Research On Key Technologies Of Task Scheduling For Multi-airship Multi-payload Earth Observing

With the rapid change of earth observing technologies, near-space has attracted focus from all over the world because of its special resource advantages. As the main flatform of nesr-space earth observation, stratospheric airship has the properties of high resolution, daylong observing continuousness and large cover range, can both halt at designated spot or cruise along the flight course. It fills in the gaps between observing satelites and observing unmanned aerial vehicles(UAVs). In order to take advantage of the superiority of earth observation on stratospheric multi-airship, it is important to locate the multi-airship legitimately and allocate the observing tasks effectively, so as to improve the task completion quality as well as lowering the total energy cost, thus increasing the efficiency of Multi-airship Multi-payload Earth Observation(MMEO) system.Referring to the current study both domestic and abroad, this dissertation focuses on the key technologies of task schedualing for multi-airship multi-payload earth observing. The main work can be concluded as following:1、The problem of MMEO is analyzed and a descriptive model is designed based on Ontology. The outline of MMEO is expounded at first, followed by analysing of the work procedure and characteristics. Then after summarizing the resource properties, task requirements as well as their restrictions, the discriptive models are designed based on Ontology including airship model, payload model and observing task model, which makes it standardized,explicit and unified to depict the problem.2、The approximate imaging calculation method with swinging of payloads is studyed. The swinging of payloads makes large observing coverage come true, but affects the observing resolution adversely at the same time. The intrinsic property of payload is analyzed first, then we investigate the calculation of coverage area when target point locates at the same latitude with the payload and otherwise respectively.After that the airship coverage and resolution approximate calculation are posed, with the conclusion that resolution weakened sharply as the distance increases.3、A method of multi-airship location deployment based on chaotic particle swarm optimization(CPSO) is present. On the basis of thorough analysis of this problem, an appropriate constraint-satisfying optimization model is established and CPSO is proposed to solve the model, which reserves the advantages of PSO and avoids the defect of random initialization and premature constringency by virtue of chaotic optimization.Simulation proves the high speed and effectiveness of CPSO on solving the problem of multi-airship location deployment.4、A method of multi-airship task allocation based on discrete particle swarm optimization(DPSO) is proposed. Constraint-satisfying optimization model is established after intrinsic restriction and objective function are fully discussed. A fitable dissociation and coding method is selected for the purpose of solving this model. Simulation proves that the CPSO proposed to solve multi-airship task allocation is effective.