Micro aerial vehicles avoid obstacles in real time using onboard lidar
LiDARFlow: Real-Time Panel-Based MAV Guidance in Unknown Environments
Robotics
Summary
Flying tiny drones indoors or in cluttered spaces is hard because they need to avoid bumping into things without knowing the area beforehand. This paper shows a new method that lets drones sense obstacles with onboard LiDAR and smoothly steer around them in real time. The method combines a math-based flow approach with live updates from distance sensors to guide the drone safely to its goal. Tests show the drone can follow directions and reach waypoints without crashes using only its own sensors. The main challenge is processing the sensor data quickly enough on the drone itself.
What this means in practice
- •For drone developers: Enable small drones to navigate indoors or cluttered areas safely using only sensors on the drone without pre-mapped environments.
- •For robotic system integrators: Incorporate a lightweight real-time obstacle avoidance method into autonomous flying robots operating in unknown environments.
Authors
João Machado, Zeynep Bilgin, Matthieu Verdoucq, Murat Bronz
Abstract
This paper presents a guidance algorithm for micro aerial vehicles operating in unknown, cluttered environments using only onboard sensing. The method is based on a panel formulation originally derived from aerodynamic potential-flow theory and generates smooth, collision-free guidance vectors from locally perceived obstacles. The approach is extended to unknown environments by constructing and updating the obstacle representation online from onboard LiDAR measurements. The resulting obstacle-avoidance field is integrated with a nominal guiding vector field to produce the final control input. The system is experimentally validated in indoor flight tests under two scenarios: waypoint navigation and directional guidance. In both cases, the vehicle successfully completes its task while avoiding all obstacles in real time using only onboard perception. The results demonstrate that the method is computationally lightweight and suitable for onboard implementation, with pointcloud processing identified as the main practical limitation. These results support the feasibility of lightweight onboard guidance in unknown environments.