Targeting the challenges of complex and volatile environments

difficulties in multi-Unmanned Aerial Vehicle(UAV) coordination

and insufficient dynamic risk avoidance in low-altitude emergency rescue

An intelligent collaborative control technology based on multi-dimensional situational awareness is proposed. By constructing a heterogeneous information fusion model based on dynamic bayesian networks

airborne sensor data and external spatio-temporal situational data are mapped into a 3D dynamic risk map. On this basis

a risk-coupled multi-agent reinforcement learning scheduling strategy and an adaptive path planning method are designed

to integrate real-time risk quantification into the decision-making and planning loop. Field experiments demonstrate that this method significantly improves the perception accuracy of hidden dynamic risks

increases mission success rate

shortens obstacle-avoidance response time and overall mission completion time

and reduces risk exposure duration in complex environments

thereby providing technical support for safe and efficient low-altitude emergency rescue systems.