PREDICTIVE POTENTIAL FIELD-BASED COLLISION AVOIDANCE FOR MULTICOPTERS

Reliable obstacle avoidance is a key to navigating with UAVs in the close vicinity of static and dynamic obstacles. Wheel-based mobile robots are often equipped with 2D or 3D laser range finders that cover the 2D workspace sufficiently accurate and at a high rate. Micro UAV platforms operate in a 3D environment, but the restricted payload prohibits the use of fast state-of-the-art 3D sensors. Thus, perception of small obstacles is often only possible in the vicinity of the UAV and a fast collision avoidance system is necessary. We propose a reactive collision avoidance system based on artificial potential fields, that takes the special dynamics of UAVs into account by predicting the influence of obstacles on the estimated trajectory in the near future using a learned motion model. Experimental evaluation shows that the prediction leads to smoother trajectories and allows to navigate collision-free through passageways

TOWARDS MULTIMODAL OMNIDIRECTIONAL OBSTACLE DETECTION FOR AUTONOMOUS UNMANNED AERIAL VEHICLES

Limiting factors for increasing autonomy and complexity of truly autonomous systems (without external sensing and control) are onboard sensing and onboard processing power. In this paper, we propose a hardware setup and processing pipeline that allows a fully autonomous UAV to perceive obstacles in (almost) all directions in its surroundings. Different sensor modalities are applied in order take into account the different characteristics of obstacles that can commonly be found in typical UAV applications. We provide a complete overview on the implemented system and present experimental results as a proof of concept