Human-Centric Aware UAV Trajectory Planning in Search and Rescue
  Missions Employing Multi-Objective Reinforcement Learning with AHP and
  Similarity-Based Experience Replay
Mahya Ramezani Jose Luis Sanchez-Lopez
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.