RHSOA: Receding Horizon Optimization-Based Swift Obstacle Avoidance Approach for UAVs in Unknown Environments

This paper presents a novel approach, called RHSOA, for real-time obstacle avoidance control of nonlinear systems in dynamic environments, considering safety and speed for UAVs. The approach relies on the integration of nonlinear receding horizon optimization (RHO), joint control Lyapunov functions (CLFs), and control barrier functions (CBFs). The proposed method is aimed at achieving close-to-optimal obstacle avoidance control for the demand of real-time obstacle avoidance in unknown dynamic environments of UAVs. The method is divided into two main parts: firstly, the classification of obstacles is pre-processed, and a safety-enveloping ellipse is proposed to associate obstacles, which reduces the computational workload. Secondly, the RHO is improved under the limited time, taking into account both rapidity and safety priority. The effectiveness of the proposed method is demonstrated by comparing simulation results and performance metrics. The results show that the proposed method achieves superior performance and outperforms some existing approaches.