%0 Generic
%A Gil García, Guillermo
%A Cobano-Suárez, José-Antonio
%A Caballero, Fernando
%A Merino, Luis
%T A framework for safe local 3D path planning based on online neural euclidean signed distance fields
%D 2025
%U https://hdl.handle.net/10433/23952
%X This 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_local
%K Path Planning
%K Distance Map
%K Local Path
%K Signed Distance Function
%K Safe Path
%K 3D Path Planning
%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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