%0 Generic
%A Gil García, Guillermo
%A Cobano-Suárez, José-Antonio
%A Merino, Luis
%A Caballero, Fernando
%T C-3TO: Continuous 3D Trajectory Optimization on Neural Euclidean Signed Distance Fields
%D 2026
%U https://hdl.handle.net/10433/27332
%X This 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.
%K Trajectory Planning
%K Distance Map
%K Local Path
%K Signed Distance Function
%K Safe Path
%K 3D Trajectory Planning
%K Path Planning
%K Trajectory Optimization
%K Neural Network
%K Urban Planning
%K Pathfinding
%K Representation Of The Environment
%K Planning Algorithm
%K Software Framework
%K Efficient Path
%K Path Computation
%K Robot Operating System
%K Safe Navigation
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