A Framework for Multi-Task Formation Control of Nonholonomic Robotic Systems

2008 ◽  
Author(s):  
Junjie Zhang ◽  
Jingang Yi ◽  
Suhada Jayasuriya
Keyword(s):  
Feedback Linearization ◽  
Formation Control ◽  
Null Space ◽  
Lower Layer ◽  
Hierarchical Architecture ◽  
Practical Applications ◽  
Control Scheme ◽  
Robot Systems ◽  
Asymptotic Tracking ◽  
Reference Trajectories

In practical applications, multi-robot systems may have to simultaneously deal with several tasks: collision-free maneuvers in dynamic environments; tracking certain desired trajectories; forming suitable patterns or geometrical shapes, and/or varying the pattern when necessary. The proposed formation control scheme in this paper addresses these issues. First a dynamic model for a nonholonomic robot prototype is developed. Tracking control is then realized by employing input-output feedback linearization. To achieve typical complex formation missions, a two-layer hierarchical architecture is proposed. At the upper layer, a null-space method is utilized to prioritize the tasks of the robot team and to generate reference trajectories for formation control. In the lower layer, the control scheme for each individual robot guarantees asymptotic tracking of the desired trajectories. Numerical simulations of a realistic case study illustrate the effectiveness of the proposed framework.

scholarly journals Nonlinear Predictive Control With End Point Constraints

2006 ◽  
Vol 3 (1) ◽  
pp. 69
Author(s):  
R. Hedjar
Keyword(s):  
Predictive Control ◽  
Reference Signal ◽  
Control Law ◽  
Tracking Errors ◽  
End Point ◽  
Nonlinear Predictive Control ◽  
Control Scheme ◽  
Time Minimization ◽  
Asymptotic Tracking ◽  
Reference Trajectories

The optimal nonlinear predictive control structure with end point constraints is presented, which provides asymptotic tracking of smooth reference trajectories. The controller is based on a finite horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform an online optimization, and asymptotic tracking of smooth reference signal is guaranteed. The proposed control scheme is applied to planning motions problem of a mobile robot. Simulations results are performed to validate the tracking performance of the proposed controller. 

scholarly journals The NSB control: a behavior-based approach for multi-robot systems

2010 ◽  
Vol 1 (1) ◽  
Author(s):  
Gianluca Antonelli ◽  
Filippo Arrichiello ◽  
Stefano Chiaverini
Keyword(s):  
Formation Control ◽  
Ad Hoc ◽  
Null Space ◽  
Robotic Systems ◽  
Lower Priority ◽  
Robot Systems ◽  
Mobile Ad Hoc ◽  
Behavior Based ◽  
Hoc Networks ◽  
Multi Robot

AbstractThe paper presents an overview on the use of a behavior-based approach, namely the Null-Space-based Behavioral (NSB) approach, to control multi-robot systems in a wide application domain. The NSB approach has been recently developed to control the motion of generic robotic systems; it uses a projection mechanism to combine the multiple, prioritized, behaviors that compose the robotic mission so that the lower priority behaviors do not effect the higher priority ones. In this paper we describe how the NSB approach has been used to control different multi-robot systems (e.g., composed of wheeled and marine robots) to achieve missions such as formation control, entrapping/escorting of targets, control of mobile ad-hoc networks, flocking, border patrol and cooperative caging.

