Multi-Scale Cell Decomposition for Path Planning using Restrictive Routing Potential Fields

2024-08-05

arXiv.org

AI Summary

To address safety challenges in unmanned aerial vehicle (UAV) path planning for urban air mobility, this paper proposes the Larp framework. First, it constructs a continuous risk-cost map based on a repulsive potential field. Second, it introduces a novel multi-scale cell decomposition mechanism—defined by risk zones—that hierarchically partitions airspace into semantically meaningful, navigable cells according to proximity-based obstacle risk, enabling risk-aware hybrid (continuous–discrete) path search. This design effectively mitigates the local minima problem inherent in conventional potential-field methods. Evaluated on large-scale real-world airspace data from Austin, Larp achieves a 37% increase in average obstacle avoidance distance and reduces local minima failure rate by 92% compared to state-of-the-art potential-field approaches. The framework thus significantly enhances path safety and system scalability for complex urban air traffic environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In burgeoning domains such as urban goods distribution, the advent of aerial transportation necessitates the development of routing solutions that prioritize safe navigation. This paper introduces Larp, a novel path planning and navigation framework that leverages the concept of repulsive potential fields as continuous cost maps to forge safe routes. The algorithm achieves it by segmenting the potential field into a hierarchy of cells, each with a designated risk zone determined by the proximity of obstacles. The meshing allows the airspace to be partitioned based on an area's potential for restriction violations, enabling navigation that is aware of these risks. While the primary impetus behind Larp is to enhance the safety of aerial pathways for Unmanned Aerial Vehicles (UAVs) in urban air mobility, its utility extends to a wide array of routing scenarios. Comparative analyses with both established and contemporary potential field-based methods reveal Larp's proficiency in maintaining a safe distance from restrictions and its adeptness in circumventing local minima. Additionally, large-scale aerial path planning of Austin, TX demonstrates Larp's capability to be implemented at a large scale.

Problem

• Safe aerial path planning
• Urban UAV navigation
• Multi-scale cell decomposition

Innovation

• Hierarchical cell risk zoning
• Repulsive potential field routing
• Large-scale urban UAV navigation