Gil García, GuillermoCobano-Suárez, José-AntonioCaballero, FernandoMerino, Luis2025-05-302025-05-302025-05-272025 International Conference on Unmanned Aircraft Systems (ICUAS), Charlotte, NC, USA, 2025, pp. 511-51710.1109/ICUAS65942.2025.11007788https://hdl.handle.net/10433/23952This paper presents an open-source framework that integrates a distance-aware 3D local path planning al-gorithm based on Euclidean Signed Distance Fields (ESDFs) with an online generated Sinusoidal Representation Neural Network (SIREN) to estimate the required ESDF. The main con-tribution of the paper is a software framework that incorporates an online generated ESDF into local planners for efficient and safe 3D path planning by leveraging the ESDF properties. The framework includes a neural network that can be used by the local planner as an up-to-date representation of the environment. Experimental validation shows favorable results in exploiting the intrinsic characteristics of online generated ESDFs and acknowledges this framework as a feasible method to perform local path computation. The source code of the frame-work and more details about the software implementation is available at: https://github.com/robotics-upo/neural_esdf_localapplication/pdfenPath PlanningDistance MapLocal PathSigned Distance FunctionSafe Path3D Path PlanningNeural NetworkUrban PlanningPathfindingRepresentation Of The EnvironmentPlanning AlgorithmSoftware FrameworkEfficient PathPath ComputationRobot Operating SystemSafe Navigationconference outputrestricted access