Integration of acoustic compliance and noise mitigation in path planning for drones in human–robot collaborative environments

Adlakha, Revant; Liu, Wansong; Chowdhury, Souma; Zheng, Minghui; Nouh, M.

doi:10.1177/10775463221124049

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…Despite these applications, the sophisticated scenario accomplishment is premised on the drone's steady flight. The potential safety issue associated with an unsteady drone flight limits the reliability of the drone application, especially considering that some drone tasks in the warehouse, including moving, carrying, and monitoring, require close proximity between drones and human workers [6]. This potential issue increases the risk of physical injury to human workers.…”

Section: Introductionmentioning

confidence: 99%

An audio‐based risky flight detection framework for quadrotors

Liu,

Sajedi

et al. 2024

IET Cyber-Syst and Robotics

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Drones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network‐based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network‐based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well‐trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.

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Section: Introductionmentioning

confidence: 99%