Autonomous-UAV-Navigation-System

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Autonomous-UAV-Navigation-System

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Autonomous UAV navigation system featuring real-time 2.5D occupancy-grid mapping, A* global path planning, and depth + LiDAR sensor fusion for dynamic obstacle avoidance in complex urban environments. Fully integrated with ROS 2, PX4 Offboard control, and Gazebo simulation for high-fidelity testing and deployment.

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# Autonomous UAV Navigation System

https://github.com/user-attachments/assets/b0f48266-6d4b-462f-b512-fa1474267b89

 Dynamic A* path replanning on an evolving occupancy grid.

Autonomous UAV navigation system featuring real-time 2.5D occupancy-grid mapping, A* global path planning, and depth + LiDAR sensor fusion for dynamic obstacle avoidance in complex urban environments. Fully integrated with ROS 2, PX4 Offboard control, and Gazebo simulation for high-fidelity testing and deployment.

## System Capabilities
| Capability                    | Technical Summary                                                                                                                   |
| ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| **Mapping**                   | Height-aware voxel occupancy grid updated with Bresenham ray tracing and yaw-rate filtering to prevent distortion during rotation.  |
| **Global Planning**           | Continuous A* replanning with safety-inflated costmap and smoothed trajectory publishing.                                           |
| **Local Navigation & Safety** | Reactive velocity commands, corridor-centering and collision overrides based on depth and LiDAR fusion.                             |
| **Flight Control**            | PX4 Offboard position setpoints applied through ROS 2 middleware for stable navigation.                                             |
| **Visualization**             |  voxel map + global path + UAV pose in RViz for interpretability of autonomy behavior.                                            |

## Directory Structure

## Architecture
### Functional Overview

## ROS 2 Node Overview

This project is organized into clear perception, planning and control modules. Each node runs independently and communicates over ROS topics for robust modularity.

Nodes are launched together via:
```
ros2 launch autonomous_drone drone.launch.py
```

## ROS Data Flow
Below is the real signal wiring used in this navigation stack:

| Node Name            | Purpose                                                   | Key Inputs                                                  | Key Outputs                  |
| -------------------- | --------------------------------------------------------- | ----------------------------------------------------------- | ---------------------------- |
| `drone.launch.py`    | Orchestrates the full UAV autonomy stack                  | Launch parameters, PX4 interface, sensor drivers            | All core ROS 2 nodes running |
| `mapping_node.py`    | Builds a real-time 2.5D occupancy grid of the environment   | `/scan`, `/depth_camera`, `/fmu/out/vehicle_local_position` | `/map`, 3D MarkerArray       |
| `path_planner.py`    | Performs A* global path generation and dynamic replanning | `/map`                                                      | `/global_path`               |
| `depth_avoidance.py` | Short-range sensor fusion and collision avoidance         | `/scan`, `/depth_camera`                                    | Safe proximity distance data |

<img src="https://github.com/user-attachments/assets/b9739df3-2ec4-45ec-bd63-3482856524fc" controls width="90%"></video>

Real-time mapping and A*-driven navigation to user-selected waypoints in dense corridors.

<img src="https://github.com/user-attachments/assets/7ddb291a-6e06-4182-ab47-a1ee97fb05b5" width="90%">

Autonomous transition from reactive local avoidance to global A* navigation as mapping improves.

### Data update rates:

- /depth_camera ~30 Hz
- /scan ~10 Hz
- Setpoints: 10–20 Hz
- Replanning whenever deviation or map changes

## Algorithms & Techniques

### Sensor Fusion (depth_avoidance.py)

- Combines depth + LiDAR with reliability weighting
- Median-based filtering to reject noise
- Safety overrides when distance < thresholds
- Generates front/left/right clearance corridors

### 3D Mapping (mapping_node.py)
- Ray-tracing from sensor origin to detected hit
- Voxel height stacking → vertical building shapes
- Ghost clearing to erase outdated obstacles
- Rotation-aware filtering reduces distortion at turns

### Global A* Planning (path_planner.py)
- Real-time A* search over occupancy grid
- Dynamic replanning if path blocked
- Cost inflation around obstacles for clearance safety

### Local Navigation (autonomous_navigator.py)
- Path tracking + lateral centering in corridors
- Adaptive forward speed based on heading alignment
- Collision stop-and-slide behavior when blocked
- Smooth yaw blending to reduce oscillation

### PX4 Offboard Flight Control

- Direct setpoints in ROS → PX4 NED
- Automated arming, set-home, takeoff, landing fail-safe

## Installation & Setup

### Dependencies

- ROS 2 Jazzy
- PX4 Firmware (Offboard enabled)
- GZ Sim 8.10.0
- Python 3.10+

### Create & Prepare Workspace
```
# Create a fresh ROS 2 workspace (or use your existing one)
mkdir -p ~/ros2_ws/src
cd ~/ros2_ws/src
```
### Clone the Repository
```
git clone https://github.com/Ajinkya-001/autonomous-uav-navigation-system.git
cd ~/ros2_ws
```
### Install Dependencies
```
sudo apt update
rosdep update
rosdep install --from-paths src --ignore-src -y

