Policy-based monocular vision autonomous quadrotor obstacle avoidance method

Abstract

Aiming at the obstacle avoidance control problem of small quadrotor, a method of quadrotor obstacle avoidance based on reinforcement learning is proposed. The proposed method can make training converge quickly and has good environmental robustness. The proposed methods include: (1) a framework adopts perception module and decision module to improve the generalization ability of the obstacle avoidance model; (2) An Actor-Critic framework-based Proximal Policy Optimization (PPO) algorithm to provide quadrotor with policy-based decision-making capabilities; The experimental simulation results show that the strategy-based framework converges quickly and has a high success rate, the training time is much lower than that of the value-based framework. The monocular vision observation ability is limited, which leads to deviations between local observation and global state, So LSTM layer is usually added to increase model performance. Policy -based decision can have a good obstacle avoidance effect without adding the LSTM layer, and have good generalization ability after short relearning after changing.