Formation control of wheeled robots with vision-based position measurement

Abstract

Many applications require multiple mobile robots to move with a common velocity and at fixed relative distances. We present a time-invariant, state-feedback control law and a novel vision-based pose reconstruction system that allows one differential drive robot to follow another at a constant relative distance. The control law does not require measurement or estimation of the leader robot velocity and has tunable parameters that allows one to prioritize the error bounds of the desired states. The proposed pose reconstruction algorithm is computationally inexpensive and reliable. We present experimental results on two iRobot Create robots showing the performance of the controller and vision algorithm.

A Leader-Follower Formation Control of Multiple Mobile Robots by Position-Based Visual Servo Method using Fisheye Camera

“…The marker point P i f corresponding to point P i in the spherical model and the relationship can be expressed by the rotation matrix R and the translation vector T in the equation (7) as follows:…”

UAV Formation Control Under Communication Constraints Based on Distributed Model Predictive Control

“…Therefore, it is important to study vision-based formation control. In the past decade, there have been many vision-based formation control studies [15][16][17][18][19][20][21]. However, in [15][16][17][18] , the formation height variation was neglected and only the formation control in two-dimensional planes was studied, and the formation control for complex nonlinear models in three-dimensional space was not considered enough.…”

“…In the past decade, there have been many vision-based formation control studies [15][16][17][18][19][20][21]. However, in [15][16][17][18] , the formation height variation was neglected and only the formation control in two-dimensional planes was studied, and the formation control for complex nonlinear models in three-dimensional space was not considered enough. Although [19,20] consider the formation control problem in a 3D environment, formation control in a perturbed environment has not been sufficiently studied in [19,20].…”

Adaptive Image-Based Leader-Follower Approach of Mobile Robot with Omnidirectional Camera

“…Dani et al [2] estimated the relative velocity using a nonlinear estimator. Poonawala et al [3] eliminated the need of leader's velocity in the controller, so the design of the observer to estimate the leader's motion could be avoided. Wang et al [4] proposed an adaptive imagebased controller based on the backstepping technology.…”

“…The relative pose is measured by the on-board laser sensor, and the unknown leader's motion was estimated by a novel observer. Dani et al [2] and Poonawala et al [3] measured the relative pose by pose reconstruction using a perspective camera. Dani et al [2] estimated the relative velocity using a nonlinear estimator.…”