(An) integrated motion planning framework for autonomous aerial robots in complex environments

Abstract

This study proposes an efficient solution for autonomous flight for aerial robots in complex environments by introducing MAT-CMPC-based global/local planning and reinforcement learning (RL)-based reactive obstacle avoidance. The framework integrates three key components. First, a Medial Axis Transformation (MAT)-based Safe Flight Corridor (SFC) defines safe zones by generating sequential spherical volumes, ensuring safe navigation through global planning. Second, the MAT-Convex Model Predictive Control (CMPC) formulation optimizes local trajectories, ensuring smooth and collision-free navigation. Third, the RL-based reactive obstacle avoidance method enables real-time handling of unforeseen obstacles through quick and reactive control. Simulations demonstrated that the MAT-CMPC framework efficiently navigates complex environments, outperforming existing methods in computation time, trajectory length, and flight duration. Additionally, the RL-based reactive obstacle avoidance method showed adaptability to various obstacle environments, forward velocities, and local planner configurations. Real-world experiments further confirmed the computational efficiency and practical applicability of the RL-based reactive obstacle avoidance method using a multirotor equipped with an onboard computer.