An Infrastructure-less, Control-Independent Solution to Relative Localisation of a Team of Mobile Robots using Ranging Measurements

2026-06-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Robotics & Autonomous Systems, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new Multi-Hypothesis Bayesian-based Decentralised Cooperative Localisation (MHDCL) algorithm has been developed for teams of mobile robots, accepted for publication in the IEEE International Conference on Robotics and Automation (ICRA). This infrastructure-less and control-independent method enables relative localization without fixed anchors or requiring robot motion control for observability. It operates using only local odometry, sparse inter-agent ranging measurements (e.g., UWB-based), and short-range communication. The algorithm employs a multi-hypothesis Bayesian framework to maintain all feasible solutions, ensuring robustness in transient unobservable conditions and leveraging information sharing across partially connected networks. Experimental validation using LIMO differential-drive mobile robots equipped with DWM1001 UWB modules demonstrated its ability to reconstruct accurate estimates in challenging scenarios, including weakly observable, nearly collinear configurations and partially connected fleets of up to five agents.

Key takeaway

For robotics engineers designing multi-agent systems in unstructured or dynamic environments, this algorithm offers a robust localization solution. You can deploy robot fleets without fixed infrastructure or needing to constrain robot motion for observability. Consider implementing the Multi-Hypothesis Bayesian-based Decentralised Cooperative Localisation (MHDCL) algorithm to achieve reliable relative positioning, even with sparse UWB ranging measurements and partial network connectivity. This approach frees up control effort for task execution.

Key insights

The MHDCL algorithm enables robust, infrastructure-less multi-robot localization by maintaining multiple pose hypotheses without motion control.

Principles

• Observability can be embraced, not forced.
• Multi-hypothesis tracking enhances robustness.
• Decentralized information sharing improves estimates.

Method

MHDCL uses a particle filter, propagating motion and updating hypotheses with ranging measurements. Particles are clustered, and information is shared collaboratively, even in partially connected networks.

In practice

• Deploy on UWB-equipped mobile robots.
• Suitable for unstructured environments.
• Supports partially connected robot fleets.

Topics

• Multi-robot Localization
• Decentralized Algorithms
• Ultra-Wideband (UWB) Ranging
• Bayesian Filtering
• Mobile Robotics
• Observability

Best for: Robotics Engineer, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.