Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments
This addresses safety and autonomy challenges for micro aerial vehicles operating in unpredictable settings, representing an incremental improvement in robust control methods.
The paper tackles the problem of enabling micro aerial vehicles to avoid moving obstacles in dynamic environments using on-board vision, achieving effective real-time collision avoidance with a chance-constrained model predictive controller that ensures collision probability stays below a specified threshold.
In this paper, we present an on-board vision-based approach for avoidance of moving obstacles in dynamic environments. Our approach relies on an efficient obstacle detection and tracking algorithm based on depth image pairs, which provides the estimated position, velocity and size of the obstacles. Robust collision avoidance is achieved by formulating a chance-constrained model predictive controller (CC-MPC) to ensure that the collision probability between the micro aerial vehicle (MAV) and each moving obstacle is below a specified threshold. The method takes into account MAV dynamics, state estimation and obstacle sensing uncertainties. The proposed approach is implemented on a quadrotor equipped with a stereo camera and is tested in a variety of environments, showing effective on-line collision avoidance of moving obstacles.