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.”

在存在知之甚少和动态障碍物的情况下，小型无人机系统 (SUAS) 的路径规划是一个具有挑战性的问题：例如，在蔓延的野火上空飞行。证据信息融合用于估计当前的野火状态和在飞行高度产生的热光环。这种方法解释了无知，这是在恶劣环境中运行的传感器与使用精度不足的火灾演化模型的计算预测代理之间发生冲突的结果。SUAS 用于访问高度冲突的地点，以提供额外的态势感知。飞行水平的热光环被建模为具有概率边界的禁区，用于 SUAS 路径规划。开发了一种新的无监督分类算法来识别估计的热光环内不同的障碍物边界。路径规划被提出为机会约束的最优控制问题，通过伪谱离散化转化为非线性程序。结果表明，这种方法可以产生一系列解决方案，这些解决方案会引发与每个任务设计相关的风险，并且适当的风险选择可以帮助生成“钥匙孔路径”。