Formation control for multi-robot systems开题报告

1. 研究目的与意义（文献综述包含参考文献）

1. Literature review Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Multi-Robot Systems (MRS) typically focus on fundamental technical aspects, like coordination and communication, that need to be considered in order to coordinate a team of robots to perform a given task effectively and efficiently. Formation control is one of the most challenging problems in cooperative multi-robot systems, which has attracted significant attention in the robot research community over the past decades. It is an important part in the cooperative area of multi-robot has always been the hottest research topics. There are many approaches have been applied for theoretical research and engineering applications, such as behavior-based, potential field-based, leader-follower, graph theory-based and virtual structure, etc. In general, the mobile robots formation can be described as controlling a group of mobile robots track a desired trajectory while maintaining a desired geometric shape including positions and orientations. Since the late 1980s researchers have been motivated to design and build teams of robots with the ability of working together on some given task. This motivation stems from the fact that in many applications, Multi-Robot Systems (MRS) brings about several advantages over Single Robot Systems. In particular, MRS are generally more time-efficient, less prone to single-points of failure, and typically exhibit multiple capabilities, which in many cases yield a more effective solution to a given problem. In early works, researchers observed natural systems, such as a swarm of bees, ants and even humans, to study how a group of individual entities can work together to perform a given task. The multidisciplinary nature of these early studies, eventually led to MRS being applied in several different application domains such as surveillance, search and rescue, foraging, exploration, cooperative manipulation and transportation of objects, among others. In the past few years, observer has been widely applied to the formation control [2][3][4]. For example, augmented fuzzy observer was put forward in [5] to implement synchronous estimation of the system state and the disturbance term. In[6], the integral action was incorporated into the observer controller to improve the formation tracking and observation performance. 1.1 Related work There are several types of formation control that have been studied in the field of formation control , for instance , in [10], the author studies distributed containment control for double-integrator dynamics in the presence of both stationary and dynamic leaders. The majority of literature review papers on Multi-Robot Systems (MRS) focus on classifying the most fundamental aspects of an MRS, such as coordination and communication. In [1] Farinelli et al. classify these MRS features into two dimensions. The first, termed the coordination dimension, deals with the different classes of cooperation schemes, such as whether the system is centralized or decentralized, strongly cooperative or weakly cooperative, among others. The system dimension classifies the existing types of communication schemes and team decomposition attributes. In [6], the authors developed the distributed controller based on a reduced-order state observer to track the high-dimensional leader robot. An adaptive observer-based sliding mode controller was applied in [7] to estimate the velocity from the position of noisy. In the existing results, there are few literatures to consider the sudden change of the given trajectory. At present, obstacle avoidance is a key problem to be ad-dressed urgently in multi-robot system. In practice, the robots must avoid colliding with other robots as well as with obstacles. At present, obstacle avoidance is a key problem to be ad-dressed urgently in multi-robot system. In practice, the robots must avoid colliding with other robots as well as with obstacles. As an example, in [9], the feedback control strategy that the leader completes obstacle avoidance whereas the followers ensure visibility maintenance of robots. 2. Introduction Formation control of multi-robot systems has received important attention in last decades due to its multiple potential applications in space-based interferometers, combat such as military, and surveillance, and reconnaissance systems, hazardous material handling, and distributed reconfigurable sensor networks. Formation control usually requires that individual vehicles share a consistent view of the objectives and the surrounded areas. For example, a multiple-robot rendezvous task requires that each vehicle know the rendezvous point. Control problems of robot have gained a lot of attention and developed rapidly owing to their extensive applied space. A number of great theoretical significance results have been reported. Formation control is an important issue in coordinated control for a group of unmanned autonomous vehicles/robots. In many applications, a group of autonomous vehicles are required to follow a predefined trajectory while maintaining a desired spatial pattern. Motivated by the above analysis, in this research , the formation forming maintain for the multi-robot/agent systems will be studied, in a crowded environment with obstacles . Through the design of a special formation control law, from the initial position to the targeted position with an aim of avoiding any collision with any obstacle founded in the path leading to the goal position and maintain the predefined formation.3. METHODOLOGY We present, evaluate and investigate the subject of multi-robot formation control by simulation and experimentally, robots may be utilized for a variety of scenarios.3.1 Leader-Follow Formation Control The leaderfollower formations are attractive in the coordination control of multi-robot systems. Partly, such formations benefit multiple robots because the formations can have guaranteed formation stability via control design [11]. The basic control idea of the leaderfollower mechanism is that multiple robots are divided into several leaderfollower pairs. In the leaderfollower mechanism, all follower robots share the same leader. In each pair, the leader robot moves along the predefined trajectory, while the follower robots track the leader with desired relative distance and angle. In the leaderfollower system of multiple robots, Figure 1 Leader-Follower formation model.Only partial followers can obtain the state of the leader, and the interaction between follower robots and leader robot is local [12]. Many control methods have been applied in the leaderfollower multi-robot systems, for example, Figure 1 show the triangle formation control, which consist of one leader. 