Unmanned Aerial Vehicles (UAVs) demonstrated improved response times and safety in life-saving missions. This paper presents a Model Predictive Control (MPC) framework for autonomous UAV search missions in maritime environments, where the UAV must locate multiple castaways floating on the sea surface after a maritime incident. The approach uses receding horizon optimization to plan trajectories that balance two competing objectives: achieving rapid area coverage at high altitudes versus maintaining reliable target detection at lower altitudes. Target detection relies on Convolutional Neural Networks (CNNs), with detection performance characterized through field experiments that measure True Positive (TP) rates and False Positive (FP) rates across multiple flight altitudes. The MPC framework dynamically adjusts the UAV’s altitude and trajectory based on these altitude dependent detection statistics, enabling mission adaptive behavior that outperforms constant-altitude search patterns. Simulation results demonstrate improved search performance compared to conventional constant-altitude missions. Real-world flight experiments validate the practical applicability of the proposed framework and confirm its effectiveness in realistic maritime search scenarios.
Andreas Anastasiou, Savvas Papaioannou, P. Kolios et al.· Journal of Intelligent &...· 0 citations
This paper addresses morning commute congestion caused by concentrated school-related trips in urban networks. We propose a bi-level optimization framework for regulating school start times in a multi-region urban network characterized by Macroscopic Fundamental Diagrams (MFDs), explicitly coupling system-level regulation with multi-class user-equilibrium-based departure-time choices. The Upper-Level problem jointly minimizes total time spent and deviations from current school schedules, while the Lower-Level problem models commuter behavior through a deterministic dynamic multi-class user equilibrium formulation incorporating alpha-beta-gamma preferences for travel time, earliness, and lateness costs. To address the computational challenges arising from the bilevel structure, non-convex traffic dynamics, and endogenous demand responses, an iterative algorithm alternating between the Upper- and Lower-Level problems is developed. The Upper-Level problem is approximated through a formulation solvable with standard mathematical programming solvers, while an iterative algorithm provides an approximate solution to the Lower-Level equilibrium problem. Numerical results demonstrate substantial congestion reductions and characterize the trade-off between school start-time flexibility and traffic efficiency. Sensitivity analyses further examine the effects of MFD uncertainty and scheduling preferences.
A. Georgantas, S. Timotheou, Christos G. Panayiotou· 0 citations
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