In-flight Kinematic Model Parameter Estimation and Adaptive Path Planning for Unmanned Aircraft

In real-world applications, unmanned aircraft may be subject to short-term configuration changes such as new payloads, additional batteries or antennas. Furthermore, degradation of materials, system failures, icing and weather conditions may impact performance. As a consequence, performance parameters may often be unknown or subject to uncertainty. To handle such performance uncertainties within an autonomous system, the system must exhibit a high degree of self-awareness and be able to react and adapt to false assumptions and inaccurate parameter values. Within this work, an adaptive path planning approach based on in-flight estimation of kinematic model parameters is presented. In order to achieve this, the parameters of the kinematic model used for path planning are continuously monitored and the flight path is re-planned correspondingly in order to fully utilize the aircraft's turn and climbing performance. At the same time the path tracking error and required safety distance along the flight path are minimized. Simulation results for multiple scenarios indicate that the approach improves performance and safety in the presence of uncertain or changing kinematic model parameters.