Neural state estimation and optimization synthesis of flocking control for mobile robots swarm under uncertainty
TL;DR
The authors propose a method that integrates a learned state estimator based on a fully connected neural network, approximating the particle filter with simultaneous uncertainty evaluation, and an optimization regulator with a control barrier function and a control-Lyapunov function that ensures accuracy comparable to traditional filtration techniques at a computational cost one to two orders of magnitude lower.
Abstract
The paper addresses the flocking control problem for groups of mobile robots collectively moving in a three-dimensional environment with obstacles under limited communication between agents. The research relevance lies in the need for swarm coordination given intermittent interaction between an agent and those inside its communication range, i.e. neighbors. In this article, the goal is to create a flocking controller that adheres to collective motion principles while maintaining safety in conditions of limited visibility. The authors propose a method that integrates a learned state estimator based on a fully connected neural network, approximating the particle filter with simultaneous uncertainty evaluation, and an optimization regulator with a control barrier function and a control-Lyapunov function. The simulation reveals that the neural network estimator ensures accuracy comparable to traditional filtration techniques at a computational cost one to two orders of magnitude lower. In addition, the weighted centroid, which considers estimation reliability, reduces the possibility of coordination errors. Results can be applied in designing multi-robot group control systems.