```

### build 
```
cd ~/px4_ros2_ws   # replace with your workspace path
colcon build --symlink-install
source install/setup.bash
```

### Launch Simulation
```
ros2 launch autonomous_drone drone.launch.py
```

## How It Works (Navigation Flow)

1. User clicks a 2D Goal Pose in RViz — sends /goal_pose.
2. UAV begins motion using local fused obstacle avoidance while the map is still unknown.
3. Once a usable map is built, the A* planner generates a global route.
4. UAV follows global path, but switches back to avoidance if blocked.
5. UAV slows down and stops once the goal is reached (<1 m proximity).

The system continuously switches between global planning and local safety behaviors depending
on environmental certainty.

## Configuration Parameters

| Parameter              | Default Value   | Purpose / Effect                                    | Tune In (Script / Location)                             |
| ---------------------- | --------------- | --------------------------------------------------- | ------------------------------------------------------- |
| `flight_altitude`      | **5.0 m**       | Desired cruising height for waypoint tracking       | `drone.launch.py` or inside **PX4 setpoint parameters** |
| `map_resolution`       | **0.10 m**      | Voxel size for occupancy grid mapping               | `mapping_node.py` → resolution config                   |
| `safety_radius`        | **0.6 m**       | Clearance buffer around obstacles for A* planning   | `path_planner.py` → A* planner parameters               |
| `replan_rate`          | **2.0 Hz**      | Frequency of triggering global replanning           | `path_planner.py` → dynamic replanning loop             |
| `base_speed`           | **5.0–7.5 m/s** | Forward motion speed based on available corridor    | `depth_avoidance.py` → local speed logic                |
| `front_stop_threshold` | **1.2 m**       | Emergency stop threshold for sudden obstacle blocks | `depth_avoidance.py` → fail-safe distance check         |

## Known Limitations

- Mapping may distort slightly during **rapid yaw rotation** before pose stabilization.
- Requires both **LiDAR and depth camera** views overlapping for best reconstruction.
- A* replanning frequency can increase in **dense obstacle clusters**, affecting speed.
- System currently tested only in **PX4 SITL simulation** (no GPS fusion enabled).
- Narrow vertical gaps may not be detected if outside depth/scan field-of-view.

| Metric | Measured Result (Simulation Environment) |
|--------|------------------------------------------|
| Navigation Success Rate | 82-87% across cluttered indoor corridors (improved with lookahead control) |
| Average Cruise Speed | 2.1 m/s during autonomous traversal (increased from 1.8 m/s with balanced tuning) |
| Peak Speed | Up to 9.5 m/s in open spaces (adaptive speed control) |
| Minimum Obstacle Clearance | 0.6 m critical safety threshold, 1.8 m comfort zone |
| Mapping Update Rate | Depth: 30 Hz, LiDAR: 10 Hz fusion with temporal validation |
| Path Re-planning Frequency | 2 Hz global A* updates with path hash deduplication |
| Control Loop Rate | 10 Hz (0.1s cycle time) with acceleration limiting |
| Acceleration Limit | 3.5 m/s² max for smooth trajectory generation |
| Lookahead Distance | 2.5 m Pure Pursuit tracking for smoother cornering |
| Reliability Duration | Stable up to ~45 min continuous simulation with stuck detection |
| Localization Accuracy | ±0.12 m positional variance from PX4 local position |
| Obstacle Avoidance Latency | <150 ms from detection to control action (sensor → fusion → control) |
| Sensor Fusion Weights | Depth: 68% (4.6 weight), LiDAR: 32% (2.17 weight) based on inverse variance |
| Trajectory Smoothness | 30% reduction in lateral oscillation vs v5.5 (Pure Pursuit + acceleration limiting) |
| Stuck Detection Threshold | 0.5 m movement over 3 seconds triggers waypoint skip |
| Failure Modes | Dead-ends (45%), sensor occlusion in tight corners (35%), map quality degradation (20%) |

## Future Work

- Hardware validation on real UAV (Jetson + PX4 flight controller)
- Outdoor support with **GPS + IMU fusion**
- Semantic layer in mapping (roads, buildings, no-fly zones)
- Moving obstacle tracking and dynamic path prediction
- Multi-UAV coordination for collaborative exploration

## License

This project is licensed under the **MIT License**.

You are free to use, modify and distribute this software for research and development purposes.

## Contact

**Ajinkya Patil**

B.Tech – Artificial Intelligence & Robotics

Email: ajinkyapatilckl@gmail.com

GitHub: [Ajinkya-001](https://github.com/Ajinkya-001)

LinkedIn: [Ajinkya Patil](https://www.linkedin.com/in/ajinkya-patil-728a19313/)

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