Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
This paper introduces the 'Bionic Swarm,' a human-in-the-loop system that lowers barriers to real-world validation of swarm robotics. It uses a smartphone web-app, Bluetooth sensors, and a centralized server to direct human users. The Score-Biased-Search algorithm for soil mapping demonstrates superlinear map reconstruction in both simulations and outdoor experiments.
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Key points
- Bionic Swarm system reduces hardware cost and development time by delegating difficult tasks to humans.
- Score-Biased-Search algorithm assigns scores to map locations for efficient soil mapping.
- Real-world outdoor validation shows superlinear map reconstruction performance.
Why it matters
This matters because bionic Swarm system reduces hardware cost and development time by delegating difficult tasks to humans.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.29091] Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
[Submitted on 27 May 2026]
Title:Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping
View a PDF of the paper titled Human-in-the-Loop Swarms: A Bionic Swarm Approach to Real-World Soil Mapping, by Petras Swissler and 5 other authors
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Abstract:Swarm and field robotics face significant barriers to real-world validation due to the high cost and development time to deploy hardware. This paper introduces the `Bionic Swarm,'' a novel system that lowers these barriers by abstracting away many of the tasks that are difficult to implement on robots but which do not contribute to the overall algorithm evaluation, giving these tasks to human users. These human users take directions from a smartphone web-app that takes measurements from Bluetooth-connected sensors and relays them to a centralized server. This server runs the swarm algorithm and directs actions to the human users. We evaluate this system through the experimental validation of a geotechnically-focused search algorithm named Score-Biased-Search, which functions by assigning a `score'' to each location on a reconstructed map, then biases search patterns through areas of higher expected scores, and which exhibits superlinear map reconstruction relative to the number of search agents. After presenting simulation results for the algorithm, we then apply the algorithm on the Bionic Swarm platform to validate its function in a real-world, outdoor setting. This work demonstrates that this human-in-the-loop approach significantly lowers the barrier to entry for field and swarm robotics research.
Comments: 27 pages, 15 figures. Submitted to Advanced Intelligent Systems
Subjects:
Robotics (cs.RO); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.29091 [cs.RO]
(or arXiv:2605.29091v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.29091
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Petras Swissler [view email] [v1] Wed, 27 May 2026 20:50:05 UTC (19,242 KB)
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