Fully distributed and resilient source seeking for robot swarms
🤖 AI Summary
This paper addresses the fully distributed source localization problem for unknown 3D scalar field peaks under challenges including absence of centralized control, robot failures, network partitioning, and uncertain agent positions. We propose a gradient-free distributed ascent direction estimation method: each robot estimates barycentric coordinates and a synthesized ascent direction in real time via local communication and high-frequency consensus, then autonomously tracks the estimated direction at constant speed. Theoretically, the algorithm achieves exponential convergence with arbitrarily controllable localization accuracy; maintains functional autonomy despite subnet disconnection or node failure; and—novelly—establishes a cluster morphology optimality theory enabling environment-adaptive shape deformation for navigation. Experiments demonstrate robust convergence to the source under discrete/continuous field distributions and dynamic network topologies.
Application Category
📝 Abstract
We propose a self-contained, resilient and fully distributed solution for locating the maximum of an unknown 3D scalar field using a swarm of robots that travel at constant speeds. Unlike conventional reactive methods relying on gradient information, our methodology enables the swarm to determine an ascending direction so that it approaches the source with arbitrary precision. Our source-seeking solution consists of three algorithms. The first two algorithms run sequentially and distributively at a high frequency providing barycentric coordinates and the ascending direction respectively to the individual robots. The third algorithm is the individual control law for a robot to track the estimated ascending direction. We show that the two algorithms with higher frequency have an exponential convergence to their eventual values since they are based on the standard consensus protocol for first-order dynamical systems; their high frequency depends on how fast the robots travel through the scalar field. The robots are not constrained to any particular geometric formation, and we study both discrete and continuous distributions of robots within swarm shapes. The shape analysis reveals the resiliency of our approach as expected in robot swarms, i.e., by amassing robots we ensure the source-seeking functionality in the event of missing or misplaced individuals or even if the robot network splits into two or more disconnected subnetworks. In addition, we also enhance the robustness of the algorithm by presenting conditions for emph{optimal} swarm shapes, in the sense that the ascending directions can be closely parallel to the field's gradient. We exploit such an analysis so that the swarm can adapt to unknown environments by morphing its shape and maneuvering while still following an ascending direction.
Problem
Research questions and friction points this paper is trying to address.
Locate maximum of unknown scalar field using robot swarm
Determine ascending direction without gradient information
Ensure resiliency with adaptive swarm shape and maneuvering
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fully distributed swarm source-seeking solution
Slow-fast closed-loop with three algorithms
Resilient to individual failures and splits
🔎 Similar Papers
No similar papers found.
