Decentralized and Fully Onboard: Range-Aided Cooperative Localization and Navigation on Micro Aerial Vehicles

2026-02-18

AI Summary

This work addresses the challenge of achieving high-precision cooperative localization and formation control among multiple micro aerial vehicles (MAVs) in scenarios lacking global positioning and a central coordinator. The authors propose a fully decentralized cooperative navigation framework that fuses onboard odometry with inter-robot range measurements, enabling each MAV to independently estimate relative poses and compute its own control inputs. A novel block coordinate descent localization method is introduced, which operates without strict clock synchronization, and formation control is formulated as a factor graph inference problem that explicitly accounts for state estimation uncertainty—eliminating reliance on predefined trajectories. Experimental results demonstrate that the system achieves decimeter-level accuracy in both localization and formation maintenance across diverse indoor and outdoor environments, confirming its effectiveness and robustness.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Controlling a team of robots in a coordinated manner is challenging because centralized approaches (where all computation is performed on a central machine) scale poorly, and globally referenced external localization systems may not always be available. In this work, we consider the problem of range-aided decentralized localization and formation control. In such a setting, each robot estimates its relative pose by combining data only from onboard odometry sensors and distance measurements to other robots in the team. Additionally, each robot calculates the control inputs necessary to collaboratively navigate an environment to accomplish a specific task, for example, moving in a desired formation while monitoring an area. We present a block coordinate descent approach to localization that does not require strict coordination between the robots. We present a novel formulation for formation control as inference on factor graphs that takes into account the state estimation uncertainty and can be solved efficiently. Our approach to range-aided localization and formation-based navigation is completely decentralized, does not require specialized trajectories to maintain formation, and achieves decimeter-level positioning and formation control accuracy. We demonstrate our approach through multiple real experiments involving formation flights in diverse indoor and outdoor environments.

Problem

• decentralized localization
• cooperative navigation
• formation control
• range-aided
• micro aerial vehicles

Innovation

• decentralized localization
• range-aided
• formation control
• factor graphs
• micro aerial vehicles