Gil García, GuillermoCobano-Suárez, José-AntonioMerino, LuisCaballero, Fernando2026-08-262026-08-262026-07-142026 International Conference on Unmanned Aircraft Systems (ICUAS), Corfu, Greece, 2026, pp. 161-16810.1109/ICUAS69441.2026.11598735https://hdl.handle.net/10433/27332This paper introduces a novel framework for continuous 3D trajectory optimization (C-3TO) in cluttered environments, leveraging online neural Euclidean Signed Distance Fields (ESDFs). Unlike prior approaches that rely on discretized ESDF grids with interpolation, our method directly optimizes smooth trajectories represented by fifth-order polynomials over a continuous neural ESDF, ensuring precise gradient information throughout the entire trajectory. The framework integrates a two-stage nonlinear optimization pipeline that balances efficiency, safety and smoothness. Experimental results demonstrate that C-3TO produces collision-aware and dynamically feasible trajectories. Moreover, its flexibility in defining local window sizes and optimization parameters enables straightforward adaptation to diverse user’s needs without compromising performance. By combining continuous trajectory parameterization with a continuously updated neural ESDF, C-3TO establishes a robust and generalizable foundation for safe and efficient local replanning in aerial robotics.application/pdfenIEEETrajectory PlanningDistance MapLocal PathSigned Distance FunctionSafe Path3D Trajectory PlanningPath PlanningTrajectory OptimizationNeural NetworkUrban PlanningPathfindingRepresentation Of The EnvironmentPlanning AlgorithmSoftware FrameworkEfficient PathPath ComputationRobot Operating SystemSafe Navigationconference outputrestricted access