Distributed Geometric Optimization and Control of Rigid Differential-Drive Formations from Relative-Position Measurements
Distributed Geometric Optimization and Control of Rigid Differential-Drive Formations from Relative-Position Measurements
This paper proposes a two-layer estimation control scheme for differential-drive robot formations that regulate shape, position, and orientation using only relative position measurements and one absolute anchor. The method combines graph-proximal estimators for centroid reconstruction with distance gradient regulation and finite rotation to eliminate orientation drift. Validation is performed in ROS 2 Gazebo and with five physical robots.
The authors present a distributed control scheme for rigid differential-drive formations using only relative position measurements and one absolute anchor, validated in simulation and with five physical robots.
Source: arXiv Robotics — research abstracts · Read original article ↗
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ion, 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.
Open source S4
arXiv:2611.07163v1 Announce Type: cross 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 dir
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org