The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue
(SAR) missions presents a promising avenue for enhancing operational efficiency
and effectiveness. However, the success of these missions is not solely
dependent on the technical capabilities of the drones but also on their
acceptance and interaction with humans on the ground. This paper explores the
effect of human-centric factor in UAV trajectory planning for SAR missions. We
introduce a novel approach based on the reinforcement learning augmented with
Analytic Hierarchy Process and novel similarity-based experience replay to
optimize UAV trajectories, balancing operational objectives with human comfort
and safety considerations. Additionally, through a comprehensive survey, we
investigate the impact of gender cues and anthropomorphism in UAV design on
public acceptance and trust, revealing significant implications for drone
interaction strategies in SAR. Our contributions include (1) a reinforcement
learning framework for UAV trajectory planning that dynamically integrates
multi-objective considerations, (2) an analysis of human perceptions towards
gendered and anthropomorphized drones in SAR contexts, and (3) the application
of similarity-based experience replay for enhanced learning efficiency in
complex SAR scenarios. The findings offer valuable insights into designing UAV
systems that are not only technically proficient but also aligned with
human-centric values.

通过加强学习结合分析层次过程和基于相似性的经验重放，优化无人机轨迹，平衡运营目标与人类安全、舒适性等考虑因素，同时调查性别线索和拟人化在公众接受和信任方面的影响，在搜索与救援任务中设计与人类价值观相符的无人机系统的宝贵洞察。

应用层次分析法和基于相似度的经验回放的多目标强化学习在搜救任务中以人为中心的无人机航迹规划

Human-Centric Aware UAV Trajectory Planning in Search and Rescue  Missions Employing Multi-Objective Reinforcement Learning with AHP and  Similarity-Based Experience Replay