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Actuators
Volume 14
Issue 2
10.3390/act14020066
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Jiying Wu
1,
Zhong Yang
1,*,
Haoze Zhuo
1,
Changliang Xu
2,
Luwei Liao
1,
Danguo Cheng
1 and
Zhiyong Wang
1
1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
College of Electronic Engineering, Nanjing XiaoZhuang University, Nanjing 211171, China
*
Author to whom correspondence should be addressed.
Actuators 2025, 14(2), 66; https://doi.org/10.3390/act14020066
Submission received: 17 December 2024 / Revised: 22 January 2025 / Accepted: 29 January 2025 / Published: 31 January 2025
(This article belongs to the Section Actuators for Robotics)
Versions Notes

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.
Keywords: airborne robotic arm; floating target tracking; reinforcement learning; inverse kinematic solution airborne robotic arm; floating target tracking; reinforcement learning; inverse kinematic solution

Share and Cite

MDPI and ACS Style

Wu, J.; Yang, Z.; Zhuo, H.; Xu, C.; Liao, L.; Cheng, D.; Wang, Z. Encouraging Guidance: Floating Target Tracking Technology for Airborne Robotic Arm Based on Reinforcement Learning. Actuators 2025, 14, 66. https://doi.org/10.3390/act14020066

AMA Style

Wu J, Yang Z, Zhuo H, Xu C, Liao L, Cheng D, Wang Z. Encouraging Guidance: Floating Target Tracking Technology for Airborne Robotic Arm Based on Reinforcement Learning. Actuators. 2025; 14(2):66. https://doi.org/10.3390/act14020066

Chicago/Turabian Style

Wu, Jiying, Zhong Yang, Haoze Zhuo, Changliang Xu, Luwei Liao, Danguo Cheng, and Zhiyong Wang. 2025. "Encouraging Guidance: Floating Target Tracking Technology for Airborne Robotic Arm Based on Reinforcement Learning" Actuators 14, no. 2: 66. https://doi.org/10.3390/act14020066

APA Style

Wu, J., Yang, Z., Zhuo, H., Xu, C., Liao, L., Cheng, D., & Wang, Z. (2025). Encouraging Guidance: Floating Target Tracking Technology for Airborne Robotic Arm Based on Reinforcement Learning. Actuators, 14(2), 66. https://doi.org/10.3390/act14020066

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