 +v 15.46903800 7.63837300 -1.50274400 +v 15.46903800 7.63837300 -1.50274400 +v 14.57681200 7.63837300 -1.50274400 +v 14.57681200 7.63837300 -1.50274400 +v 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a/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly_texture_SpinningProps_1024.png b/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly_texture_SpinningProps_1024.png new file mode 100644 index 0000000..95933a4 Binary files /dev/null and b/examples/quadcopter_obstacle_avoidance/skydio_x2/assets/X2_lowpoly_texture_SpinningProps_1024.png differ diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/scene.xml b/examples/quadcopter_obstacle_avoidance/skydio_x2/scene.xml new file mode 100644 index 0000000..b32de3b --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/scene.xml @@ -0,0 +1,23 @@ + + + + + + + + + + + + + + + + + + + + + + diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.png b/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.png new file mode 100644 index 0000000..c629bbe Binary files /dev/null and b/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.png differ diff --git a/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.xml b/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.xml new file mode 100644 index 0000000..8ea1d33 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/skydio_x2/x2.xml @@ -0,0 +1,114 @@ + + + + diff --git a/examples/quadcopter_obstacle_avoidance/start.bat b/examples/quadcopter_obstacle_avoidance/start.bat new file mode 100644 index 0000000..e7e3571 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/start.bat @@ -0,0 +1,13 @@ +@echo off +echo [1/3] Starting MuJoCo Simulator Node (Port 5555)... +start "Simulator" python simulator_node.py + +echo Waiting 2 seconds for Simulator to bind... +timeout /t 2 /nobreak >nul + +echo [2/3] Starting Controller Node (RRT + MPC)... +start "Controller" python controller_node.py + +:: Start the plotter immediately after so it doesn't miss the RRT map data! +echo [3/3] Starting Plotter Dashboard Node (Port 5556)... +start "Plotter" python plotter_node.py \ No newline at end of file diff --git a/examples/quadcopter_obstacle_avoidance/start.sh b/examples/quadcopter_obstacle_avoidance/start.sh new file mode 100644 index 0000000..10ea0c8 --- /dev/null +++ b/examples/quadcopter_obstacle_avoidance/start.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +echo "[1/3] Starting MuJoCo Simulator Node (Port 5555)..." +python simulator_node.py & + +# Give the simulator a moment to bind to its port +sleep 2 + +echo "[2/3] Starting Controller Node (RRT + MPC)..." +python controller_node.py & + +# Start the plotter immediately after to catch the Controller's first broadcast +echo "[3/3] Starting Plotter Dashboard Node (Port 5556)..." +python plotter_node.py + +# Keep the Docker container alive while the processes run +wait \ No newline at end of file