Jul 2026· 2026 IEEE 27th China Conference on System Simulation Technology and its Applications (CCSSTA)· pp. 359-364· 0 citations· 14 references
Abstract
To address multi-UAV task allocation and dynamic obstacle avoidance in post-disaster scenarios, this paper proposes a hierarchical GOAPF-MARL (Goal-Oriented Artificial Potential Field Guided Multi-Agent Reinforcement Learning) framework. In the perception layer, a task model is built to couple casualty level and disaster scale, forming a unified task weight W and enabling more accurate demand representation under varying conditions. For upper-layer allocation, an improved SADCK-Medoid method is used with the Balanced Residual Capacity (BRC) metric to enable nonuniform UAV deployment across five rescue regions. In the execution layer, a Goal-Oriented Artificial Potential Field (GOAPF) is embedded into multi-agent reinforcement learning (MARL), improving navigation around buildings, debris, and expanding fire zones while enhancing coordination and decision stability in complex scenes. Simulation results demonstrate improved efficiency and robustness compared with baseline methods under both ideal and challenging settings.
Cooperative navigation of multiple unmanned aerial vehicles (UAVs) in disaster search-and-rescue scenarios is challenging due to dense obstacles, partial observability, and strong inter-agent coupling, which often result in path conflicts, collision risks, and limited policy generalization. To address these challenges,...
Li Tan, Hai-Xia Zhao, Jia-Qin Chai et al.· Unmanned Systems· 0 citations
An improved deep Q-network (DQN) algorithm is proposed, introducing a priority experience replay mechanism to improve sample utilization, and designing a composite reward function including arrival reward, step penalty, direction guidance, and safety penalty to guide the UAV to plan safe and efficient flight paths in c...
Rui Qin, Han-Jing Zhou· International Conference on...· 0 citations
In complex scenarios such as emergency rescue, power inspection, and battlefield reconnaissance, multi-UAV collaborative task allocation is the core technology for improving task execution efficiency and guaranteeing task reliability. However, the multi-source uncertainty in real scenes severely limits the adaptability...
Efficient relief supply delivery within the golden 72 hours after a disaster is critical to reducing casualties. For medium-to-large unmanned aerial vehicles (UAVs), pre-flight preparation—including payload mounting, center-of-gravity confirmation, flight route distribution, and link/airspace checks—is abstracted as re...
Jiang-Wei Zhao, Qi Feng, Cheng Zhu et al.· 2026 12th International Conf...· 0 citations
Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection is a promising solution for timely information acquisition in post-disaster scenarios with damaged terrestrial infrastructure. However, freshness-aware UAV trajectory planning is challenging due to the coupled effects of heterogeneous groun...
Jinghao Wang, Xu Wang, Ji-Hao Luo et al.· IEEE Wireless Communications...· 0 citations
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