Hierarchical Binary-Tree-Based Coordination for Multi-Uncrewed-Aerial-Vehicle Traversal Through a Gap

This paper presents a hierarchical binary tree-based framework for multiple uncrewed aerial vehicles (UAVs) to pass through a gap in a three-dimensional environment. The proposed framework uses the gap entry point and the initial UAV positions as nodes to construct a hierarchical binary tree, which provides a deterministic route for the UAVs and facilitates the identification of potential prone-to-collision nodes. A path cost function is proposed, which accounts for the crossing angles at the prone-to-collision nodes and the sum of path lengths covered by all UAVs. A genetic algorithm coupled with a greedy algorithm is utilized to obtain the hierarchical binary tree structure by minimizing the proposed path cost. Further, an occupancy zone is defined at each prone-to-collision node, and a decentralized scheduling policy assigns conflict-free time intervals to the UAVs for their entry into and exit from the occupancy zone. Numerical simulations demonstrate the effectiveness of constructing hierarchical binary trees by employing the greedy-genetic algorithm approach and validate the proposed scheduling policy in various scenarios.

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Publication Details

Journal
Journal of Guidance Control and Dynamics
Published
2026-11-09
DOI
https://doi.org/10.2514/1.g009851
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Hierarchical Binary-Tree-Based Coordination for Multi-Uncrewed-Aerial-Vehicle Traversal Through a Gap

Ashwini Ratnoo, Gautam Kumar
Journal of Guidance Control and Dynamics
Robotic Path Planning Algorithms
article

Hierarchical Binary-Tree-Based Coordination for Multi-Uncrewed-Aerial-Vehicle Traversal Through a Gap

Ashwini Ratnoo, Gautam Kumar
article en

Abstract

This paper presents a hierarchical binary tree-based framework for multiple uncrewed aerial vehicles (UAVs) to pass through a gap in a three-dimensional environment. The proposed framework uses the gap entry point and the initial UAV positions as nodes to construct a hierarchical binary tree, which provides a deterministic route for the UAVs and facilitates the identification of potential prone-to-collision nodes. A path cost function is proposed, which accounts for the crossing angles at the prone-to-collision nodes and the sum of path lengths covered by all UAVs. A genetic algorithm coupled with a greedy algorithm is utilized to obtain the hierarchical binary tree structure by minimizing the proposed path cost. Further, an occupancy zone is defined at each prone-to-collision node, and a decentralized scheduling policy assigns conflict-free time intervals to the UAVs for their entry into and exit from the occupancy zone. Numerical simulations demonstrate the effectiveness of constructing hierarchical binary trees by employing the greedy-genetic algorithm approach and validate the proposed scheduling policy in various scenarios.

Journal of Guidance Control and Dynamics
Indian Institute of Science Bangalore (IN)
Robotic Path Planning Algorithms
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