ArticleEntropy (Basel, Switzerland)2020
Cross-Entropy as a Metric for the Robustness of Drone Swarms.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Explainable machine learning model of disorganisation in swarms of drones.Scientific reports · 2024Article
- Multivariate linear regression model based on cross-entropy for estimating disorganisation in drone formations.Scientific reports · 2023Article
- An Application of Inverse Reinforcement Learning to Estimate Interference in Drone Swarms.Entropy (Basel, Switzerland) · 2022Article
- Information and Statistical Measures in Classical vs. Quantum Condensed-Matter and Related Systems.Entropy (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that would allow for their correct assessment. The novelty of this paper lies in analysing, defining and applying the construct of cross-entropy, known from thermodynamics and information theory, to swarms. It can be used as a synthetic measure of the robustness of algorithms that can control swarms in the case of obstacles and unforeseen problems. Based on this, robustness may be an important aspect of the overall quality. This paper presents the necessary formalisation and applies it to a few examples, based on generalised unexpected behaviour and the results of collision avoidance algorithms used to react to obstacles.
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