An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections

• Published in: International Conference on Advanced Cloud and Big Data pp. 222 - 227
• Main Authors: Wang, Yifan, Zeng, Wei, Li, Guanyu, Xiong, Chenglong, Wang, Zicheng, Mao, Yingchi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.11.2024
• Subjects: Autonomous aerial vehicles; Cooperative inspection; Coverage path planning; Critical infrastructure; Energy consumption; Inspection; Long short term memory; Markov decision processes; Multi-agent deep reinforcement learning; Optimization; Simulation; Termination of employment; Vehicle dynamics
• ISSN: 2573-301X
• DOI: 10.1109/CBD65573.2024.00048

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as EA-MATD3.EA-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate.