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Updated: Sep 19, 2025

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

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Published on: December 25, 2017

Geometrically-informed sequential Monte Carlo method for dynamic swarm tracking.

Tharani Rajapaksha¹, Amirali Khodadadian Gostar¹, Reza Hoseinnezhad¹

¹School of Engineering, RMIT University, Melbourne, Victoria, Australia.

ISA Transactions

|June 4, 2025

Summary

This summary is machine-generated.

This study presents a new method for tracking autonomous agent swarms using their geometric properties. The approach enhances robustness against false alarms and accurately estimates swarm dynamics and formations, outperforming traditional filters.

Keywords:

Likelihood function SMC implementation Swarm formation Swarm tracking Target tracking

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Area of Science:

Robotics and Autonomous Systems
Control Theory
Computer Vision

Background:

Dynamic tracking of autonomous agent swarms is crucial for coordinated operations.
Existing methods face challenges with sensor noise, false alarms, and dynamic formation changes.
Treating swarms as single targets simplifies tracking but requires robust state estimation.

Purpose of the Study:

To introduce a novel dynamic tracking approach for autonomous agent swarms.
To leverage swarm geometric properties for enhanced tracking accuracy and robustness.
To develop a method tolerant to high false alarm rates and dynamic formation changes.

Main Methods:

Utilizing the sequential Monte Carlo method for state estimation.
Characterizing swarm state by center location/velocity and geometric parameters.
Formulating a novel likelihood function incorporating swarm geometry and false alarm tolerance.

Main Results:

Accurate estimation of swarm movement, formation, and shape.
Demonstrated robustness against high false alarms and missed detections.
Outperformance of conventional particle filters in dynamic and challenging scenarios.

Conclusions:

The proposed geometric-based tracking method offers superior performance for autonomous swarms.
The approach effectively handles time-varying formations and sensor imperfections.
This work advances swarm tracking capabilities in complex operational environments.