Abstract
Quadcopters are susceptible to internal and external influences, many of which may lead to faults. To ensure a safe and reliable flight, the quadcopter needs to recover autonomously from faults. However, existing approaches mainly rely on parametrical faults or require a predefinition of possible faults which is not realistic for a complex realworld scenario. The recovery from unforeseen faults and structural faults like a failing engine is still an open research gap. Hence, in this paper, a concept for the automated reconfiguration, i.e. the automated recovery from a fault, which only uses information about non-faulty system behavior and is able to handle structural changes is presented. From the information about non-faulty behavior a non-faulty system model is created using established machine learning methods. Thus, faults are detected by learned model and no pre-definition of faults is needed. The system structure is modeled using a logical calculus which allows for modeling available system parts and the causal coherences between these. The approach is applied to a simulation of a quadcopter which underlies a structural fault. It is shown that the approach extends the capabilities of a quadcopter to handle faults autonomously and ensure stability and reliability.