AI & ML
Distributed Geometric Optimization and Control of Rigid Differential-Drive Formations from Relative-Position Measurements
arXiv cs.SY0 views
arXiv:2611.07163v1 Announce Type: new Abstract: Distance based formation control fixes the shape of a robot team but leaves its position and orientation unconstrained. We regulate all three for differential drive robots that have only one absolute position anchor, absolute headings, body frame relative position measurements, and communication with their neighbors. We derive a two layer estimation control scheme directly from optimization. The first half of the result is a set of graph-proximal estimators that reconstruct the centroid, the anchor vector, and the centered coordinates. Exact proximal updates reach consensus in finitely many steps, and a finite number of primal dual iterations leaves a bounded reconstruction error. The controller combines proximal centroid and orientation steps with distance gradient shape regulation, and a finite rotation removes the orientation drift caused by the shape step. A discrete Lyapunov analysis gives local exponential convergence once the estimators have converged. We validate the method in ROS 2 Gazebo and in experiments with five physical robots.
Source:https://arxiv.org/abs/2611.07163PreviousSampling Design and Confidence Tubes for Integral Curves on Riemannian ManifoldsNextState-Feedback Passivity Control for Synchronous Machine Systems
Related posts

Amazon will let you text Alexa your shopping list
0The Verge

TikTok’s For You page needs to change, says the EU
0The Verge

Stranger Things is getting a live-action spinoff called Other Worlds
0The Verge
The Cost of Self-Hosting Open-Source LLMs: GPUs, Staffing, and Operations
0dev.to
The central challenge of working with AI
0dev.to
Where an e-commerce explainer video belongs, and what to show in it
0dev.to