Encouraging Guidance: Floating Target Tracking Technology for Airborne Robotic Arm Based on Reinforcement Learning

Abstract

Aerial robots equipped with operational robotic arms are a powerful means of achieving aerial contact operations, and their core competitiveness lies in target tracking control at the end of the airborne robotic arm (ARA). In order to improve the learning efficiency and flexibility of the ARA control algorithm, this paper proposes the encouraging guidance of an actor–critic (Eg-ac) algorithm based on the actor–critic (AC) algorithm and applies it to the floating target tracking control of ARA. It can quickly lock in the exploration direction and achieve stable tracking without increasing the learning cost. Firstly, this paper establishes approximate functions, policy functions, and encouragement functions for the state value of ARA. Secondly, an adoption rate controller (ARC) module was designed based on the concept of heavy rewards and light punishments (HRLP). Then, the kinematic and dynamic models of ARA were established. Finally, simulation was conducted using stable baselines3 (SB3). The experimental results show that, under the same computational cost, the convergence speed of the Eg-ac is improved by 21.4% compared to deep deterministic policy gradient (DDPG). Compared with soft actor–critic (SAC) and DDPG, Eg-ac has improved learning efficiency by at least 20% and has a more agile and stable floating target tracking effect.