Uncertainty-Aware Robust UAV Trajectory Planning With Dynamic Collision Avoidance

Trajectory planning and obstacle avoidance technologies for unmanned aerial vehicles (UAVs) are widely applied in Internet of Things-based intelligent urban management, data collection, and related fields, and are increasingly becoming a global research hotspot. However, uncertainties in trajectory planning caused by factors such as sensor measurement noise, model mismatch, and environmental disturbances can compromise the safety and robustness of UAV flights. While existing optimization-based methods build complex nonlinear models, they are often computationally expensive and inefficient. Learning-based methods, on the other hand, demand substantial computational resources. In this paper, we develop a nonlinear chance-constrained trajectory planning model that explicitly accounts for uncertainties, enabling autonomous obstacle avoidance and landing of UAVs on a dynamic platform. We derive the robust equivalent form of the chance constraints to address the solvability of models that include uncertainty factors. We develop a method that combines lossless convexification with the sequential convex programming (SCP) algorithm to achieve low complexity and high-efficiency solutions. Additionally, a real-time planning framework is proposed to address uncertain dynamic environments. We validate the robustness and safety of the proposed algorithm under various dynamic and uncertain scenarios, including different levels of disturbance, moving platforms, and unpredictable obstacles.