摘要: The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. However, the operational safety of these systems... 展开 The use of autonomous flight vehicles has recently increased due to their versatility and capability of carrying out different type of missions in a wide range of flight conditions. However, the operational safety of these systems can easily be affected if abnormal flight conditions are not timely detected, compromising the nominal bounds of design of the system's flight envelop. This paper aims to provide a methodology for modeling and protection of autonomous vehicle trajectories under normal and abnormal flight conditions. An artificial immune system (AIS) framework is combined with the multi-goal Rapidly-Exploring Random Tree (RRT*) path planning algorithm to generate safe trajectories based on a reduced flight envelop. A high-fidelity model of a fixed-wing unmanned aerial vehicle is used to demonstrate the capabilities of the approach by timely generating safe trajectories as an alternative to original paths. 收起