The unmanned aerial vehicle routing and trajectory optimisation problem, a taxonomic review

Coutinho, Walton Pereira; Battarra, Maria; Fliege, Jörg · 2018 · OpenAlex-citations

DOI: 10.1016/j.cie.2018.04.037

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Summary

This paper addresses the lack of a unified framework for Unmanned Aerial Vehicle (UAV) routing and trajectory optimization, a gap that hinders the adoption of UAVs in complex real-world applications. While UAV usage has surged in logistics, surveillance, and disaster response, existing literature often treats routing (combinatorial optimization) and trajectory optimization (optimal control) as separate problems. This separation neglects critical flight dynamics, such as battery life, wind conditions, and vehicle kinematics, which can render purely geometric routes infeasible or inefficient. The authors aim to formally define the UAV Routing and Trajectory Optimisation Problem (UAVRTOP) and provide a taxonomic review of existing contributions to identify key components, assumptions, and future research directions. The study employs a taxonomic review methodology to categorize and analyze recent literature on UAV trajectory optimization, routing, and task assignment. The authors first provide a formal mathematical formulation of the UAVRTOP, modeling it as an integrated optimization problem where a fleet of UAVs visits a set of waypoints while satisfying generic kinematics and dynamics constraints. This formulation combines elements of the Vehicle Routing Problem (VRP) and Optimal Control (OC), incorporating system dynamics expressed through Ordinary Differential Equations (ODEs). The paper then constructs a taxonomy based on 20 attributes grouped into five classes: UAV characteristics (e.g., fleet heterogeneity, capacity, dynamic modeling), waypoint attributes (e.g., ordering, visit constraints), environmental factors, launching point characteristics, and flight duration. This taxonomy is applied to selected papers to demonstrate its utility in identifying common simplifications and modeling choices in the field. The review highlights that while trajectory optimization and path planning are well-studied in aerospace engineering and robotics, respectively, their integration with routing decisions remains fragmented. The authors distinguish between Path Planning (PP), which finds geometric paths ignoring dynamics, and Trajectory Optimization (TO), which assigns time laws and controls to satisfy dynamic constraints. They note that many existing UAV routing studies neglect flight dynamics or rely on simplified models like Dubin’s vehicles, which may not accurately represent fixed-wing UAVs. The taxonomy reveals that few studies simultaneously address routing decisions and high-fidelity trajectory optimization, often due to the computational complexity of integrating combinatorial routing with continuous optimal control. The formal definition of UAVRTOP presented in the paper serves as a conceptual model to bridge this gap, minimizing costs associated with vehicle usage, routing, and trajectory quality while respecting operational and dynamic constraints. The significance of this work lies in its provision of a structured framework for understanding the complexities of UAV operations. By introducing the UAVRTOP and a comprehensive taxonomy, the paper clarifies the distinctions between various problem variants and highlights the necessity of integrating routing and trajectory decisions for feasible and efficient UAV missions. This review identifies key research opportunities, particularly in developing algorithms that can handle the coupled combinatorial and continuous nature of the problem. The findings support the development of more realistic models for UAV applications, such as last-mile delivery and surveillance, where flight dynamics significantly impact route feasibility and performance.

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