Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization
This work addresses the challenge of reliable obstacle avoidance for UAVs in dynamic settings, representing an incremental improvement over methods limited to static environments.
The paper tackles the problem of UAV navigation in dynamic environments by proposing a gradient-based B-spline trajectory optimization algorithm that uses onboard vision to handle both static and dynamic obstacles, achieving real-time performance in simulations and physical experiments.
Navigating dynamic environments requires the robot to generate collision-free trajectories and actively avoid moving obstacles. Most previous works designed path planning algorithms based on one single map representation, such as the geometric, occupancy, or ESDF map. Although they have shown success in static environments, due to the limitation of map representation, those methods cannot reliably handle static and dynamic obstacles simultaneously. To address the problem, this paper proposes a gradient-based B-spline trajectory optimization algorithm utilizing the robot's onboard vision. The depth vision enables the robot to track and represent dynamic objects geometrically based on the voxel map. The proposed optimization first adopts the circle-based guide-point algorithm to approximate the costs and gradients for avoiding static obstacles. Then, with the vision-detected moving objects, our receding-horizon distance field is simultaneously used to prevent dynamic collisions. Finally, the iterative re-guide strategy is applied to generate the collision-free trajectory. The simulation and physical experiments prove that our method can run in real-time to navigate dynamic environments safely. Our software is available on GitHub as an open-source package.