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Emerging Inter-Swarm Collaboration for Surveillance Using Pheromones and Evolutionary Techniques.

Daniel H Stolfi¹, Matthias R Brust¹, Grégoire Danoy^1,2

¹SnT, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg.

Sensors (Basel, Switzerland)

|May 6, 2020

Summary

A new mobility model, Attractor Based Inter-Swarm collaborationS (ABISS), enhances surveillance using diverse autonomous vehicles. This collaborative approach improves coverage in restricted areas by up to 11%.

Keywords:

bio-inspiration evolutionary algorithm inter-swarm collaboration mobility model pheromones swarm robotics unmanned aerial vehicle unmanned ground vehicle

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Area of Science:

Robotics and Autonomous Systems
Artificial Intelligence
Operations Research

Background:

Effective surveillance of restricted areas is crucial.
Current autonomous vehicle (AV) strategies often lack inter-swarm collaboration for complex terrains.
Unpredictable trajectories are needed for thorough exploration.

Purpose of the Study:

To introduce the Attractor Based Inter-Swarm collaborationS (ABISS) mobility model.
To enhance the surveillance capabilities of unmanned autonomous vehicles in restricted areas.
To demonstrate the effectiveness of inter-swarm collaboration.

Main Methods:

Utilizing diverse vehicle types (e.g., ground, aerial) with chaotic trajectories.
Implementing an evolutionary algorithm for parameterizing and configuring collaborative strategies.
Simulating and comparing the ABISS model against non-collaborative approaches.

Main Results:

Collaboration between different vehicle swarms is demonstrated as feasible.
The ABISS model achieved up to an 11% improvement in total covered area.
Collaborative configurations emerged naturally from the evolutionary algorithm.

Conclusions:

The ABISS model significantly improves surveillance coverage through inter-swarm collaboration.
Evolutionary algorithms are effective in optimizing collaborative autonomous vehicle strategies.
ABISS offers a promising solution for complex area surveillance missions.