3.2 SimulationsWe use simulations to quantitatively determine the performances of the multi-robot system, to verify the correct implementation of the packages and determine possible improvements. Simulations are performed using the ROS stages simulator. ROS (Robot Operation System) is installed on Ubuntu 16.04, ROS kinetic must be installed with gazebo simulator world to control the virtual turtlebot robots. Robot team consists of three robots.ROS was designed allowing primarily local inter-process communication managed by a master using the publish subscribe communication pattern. The master matches publishers and subscribers based on ROS topics, i.e., communication channels between processes. To implement a multi-robot system, in a first step, all robots would have to connect wirelessly to a single master, Only the master allows to establish direct communication channels between processes. Two processes need to communicate with the master to detect each other. If connections fail, processes rely on the master to reconnect them again. The solution having a single master per system has the drawback that, firstly, local inter-process communication is managed by an externally running master.Figure 2 Robots initial position Figure 3 Robots final positionSimulations are performed several times for the robot team, a global map of the simulation environment. The global map named (Empty wall). The map has dimensions of 26m by 15 m. square block have dimensions of 4m by 4:9 m. Robots starts in the initial position of the virtual world in the lower left corner of the square block facing to the right as well facing the wall as shown in Figure 2, the robots will be start to move to the goal position as in Figure 3, as the leader starts moving, followers will track their leader, while maintaining the distances between each other, avoiding obstacles and maintaining their formation. Simulation can be checked and reviewed online, as it was uploaded to http://www.rosject.io/l/10ff3d08/ .Rosject is an online ROS development studio; where you can upload your own ROS project and share it with others. 3.3 Future experiments workThe experiment will be in physical verification experiments in use of multi wheeled mobile robots, design formation control method; using three turtlebot robots in Nanjing Tech University lab and partly in environments built for demonstration. The turtlebot will be equipped with a sensor which will help them to navigate in indoor and outdoor environments, in addition, we will execute long running tests on laptop using ROS to test and control the turtlebot stability and expand the simulation work using MATLAB simulations (Simulink), to track the movement and calculate the error state. Figure 4 MATLAB ROS TURTLEBOT NetworkingReferences[1] Farinelli, L. Iocchi, D. Nardi, and A. Multirobot, Multirobot Systems: A Classification Focused on Coordination, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 34, no. 5,pp. 20152028, 2004.[2] A. Farinelli, L. Iocchi, D. Nardi, and A. Multirobot, Multirobot Systems: A Classification Focused on Coordination, IEEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 34, no. 5,pp. 20152028, 2004.[3] C. Yang, L. Y. Zhang, C. Y. Li, and M. Z. Q. Chen. "Observer-based consensus tracking of nonlinear agents in hybrid varying directed topology," IEEE Journals and Magazines., vol. 47, no. 8, pp. 2212-2222, 2017.[4] A. Ahmad, G. Lawless, and P. Lima. "An online scalable approach to unified multirobot cooperative localization and object tracking," IEEE Trans-actions on Robotics. vol. 33, no. 5, pp. 1184-1199, 2017.[5] Z. X. Zhong, Y. Z. Zhu, and C. K. Ahn, "Reachable set estimation for Takagi-Sugeno fuzzy systems against unknown output delays with application to tracking control of AUVs," ISA Transactions., vol. 78, pp. 31-38, 2018.[6] K. Shojaei. "Neural adaptive PID formation control of car-like mobile robots without velocity measurements," Advanced Robotics., vol. 31, no. 18, pp. 947-964, 2017.[7] G. H. Wen, T. W. Huang, W. W. Yu, Y. Q. Xia, and Z. W. Liu "Cooperative tracking of networked agents with a high-dimensional leader: qualitative analysis and performance evaluation, "IEEE Trans Cybern., vol. 48, no. 7, pp. 2060-2073, 2018.[8] R. R. Nair, and L. Behera. "Robust adaptive gain higher order sliding mode observer based control-constrained nonlinear model observer predictive control for spacecraft formation flying," IEEE/CAA Journal of Automatica Sinica., vol. 5, no. 1, pp. 367-381, 2018.[9] D. Panagou, and V. Kumar, "Cooperative visibility maintenance for leaderfollower formations in obstacle environments," IEEE Transactions on Robotics., vol. 30, no. 4, pp. 831-844, 2014.[10] Yongcan Cao, Student Member, IEEE, Daniel Stuart, Wei Ren, Member, IEEE, and Ziyang Meng "Distributed Containment Control for Multiple Autonomous Vehicles With Double-Integrator Dynamics: Algorithms and Experiments.[11] Li, J.; Sang, H.; Han, Y.; Wang, C.; Gao, K. Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. J. Clean. Prod. 2018, 181, 584598. [Google Scholar] [CrossRef].[12] Qian, D.W.; Tong, S.W.; Li, C.D. Leader-following formation control of multiple robots with uncertainties through sliding mode and nonlinear disturbance observer. ETRI J. 2016, 38, 10081018. [Google Scholar] [CrossRef].

2. 研究的基本内容、问题解决措施及方案

4. Pathways and Conclusion The concept of multi-agent systems, formation, obstacle avoidance. A multi wheeled mobile robots, design formation control method Simulation verification on MATLAB Simulink and ROS (Robot operation System) for multi-wheeled mobile robots; Physical verification experiment on the wheeled mobile robots. 4.1 ConclusionThis research proposal for a formation control strategy for the multi-robot/agents system. First, the leader robot follow the given path and the followers track the leader according to the formation control law , a complete formation method transformation. Moreover, a complete experiment work will be provided with simulation work to study the error rate and the precise of the formation.