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A Novel Multivariate Skew-Normal Mixture Model and Its Application in Path-Planning for Very-Large-Scale Robotic Systems

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Pingping Zhu, Chang Liu, Peter Estephan

Multiagent Systems · Submitted 16 Feb 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods.

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arXiv ID2402.11091 (version 1)
Pith IDHUTBG2K7, Pith's permanent record of this paper and its review
CitationChang Liu, Peter Estephan, Pingping Zhu. "A Novel Multivariate Skew-Normal Mixture Model and Its Application in Path-Planning for Very-Large-Scale Robotic Systems." arXiv:2402.11091 (2024). https://pith.science/paper/HUTBG2K7
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 6,200 input tokens.

What the abstract itself states, as probabilities: claims first 0.12 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.80 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.96 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. VLSR systems consist of numerous cooperative agents or robots working together autonomously. Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represent agents' evolving spatial distribution, serving as a macroscopic view of the system's state. However, our recent research into VLSR systems has unveiled limitations in using GMMs to represent agent distributions, especially in cluttered environments. To overcome these limitations, we propose a novel model called the skew-normal mixture model (SNMM) for representing agent distributions. Additionally, we present a parameter learning algorithm designed to estimate the SNMM's parameters using sample data. Furthermore, we develop two SNMM-based path-planning algorithms to guide VLSR systems through complex and cluttered environments. Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. background 0.97 0.27 0.03 asserted 0.97
s1 VLSR systems consist of numerous cooperative agents or robots working together autonomously. background 1.00 0.07 0.02 asserted 1.00
s2 Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represent agents' evolving spatial distribution, serving as a macroscopic view of the system's state. background 1.00 0.06 0.02 asserted 0.98
s3 However, our recent research into VLSR systems has unveiled limitations in using GMMs to represent agent distributions, especially in cluttered environments. gap 0.90 0.91 0.04 asserted 1.00
s4 To overcome these limitations, we propose a novel model called the skew-normal mixture model (SNMM) for representing agent distributions. contribution 0.86 0.91 0.13 asserted 1.00
s5 Additionally, we present a parameter learning algorithm designed to estimate the SNMM's parameters using sample data. method 0.60 0.96 0.03 asserted 0.99
s6 Furthermore, we develop two SNMM-based path-planning algorithms to guide VLSR systems through complex and cluttered environments. contribution 0.78 0.95 0.08 asserted 0.99
s7 Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods. result 0.89 0.96 0.83 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper addresses the path-planning challenge for very large-scale robotic systems (VLSR) operating in complex and cluttered environments. VLSR systems consist of numerous cooperative agents or robots working together autonomously. Traditionally, many approaches for VLSR systems are developed based on Gaussian mixture models (GMMs), where the GMMs represent agents' evolving spatial distribution, serving as a macroscopic view of the system's state. However, our recent research into VLSR systems has unveiled limitations in using GMMs to represent agent distributions, especially in cluttered environments. To overcome these limitations, we propose a novel model called the skew-normal mixture model (SNMM) for representing agent distributions. Additionally, we present a parameter learning algorithm designed to estimate the SNMM's parameters using sample data. Furthermore, we develop two SNMM-based path-planning algorithms to guide VLSR systems through complex and cluttered environments. Our simulation results demonstrate the effectiveness and superiority of these algorithms compared to GMM-based path-planning methods.

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