On Discrete-Time Feedforward Asymptotic Tracking Controllers

1991 ◽  
Vol 113 (3) ◽  
pp. 509-514
Author(s):  
H. A. Pak ◽  
Rowmau Shieh
Keyword(s):  
Discrete Time ◽  
Control Algorithm ◽  
Reference Trajectory ◽  
Time Control ◽  
Numerical Noise ◽  
Control Scheme ◽  
Tracking Controllers ◽  
Tightly Coupled ◽  
Asymptotic Tracking ◽  
Reference Trajectories

A feedforward design formulation for asymptotic tracking controllers is presented for linear multivariable systems under discrete-time control. The control scheme is based upon the use of future set points along reference trajectories with known models. One advantage of the control scheme is that it does not require the derivative states of the reference trajectory explicitly during its operation. As a result, apart from numerical noise reduction, the autonomous trajectory generation algorithms need not be tightly coupled to the control algorithm for purpose of real time execution.

scholarly journals Reinforcement-Learning-Based Asynchronous Formation Control Scheme for Multiple Unmanned Surface Vehicles

2021 ◽  
Vol 11 (2) ◽  
pp. 546
Author(s):  
Jiajia Xie ◽  
Rui Zhou ◽  
Yuan Liu ◽  
Jun Luo ◽  
Shaorong Xie ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Formation Control ◽  
Rapid Development ◽  
Gradient Algorithm ◽  
Robot System ◽  
Physical Relationship ◽  
Unmanned Surface Vehicles ◽  
Main Challenge ◽  
Control Scheme ◽  
Multi Robot

The high performance and efficiency of multiple unmanned surface vehicles (multi-USV) promote the further civilian and military applications of coordinated USV. As the basis of multiple USVs’ cooperative work, considerable attention has been spent on developing the decentralized formation control of the USV swarm. Formation control of multiple USV belongs to the geometric problems of a multi-robot system. The main challenge is the way to generate and maintain the formation of a multi-robot system. The rapid development of reinforcement learning provides us with a new solution to deal with these problems. In this paper, we introduce a decentralized structure of the multi-USV system and employ reinforcement learning to deal with the formation control of a multi-USV system in a leader–follower topology. Therefore, we propose an asynchronous decentralized formation control scheme based on reinforcement learning for multiple USVs. First, a simplified USV model is established. Simultaneously, the formation shape model is built to provide formation parameters and to describe the physical relationship between USVs. Second, the advantage deep deterministic policy gradient algorithm (ADDPG) is proposed. Third, formation generation policies and formation maintenance policies based on the ADDPG are proposed to form and maintain the given geometry structure of the team of USVs during movement. Moreover, three new reward functions are designed and utilized to promote policy learning. Finally, various experiments are conducted to validate the performance of the proposed formation control scheme. Simulation results and contrast experiments demonstrate the efficiency and stability of the formation control scheme.

scholarly journals Research Progress on Synergistic Technologies of Agricultural Multi-Robots

2021 ◽  
Vol 11 (4) ◽  
pp. 1448
Author(s):  
Wenju Mao ◽  
Zhijie Liu ◽  
Heng Liu ◽  
Fuzeng Yang ◽  
Meirong Wang
Keyword(s):  
Path Planning ◽  
Formation Control ◽  
Research Progress ◽  
Hybrid Architecture ◽  
Labor Costs ◽  
Robot System ◽  
System Architectures ◽  
Research Results ◽  
Robot Systems ◽  
Multi Robot

Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.

A Robust Control Design Approach Combining Exact Linearization and High-Gain PI-Observer

2007 ◽  
Author(s):  
Yan Liu ◽  
Dirk So¨ffker
Keyword(s):  
Feedback Linearization ◽  
Control Method ◽  
High Gain ◽  
Observer Design ◽  
Input State ◽  
Exact Linearization ◽  
Practical Applications ◽  
Robust Control Design ◽  
Nonlinear Control Method ◽  
Feedback Linearization Approach

This paper introduces a robust nonlinear control method combining classical feedback linearization and a high-gain PI-Observer (Proportional-Integral Observer) approach that can be applied to control a nonlinear single-input system with uncertainties or unknown effects. It is known that the lack of robustness of the feedback linearization approach limits its practical applications. The presented approach improves the robustness properties and extends the application area of the feedback linearization control. The approach is developed analytically and fully illustrated. An example which uses input-state linearization and PI-Observer design is given to illustrate the idea and to demonstrate the advantages.

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