In network-unavailable environments, the path planning of unmanned aerial vehicles (UAVs) becomes constrained by limitations in their energy resources and computing capabilities within a predefined mission timeframe. Additional low-power flight control must be considered to effectively complete a flight mission. To support UAV path planning in network-unavailable environments, this paper proposes a multi-sensor self-adaptive scheduling and optimized depth deterministic strategy gradient (MAS-DDPG) algorithm. Specifically, unlike considering activating all sensors in varying scenarios, a multi-sensor self-adaptive scheduling (MAS) strategy based on Multi-nominal Logit (MNL) is proposed. This approach allows the UAV to choose an appropriate scheduling strategy according to different scenarios. Furthermore, instead of relying on randomly chosen experiences from a pool, a mechanism based on screening experience is proposed. This mechanism takes into account successful experiences along with their contextual counterparts to enhance the learning process. Our goal is to maximize the average flight speed of the UAV and minimize the number of flight decisions and flight energy consumption. Simulation results show that the proposed approach can achieve significantly hinter speed and lower energy consumption than the benchmark and other typical algorithms.
MAS-DDPG: Multi-Sensor Self-Adaptive Scheduling for UAV Path Planning in Network-Unavailable Environments
2023-12-21
1014689 byte
Conference paper
Electronic Resource
English
Research on dynamic path planning of mobile robot based on improved DDPG algorithm
BASE | 2021
|An Optimized Path Planning Method for Coastal Ships Based on Improved DDPG and DP
DOAJ | 2021
|Decentralized adaptive calculation unloading method based on DDPG algorithm
European Patent Office | 2024
|