Collision avoidance is an essential concern for the autonomous operations of aerial vehicles in dynamic and uncertain urban environments. This paper introduces a risk-bounded path planning algorithm for unmanned aerial vehicles (UAVs) operating in such environments. This algorithm advances the rapidly-exploring random tree (RRT) with chance constraints to generate probabilistically guaranteed collision-free paths that are robust to vehicle and environmental obstacle uncertainties. Assuming all uncertainties follow Gaussian distributions, the chance constraints are established through converting dynamic and probabilistic constraints into equivalent static and deterministic constraints. By incorporating chance constraints into the RRT algorithm, the proposed algorithm not only inherits the computational advantage of sampling-based algorithms but also guarantees a probabilistically feasible flying zone at every time step. Simulation results show the promising performance of the proposed algorithm.
翻译:避免碰撞是飞行器在动态和不确定的城市环境中自主运作的基本关切,本文件为在这种环境中运作的无人驾驶航空器引入了一种有风险的路径规划算法,这种算法推进了快速探索随机树(RRT),有几处机会限制,以产生对车辆和环境障碍的不确定性具有可靠保障的概率无碰撞路径。假设所有不确定因素都随高斯分布而变化,则通过将动态和概率限制转化为等效的静态和确定性限制来确立机会限制。通过将机会限制纳入RRT算法,拟议的算法不仅继承了取样算法的计算优势,而且还保证了每个步骤都具有概率可行的飞行区。模拟结果显示拟议的算法有良好的表现。