RT Conference Proceedings
T1 A framework for safe local 3D path planning based on online neural euclidean signed distance fields
A1 Gil García, Guillermo
A1 Cobano-Suárez, José-Antonio
A1 Caballero, Fernando
A1 Merino, Luis
K1 Path Planning
K1 Distance Map
K1 Local Path
K1 Signed Distance Function
K1 Safe Path
K1 3D Path Planning
K1 Neural Network
K1 Urban Planning
K1 Pathfinding
K1 Representation Of The Environment
K1 Planning Algorithm
K1 Software Framework
K1 Efficient Path
K1 Path Computation
K1 Robot Operating System
K1 Safe Navigation
AB 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
PB IEEE
YR 2025
FD 2025-05-27
LK https://hdl.handle.net/10433/23952
UL https://hdl.handle.net/10433/23952
LA en
NO 2025 International Conference on Unmanned Aircraft Systems (ICUAS), Charlotte, NC, USA, 2025, pp. 511-517
NO Service Robotics Lab
DS RIO
RD May 5, 2026