In this article, a fast chance-constrained trajectory generation strategy incorporating convex optimization and convex approximation of chance constraints is designed so as to solve the unmanned vehicle path planning problem. A path-length-optimal unmanned vehicle trajectory optimization model is constructed with the consideration of the pitch angle constraint, the curvature radius constraint, the probabilistic control actuation constraint, and the probabilistic collision avoidance constraint. Subsequently, convexification technique is introduced to convert the nonlinear problem formulation into a convex form. To deal with the probabilistic constraints in the optimization model, convex approximation techniques are introduced such that the probabilistic constraints are replaced by deterministic ones while simultaneously preserving the convexity of the optimization model. Numerical results, obtained from a number of case studies, validate the effectiveness and reliability of the proposed approach. A number of comparative studies were also performed. The results confirm that the proposed design is able to produce more optimal flight paths and achieve enhanced computational performance than other chance-constrained optimization approaches investigated in this article.

中文翻译：

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无人飞行器机会约束飞行轨迹的快速生成

本文设计了一种结合凸优化和凸逼近机会约束的快速机会约束轨迹生成策略，以解决无人驾驶汽车路径规划问题。考虑俯仰角约束，曲率半径约束，概率控制致动约束和概率碰撞回避约束，构造了路径长度最优的无人车辆轨迹优化模型。随后，引入凸化技术将非线性问题公式转换为凸形。为了处理优化模型中的概率约束，引入了凸逼近技术，从而用概率性约束替换为确定性约束，同时保留优化模型的凸性。从大量案例研究中获得的数值结果验证了该方法的有效性和可靠性。还进行了许多比较研究。结果证实，与本文研究的其他机会受限的优化方法相比，所提出的设计能够产生更多的最佳飞行路径并获得增强的计算性能。