Minimum-Risk Path Planning for Long-Range and Low-Altitude Flights of Autonomous Unmanned Aircraft

We present the architecture, implementation and benchmark results of a minimum-risk path planning framework targeted towards long-range unmanned flights at low altitudes. Application scenarios, such as transportation, remote sensing or surveillance missions, often require long-range flights beyond the visual line-of-sight of a pilot on the ground. Autonomy is a key enabler for such operations as it decreases the dependency on command-and-control links and human interaction. An essential technology for autonomous unmanned aircraft is onboard and online path planning. It requires highly efficient planning algorithms to be integrated in onboard systems with limited computational resources. In this work, we demonstrate how semantic geospatial datasets and sampling-based planning algorithms can be used to calculate long-range and low-altitude flight paths within seconds. We present benchmark results from a set of mission scenarios of realistic complexity and extent. The results indicate that in-flight and onboard path planning for autonomous aircraft can be realized through effective pre-processing of input data and careful design, integration and tuning of planning algorithms.