Robust Vision-Based Tube Model Predictive Control of Multiple Mobile Robots for Leader–Follower Formation

Generally, vision-based controls use various camera sensors and require camera calibration, while the control performance would degrade due to inaccuracy calibration. Therefore, in this paper, the proposed controller only makes use of the image information from an un-calibrated perspective camera mounted at the follower robot without relative position measurement or any communication among the robots. First, the nominal visual formation kinematic model is developed using the camera models. Then it is redescribed as a quadratic programming (QP) with the specified constraints. A neurodynamic optimization based on primal-dual neural network is utilized to ensure the QP being converged to the exact optimal values. Through two-time-scale neuro-dynamical optimization, the gain scheduling of the ancillary state feedback can be realized so that the state variables are constrained within an invariant designed tube. The experiment results provide the verification for the effectiveness of the proposed approach.

[1]  Chun-Yi Su,et al.  Vision-Based Model Predictive Control for Steering of a Nonholonomic Mobile Robot , 2016, IEEE Transactions on Control Systems Technology.

[2]  Sung Jin Yoo,et al.  Brief paper: adaptive formation tracking control of electrically driven multiple mobile robots , 2010 .

[3]  Jun Wang,et al.  Robust Pole Assignment for Synthesizing Feedback Control Systems Using Recurrent Neural Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[4]  David Q. Mayne,et al.  Tube‐based robust nonlinear model predictive control , 2011 .

[5]  Renquan Lu,et al.  Trajectory-Tracking Control of Mobile Robot Systems Incorporating Neural-Dynamic Optimized Model Predictive Approach , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[6]  K. D. Do,et al.  Nonlinear formation control of unicycle-type mobile robots , 2007, Robotics Auton. Syst..

[7]  A.J. Calise,et al.  Approaches to vision-based formation control , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[8]  Philippe Martinet,et al.  Towards a reliable vision-based mobile robot formation control , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[9]  H. Sasaki,et al.  An application of interior point quadratic programming algorithm to power system optimization problems , 1995 .

[10]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[11]  David Q. Mayne,et al.  Robust model predictive control using tubes , 2004, Autom..

[12]  Ahmed Rahmani,et al.  Leader-follower formation control of nonholonomic mobile robots based on a bioinspired neurodynamic based approach , 2013, Robotics Auton. Syst..

[13]  Tingting Wang,et al.  Robust Online Model Predictive Control for a Constrained Image-Based Visual Servoing , 2016, IEEE Transactions on Industrial Electronics.

[14]  Jun Wang,et al.  A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Basil Kouvaritakis,et al.  Robust Tubes in Nonlinear Model Predictive Control , 2010, IEEE Transactions on Automatic Control.

[16]  Guoqiang Hu,et al.  Adaptive Vision-Based Leader–Follower Formation Control of Mobile Robots , 2017, IEEE Transactions on Industrial Electronics.

[17]  Tucker R. Balch,et al.  Behavior-based formation control for multirobot teams , 1998, IEEE Trans. Robotics Autom..

[18]  Baris Fidan,et al.  Single-View Distance-Estimation-Based Formation Control of Robotic Swarms , 2013, IEEE Transactions on Industrial Electronics.

[19]  C. Durazzi,et al.  Parallel Interior-Point Method for Linear and Quadratic Programs with Special Structure , 2001 .

[20]  Wei Ren,et al.  Distributed coordination architecture for multi-robot formation control , 2008, Robotics Auton. Syst..

[21]  K. Clements,et al.  An efficient interior point method for sequential quadratic programming based optimal power flow , 2000 .

[22]  Nathan Michael,et al.  Vision-Based Localization for Leader–Follower Formation Control , 2009, IEEE Transactions on Robotics.

[23]  Yingmin Jia,et al.  Adaptive leader-follower formation control of non-holonomic mobile robots using active vision , 2015 .

[24]  Wei Bian,et al.  Neural Network for Solving Constrained Convex Optimization Problems With Global Attractivity , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Yiguang Liu,et al.  A simple functional neural network for computing the largest and smallest eigenvalues and corresponding eigenvectors of a real symmetric matrix , 2005, Neurocomputing.

[26]  Tofael Ahamed,et al.  Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles , 2016, Sensors.

[27]  Maruthi R. Akella,et al.  Vision-based adaptive tracking control of uncertain robot manipulators , 2005, IEEE Transactions on Robotics.

[28]  Domenico Prattichizzo,et al.  Discussion of paper by , 2003 .

[29]  Nicholas R. Gans,et al.  Formation control of wheeled robots with vision-based position measurement , 2012, 2012 American Control Conference (ACC).

[30]  Nathan Michael,et al.  Vision-Based, Distributed Control Laws for Motion Coordination of Nonholonomic Robots , 2009, IEEE Transactions on Robotics.