Path planning for small unmanned aerial systems (SUAS) in the presence of poorly understood and dynamic obstacles is a challenging problem: for example, flight over a spreading wildfire. Evidential information fusion is used to estimate the current wildfire state and the resulting heat aura at flight level. This approach accounts for ignorance, which is a result of conflict among sensors operating in a harsh environment and a computational forecasting agent that uses a fire evolution model of inadequate accuracy. An SUAS is employed to visit locations of high conflict to provide additional situational awareness. Flight-level heat aura is modeled as a keepout zone with probabilistic boundaries for SUAS path planning. A novel unsupervised classification algorithm is developed to identify distinct obstacle boundaries within the estimated heat aura. Path planning is posed as a chance-constrained optimal control problem, which is transcribed to a nonlinear program via pseudospectral discretization. The results show that this approach can yield a family of solutions that elicit the risk associated with each mission design, and the appropriate choice of risk can aid in the generation of “keyhole paths.”


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    Title :

    Risk-Aware Path Planning for Unmanned Aerial Systems in a Spreading Wildfire


    Contributors:

    Published in:

    Publication date :

    2022-09-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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