A Range-Based Algorithm for Autonomous Navigation of an Aerial Drone to Approach and Follow a Herd of Cattle.

Manaram Gnanasekera, Jay Katupitiya, Andrey V Savkin, A H T Eranga De Silva

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

TUBER: Time-aware UAV-based energy-efficient reconfigurable routing scheme for smart wireless livestock sensor network.PloS one · 2024
Article
UBER: UAV-Based Energy-Efficient Reconfigurable Routing Scheme for Smart Wireless Livestock Sensor Network.Sensors (Basel, Switzerland) · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Manaram GnanasekeraSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.

Jay KatupitiyaSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.

Andrey V SavkinSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.ORCID 0000-0001-9390-6634

A H T Eranga De SilvaSchool of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, NSW 2052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes an algorithm that will allow an autonomous aerial drone to approach and follow a steady or moving herd of cattle using only range measurements. The algorithm is also insensitive to the complexity of the herd's movement and the measurement noise. Once arrived at the herd of cattle, the aerial drone can follow it to a desired destination. The primary motivation for the development of this algorithm is to use simple, inexpensive and robust sensing hence range sensors. The algorithm does not depend on the accuracy of the range measurements, rather the rate of change of range measurements. The proposed method is based on sliding mode control which provides robustness. A mathematical analysis, simulations and experimental results with a real aerial drone are presented to demonstrate the effectiveness of the proposed method.

Indexed as

AlgorithmsNoiseAnimalsCattleaerial dronesanimal farmingautonomous navigationherdingrange-based guidancerange only measurementssliding mode controlUAVsunmanned aerial vehicles

Identifiers

PMID34770525

DOI10.3390/s21217218

PMCPMC8588052

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.