AI Drone Control with Reinforcement Learning
Classic PID controller is reliable but struggles with sharp maneuvers, strong wind, or landing on moving platforms. We solve these using reinforcement learning (RL): train the drone on millions of flights in the AirSim simulator, then transfer the policy to a real drone. This saves up to 1.5 million RUB on field tests. Here's how it works in practice.
Why RL Over PID?
PID controllers work well under normal conditions, but their parameters are fixed. RL adapts to unexpected situations: motor failure, wind gusts of 10 m/s, or a suddenly shifted target. In tests, our RL policy shows 30% lower tracking error under wind gusts up to 12 m/s compared to PID. For aggressive maneuvers (flip, racing), RL outperforms PID by far—traditional controllers simply cannot perform such moves.
| Characteristic | PID/MPC | RL |
|---|---|---|
| Adaptation to disturbances | Low | High |
| Computational load | Minimal | Medium (50-100 Hz) |
| Model requirement | Yes (dynamics) | No (model-free) |
| Aggressive maneuvers | Limited | Possible (flip, racing) |
| Tuning time | Days | Weeks–months |
| Sim-to-real | Not required | Required |
How We Train the Policy in Practice
Simulation Environment
We use AirSim + AirGen for photorealistic scenes. Domain randomization: random wind (0–15 m/s), sensor noise (±2 cm for LiDAR), drone mass variations (1.2–1.8 kg). This is critical for sim-to-real transfer.
Policy Architecture
For hover/navigation—MLP with 256 neurons. For vision-based obstacle avoidance—CNN encoder (depth image) + MLP. Under partial observability (wind is not directly visible), we add an LSTM layer to memorize environment dynamics. A typical policy runs at 50 Hz with p99 latency under 10 ms on Jetson Orin.
Reward Function
Balances goal achievement, collision avoidance, and energy efficiency. Example for navigation:
def compute_reward(self, state, target_pos):
drone_pos = np.array([...]) # drone position
dist_to_goal = np.linalg.norm(drone_pos - target_pos)
reward = -dist_to_goal * 0.1
if dist_to_goal < 0.5:
reward += 100.0
collision = self.client.simGetCollisionInfo()
if collision.has_collided:
reward -= 200.0
velocity = state.kinematics_estimated.linear_velocity
speed = np.sqrt(velocity.x_val**2 + velocity.y_val**2 + velocity.z_val**2)
reward -= speed * 0.01 # small speed penalty
return reward
Sim-to-Real Transfer
Main challenge is the reality gap. We use three methods: system identification (measure real thrust curves and moments of inertia with ±5% accuracy), domain randomization (wide range of physical parameters), and residual policy learning (PID + RL correction). The latter is especially effective—RL fixes PID errors without full replacement, increasing reliability to 95% of scenarios.
| Transfer Method | Essence | Effectiveness |
|---|---|---|
| System identification | Measure real parameters | High, but labor-intensive |
| Domain randomization | Wide range in simulation | Medium, but simple |
| Residual policy learning | PID + RL correction | Very high (>95% reliability) |
What Problems Does RL Solve?
Trajectory tracking with disturbances. Wind gusts up to 12 m/s, sensor noise, motor failures—RL agent adapts where PID loses stability. Aggressive maneuvers: flips, flying at max speed 10 m/s through gates (drone racing). Classical controllers fail under aggressive maneuvers—RL policy learns directly. Landing on a moving platform: ship/car as landing platform with landing accuracy under 10 cm. Relative navigation via AprilTag or ArUco markers.
How We Work on the Project
- Analysis and specification: Define use cases, boundary conditions (wind, maneuvers, landing accuracy).
- Simulation design: Configure AirSim/Gazebo for your platform, model sensors and wind.
- Policy training: PPO or SAC, hyperparameter optimization, thousands of trials. Average training time in simulation—500 episodes (about 2 hours on RTX 4090).
- Sim-to-real transfer: Calibration, domain randomization, tests on real drone (at least 50 flights).
- GCS integration: MAVLink, QGroundControl, companion computer communication.
- Documentation and training: Architecture description, startup guide, monitoring dashboard.
- 3-month support: Assistance in operation and fine-tuning.
Our Experience and Guarantees
Our team has 5+ years in AI/ML, with projects completed for drone racing and oil rig inspections. We guarantee policy stability under specification conditions. Our engineers are certified in PX4 and ROS2. We'll assess your project and propose the optimal solution—contact us for a consultation.
Estimated Timelines
- Basic navigation (hover + movement): 10–14 weeks.
- Obstacle avoidance + aggressive maneuvers: 20–28 weeks.
- Landing on a moving platform: 16–24 weeks.
The cost is calculated individually after analyzing your scenario and hardware requirements. Get a consultation—write to us, and we will evaluate your project in 1–2 days.