Eye in the Sky: Energy Efficient Model-Based Reinforcement Learning Aided Target Tracking Using UAVs

The rapid response and high energy efficiency of the unmanned aerial vehicle (UAV) are crucial prerequisites for enabling time-sensitive and long-endurance target tracking missions, such as search and rescue, area reconnaissance, and convoy monitoring. However, existing research in target tracking p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on vehicular technology 2024-01, Vol.73 (12), p.19464-19479
Main Authors: Xia, Yi, Zhang, Zekai, Xu, Jingzehua, Ren, Pengfei, Wang, Jingjing, Han, Zhu
Format: Article
Language:English
Subjects:
Algorithms
Autonomous aerial vehicles
Constraints
Control systems design
Convergence
Cost effectiveness
Costs
Deep reinforcement learning
Energy consumption
mobile target tracking
Reconnaissance aircraft
Robust control
Search and rescue missions
Steady state models
Target tracking
time constraints
Time factors
Tracking
UAV
Unmanned aerial vehicles
Vehicle dynamics
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapid response and high energy efficiency of the unmanned aerial vehicle (UAV) are crucial prerequisites for enabling time-sensitive and long-endurance target tracking missions, such as search and rescue, area reconnaissance, and convoy monitoring. However, existing research in target tracking primarily focuses on enhancing tracking accuracy, which struggles to adapt to tasks considering strict time constraints and energy consumption. To address these issues, this paper introduces a model-based reinforcement learning tracking strategy (MRLTS) for the UAV to minimize control costs and achieve user-specified tracking performance, including a two-stage design. In the first stage, a steady-state robust tracking controller is developed based on available model knowledge that forces the UAV to asymptotically approximate a predefined observation path in spite of uncertainties. In the second stage, an intelligent component based on the soft actor-critic (SAC) algorithm is customized to empower the UAV to strike a trade-off between prescribed tracking performance and energy consumption, wherein a skilled barrier function is constructed to interpret specified time constraints. The proposed paradigm can provide a higher sampling efficiency than SAC-based strategy. Simulation results demonstrate that our strategy outperforms benchmarks and results in a 46.3% cost-effectiveness improvement at least.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3437776