Ground-Aware Octree-A* Hybrid Path Planning for Memory-Efficient 3D Navigation of Ground Vehicles

2025-09-05

AI Summary

Addressing the challenges of high memory consumption and the trade-off between optimality and real-time performance in 3D path planning for ground vehicles (e.g., UGVs, legged robots) operating in complex terrain, this paper proposes a hybrid planning framework integrating height-aware A* with an adaptive octree-based map representation. The method innovatively models traversable obstacles as navigational auxiliary nodes and introduces a height-penalized cost function to enhance terrain adaptability. By hierarchically compressing the 3D occupancy grid via an adaptive octree, the approach significantly reduces both memory footprint and computational overhead while preserving global path optimality. Experimental evaluation on representative complex terrains demonstrates a 62% reduction in memory usage and a 53% decrease in planning time, without compromising solution optimality—enabling efficient, real-time 3D navigation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Heuristic Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Agentic search

Abstract

In this paper, we propose a 3D path planning method that integrates the A* algorithm with the octree structure. Unmanned Ground Vehicles (UGVs) and legged robots have been extensively studied, enabling locomotion across a variety of terrains. Advances in mobility have enabled obstacles to be regarded not only as hindrances to be avoided, but also as navigational aids when beneficial. A modified 3D A* algorithm generates an optimal path by leveraging obstacles during the planning process. By incorporating a height-based penalty into the cost function, the algorithm enables the use of traversable obstacles to aid locomotion while avoiding those that are impassable, resulting in more efficient and realistic path generation. The octree-based 3D grid map achieves compression by merging high-resolution nodes into larger blocks, especially in obstacle-free or sparsely populated areas. This reduces the number of nodes explored by the A* algorithm, thereby improving computational efficiency and memory usage, and supporting real-time path planning in practical environments. Benchmark results demonstrate that the use of octree structure ensures an optimal path while significantly reducing memory usage and computation time.

Problem

• 3D path planning for ground vehicles and legged robots
• Memory-efficient navigation using octree-A* hybrid approach
• Optimal path generation with obstacle utilization and avoidance

Innovation

• Octree-A* hybrid for 3D path planning
• Height-penalty cost function for obstacle usage
• Octree compression reduces memory and computation