ASHC: Quantum-Inspired Hierarchical Clustering for Priority-Aware Coverage Path Planning

Venkata Siva Kumar Margapuri, Garik Kazanjian
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:1169-1174, 2026.

Abstract

Coverage Path Planning (CPP) is a fundamental challenge in robotics, where the goal is to compute paths that ensure complete traversal of an environment. While classical CPP approaches perform well in structured or small-scale settings, they often struggle with scalability and lack mechanisms to adapt to context in large, complex environments. This study proposes Amplitude Structured Hierarchical Clustering, a quantum-inspired hierarchical CPP framework that integrates amplitude-based contextual encoding into the CPP pipeline. The proposed method constructs an amplitude field inspired by quantum walk dynamics to represent spatial variation across the environment, enabling the principled decomposition of coverage targets into semantically coherent clusters to guide both intra-cluster and inter-cluster traversals. Through formal analysis and experiments across varied environments, this study demonstrates the feasibility and robustness of the approach. These results position ASHC as a promising direction in quantum-inspired planning for robotic coverage tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-margapuri26a, title = {ASHC: Quantum-Inspired Hierarchical Clustering for Priority-Aware Coverage Path Planning}, author = {Margapuri, Venkata Siva Kumar and Kazanjian, Garik}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {1169--1174}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/margapuri26a/margapuri26a.pdf}, url = {https://proceedings.mlr.press/v318/margapuri26a.html}, abstract = {Coverage Path Planning (CPP) is a fundamental challenge in robotics, where the goal is to compute paths that ensure complete traversal of an environment. While classical CPP approaches perform well in structured or small-scale settings, they often struggle with scalability and lack mechanisms to adapt to context in large, complex environments. This study proposes Amplitude Structured Hierarchical Clustering, a quantum-inspired hierarchical CPP framework that integrates amplitude-based contextual encoding into the CPP pipeline. The proposed method constructs an amplitude field inspired by quantum walk dynamics to represent spatial variation across the environment, enabling the principled decomposition of coverage targets into semantically coherent clusters to guide both intra-cluster and inter-cluster traversals. Through formal analysis and experiments across varied environments, this study demonstrates the feasibility and robustness of the approach. These results position ASHC as a promising direction in quantum-inspired planning for robotic coverage tasks.} }
Endnote
%0 Conference Paper %T ASHC: Quantum-Inspired Hierarchical Clustering for Priority-Aware Coverage Path Planning %A Venkata Siva Kumar Margapuri %A Garik Kazanjian %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-margapuri26a %I PMLR %P 1169--1174 %U https://proceedings.mlr.press/v318/margapuri26a.html %V 318 %X Coverage Path Planning (CPP) is a fundamental challenge in robotics, where the goal is to compute paths that ensure complete traversal of an environment. While classical CPP approaches perform well in structured or small-scale settings, they often struggle with scalability and lack mechanisms to adapt to context in large, complex environments. This study proposes Amplitude Structured Hierarchical Clustering, a quantum-inspired hierarchical CPP framework that integrates amplitude-based contextual encoding into the CPP pipeline. The proposed method constructs an amplitude field inspired by quantum walk dynamics to represent spatial variation across the environment, enabling the principled decomposition of coverage targets into semantically coherent clusters to guide both intra-cluster and inter-cluster traversals. Through formal analysis and experiments across varied environments, this study demonstrates the feasibility and robustness of the approach. These results position ASHC as a promising direction in quantum-inspired planning for robotic coverage tasks.
APA
Margapuri, V.S.K. & Kazanjian, G.. (2026). ASHC: Quantum-Inspired Hierarchical Clustering for Priority-Aware Coverage Path Planning. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:1169-1174 Available from https://proceedings.mlr.press/v318/margapuri26a.html.

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