SPADE: Towards Scalable Path Planning Architecture on Actionable Multi-Domain 3D Scene Graphs

Fuente: arXiv
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Main Authors: Viswanathan, Vignesh Kottayam, Patel, Akash, Saucedo, Mario Alberto Valdes, Satpute, Sumeet, Kanellakis, Christoforos, Nikolakopoulos, George
Format: Preprint
Published: 2025
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author Viswanathan, Vignesh Kottayam
Patel, Akash
Saucedo, Mario Alberto Valdes
Satpute, Sumeet
Kanellakis, Christoforos
Nikolakopoulos, George
author_facet Viswanathan, Vignesh Kottayam
Patel, Akash
Saucedo, Mario Alberto Valdes
Satpute, Sumeet
Kanellakis, Christoforos
Nikolakopoulos, George
contents In this work, we introduce SPADE, a path planning framework designed for autonomous navigation in dynamic environments using 3D scene graphs. SPADE combines hierarchical path planning with local geometric awareness to enable collision-free movement in dynamic scenes. The framework bifurcates the planning problem into two: (a) solving the sparse abstract global layer plan and (b) iterative path refinement across denser lower local layers in step with local geometric scene navigation. To ensure efficient extraction of a feasible route in a dense multi-task domain scene graphs, the framework enforces informed sampling of traversable edges prior to path-planning. This removes extraneous information not relevant to path-planning and reduces the overall planning complexity over a graph. Existing approaches address the problem of path planning over scene graphs by decoupling hierarchical and geometric path evaluation processes. Specifically, this results in an inefficient replanning over the entire scene graph when encountering path obstructions blocking the original route. In contrast, SPADE prioritizes local layer planning coupled with local geometric scene navigation, enabling navigation through dynamic scenes while maintaining efficiency in computing a traversable route. We validate SPADE through extensive simulation experiments and real-world deployment on a quadrupedal robot, demonstrating its efficacy in handling complex and dynamic scenarios.
format Preprint
id arxiv_https___arxiv_org_abs_2505_19098
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle SPADE: Towards Scalable Path Planning Architecture on Actionable Multi-Domain 3D Scene Graphs
Viswanathan, Vignesh Kottayam
Patel, Akash
Saucedo, Mario Alberto Valdes
Satpute, Sumeet
Kanellakis, Christoforos
Nikolakopoulos, George
Robotics
In this work, we introduce SPADE, a path planning framework designed for autonomous navigation in dynamic environments using 3D scene graphs. SPADE combines hierarchical path planning with local geometric awareness to enable collision-free movement in dynamic scenes. The framework bifurcates the planning problem into two: (a) solving the sparse abstract global layer plan and (b) iterative path refinement across denser lower local layers in step with local geometric scene navigation. To ensure efficient extraction of a feasible route in a dense multi-task domain scene graphs, the framework enforces informed sampling of traversable edges prior to path-planning. This removes extraneous information not relevant to path-planning and reduces the overall planning complexity over a graph. Existing approaches address the problem of path planning over scene graphs by decoupling hierarchical and geometric path evaluation processes. Specifically, this results in an inefficient replanning over the entire scene graph when encountering path obstructions blocking the original route. In contrast, SPADE prioritizes local layer planning coupled with local geometric scene navigation, enabling navigation through dynamic scenes while maintaining efficiency in computing a traversable route. We validate SPADE through extensive simulation experiments and real-world deployment on a quadrupedal robot, demonstrating its efficacy in handling complex and dynamic scenarios.
title SPADE: Towards Scalable Path Planning Architecture on Actionable Multi-Domain 3D Scene Graphs
topic Robotics
url https://arxiv.org/abs/2505.19098