Concurrent-Learning Based Relative Localization in Shape Formation of Robot Swarms

2024-10-08

arXiv.org

AI Summary

Autonomous formation control of large-scale robot swarms without external positioning signals remains challenging due to limited onboard localization observability and scalability constraints. Method: This paper proposes a fully onboard sensor-driven, distributed shape formation framework comprising: (1) a concurrent-learning-based relative position estimator that relaxes persistent excitation requirements and enhances localization observability; (2) a finite-time anchor consensus protocol enabling rapid, distributed alignment of the shape reference frame; and (3) a closed-loop control architecture tightly integrating relative state estimation, behavior-driven control, and cooperative localization. Results: Comprehensive simulations and outdoor real-robot experiments demonstrate superior performance over state-of-the-art approaches in localization accuracy, formation robustness under disturbances, and scalability to swarms exceeding one hundred agents.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

In this paper, we address the shape formation problem for massive robot swarms in environments where external localization systems are unavailable. Achieving this task effectively with solely onboard measurements is still scarcely explored and faces some practical challenges. To solve this challenging problem, we propose the following novel results. Firstly, to estimate the relative positions among neighboring robots, a concurrent-learning based estimator is proposed. It relaxes the persistent excitation condition required in the classical ones such as least-square estimator. Secondly, we introduce a finite-time agreement protocol to determine the shape location. This is achieved by estimating the relative position between each robot and a randomly assigned seed robot. The initial position of the seed one marks the shape location. Thirdly, based on the theoretical results of the relative localization, a novel behavior-based control strategy is devised. This strategy not only enables adaptive shape formation of large group of robots but also enhances the observability of inter-robot relative localization. Numerical simulation results are provided to verify the performance of our proposed strategy compared to the state-of-the-art ones. Additionally, outdoor experiments on real robots further demonstrate the practical effectiveness and robustness of our methods.

Problem

• Robotics
• Formation Control
• Sensor-based Navigation

Innovation

• Shape Formation
• Relative Localization
• Decentralized Control