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一种实现FPGA机器人集合行为控制的新方法钦奈耶1，P帕德玛1，萨提亚萨维特里2，P拉杰库马尔31帕德马斯里博士B V拉朱理工学院，纳萨普尔，梅达克，A.P，印度2教授，欧洲经委会部，尼赫鲁科技大学工程学院，尼赫鲁科技大学海得拉巴，印度.3教授与部门主任，欧洲经委会部，PVPSIT，维亚亚瓦达，印度.摘要：在多智能体机器人路径规划的背景下，行为控制表现出其重要性。本文提出了两个移动机器人行为控制关键元件的新方案和硬件设计。在两个机器人中，一个被认为是领导者，另一个被认为是追随者。领队（红外导引头）根据目标（红外信标）发射的强度，规划路径从S开始，到达目标（G）。每一瞬间，它都会检查跟随者的行为，并根据其位置采取适当的行动。追随者只是跟随领导者，一路检查领导者的行为。移动机器人的设计采用了超声波传感器、FPGA和步进电机。该控制单元设计用于实现会合和引导跟随方法的自主导航。该设计采用verilog进行开发，并在斯巴达6型FPGA机器人上实现。仿真结果表明，实现成功。这种设计在工业环境中非常有用。关键词：超声波传感器；FPGA；机器人；行为控制；VLSI架构1. 介绍我们可以通过使用机器人实现自动化和使我们的生活更轻松。为了实现更好的营生，机器人的发展采用了动态并行处理算法，同样也受到了工业工作场所的影响。在这方面，机器人主要依靠导航。为了完成任务，机器人必须在考虑给定环境后进行导航。不同类型的机器人被用于工业和其他应用，如认知轮椅被用来在室内环境帮助人类。机器人时代从多机器人系统开始，以增加对上述应用的帮助。多机器人的导航依赖于三个概念(1)映射(即路径规划)。(2)行为控制(3)决策。第一个概念使用来自传感器的信息创建环境地图。第二种是将地图与传感器信息联系起来，让机器人在环境中进行自定位。第三个概念考虑路径规划问题1。时间限制和资源限制对于任何机器人应用都是非常重要的因素。本文提出了一种移动机器人的行为控制方法，它可以在工业环境中辅助完成各种任务。基于先导-跟随方法的协调控制方案 23，即使在危险环境中，多智能体机器人也可以进行不同的操作，如避障、遥控和自动搜索等4。自组织多智能体系统解决了集中规划算法与智能体自治之间的冲突5。使用多个机器人对其进行路径规划是困难的，因为它结合了两个不同的方面。该目标可以执行两种不同类型的运动，即加速运动和非加速运动7。交会机器人的问题是协同探索的关键步骤，也是相对运动学方程812中的一个问题。这种交会概念涉及与传感器通信、自主导航和路径规划。9-11在机器人算法的实现中，需要低功耗和可重构特性的并行处理支持。因此，许多人更喜欢FPGA机器人。利用硬件效率方案13-16可以实现不同的路径，通过使用地标17和基于强度的方法18的不同方法来表示目标，这项研究需要更多的输入输出端口，并且需要并行，因此选择了FPGA。本文组织结构如下：第2节用算法步骤描述了本文的主要思想；本文的下一个层次是在第3节中提到行为控制处理和流程图；第4节讨论了上述算法的VLSI体系结构；第5节用框图表示机器人的设计；第6节给出了本文的研究结果，并给出了仿真和实现结果；最后给出了结论。2.主要思想本文提出了采用基于传感器的静态环境结构的设计思想。工业中的机器人使员工的工作量大大降低。单个机器人可以完成许多工作，但对于大型工作，多个机器人工作被认为是高精度的。多个机器人需要它们之间的通信来完成任务，这种通信可以通过使用简单的传感器来完成。这些机器人是在实验室里为执行1)会合，和2)领袖跟随者方法论而在当地开发的。该算法的基本思想是实现两个具有行为控制的机器人的自主导航。机器人从节点开始，通过传感器进行相互通信。其中一个机器人被认为是主人或领导者，它进行路径规划以达到目标（G）。领导者利用红外导引头建立导引头，目标是红外信标19建成的塔。跟随者在这两种情况下都起作用，作为与领导者的集合点（并行），并使用领导者-跟随者方法跟随领导者。2.1 算法步骤：第一步：机器人用传感器信息估计定位。第二步：领导或主机器人检查跟随者或从者的行为，无论是在集合还是跟随模式。第三步：从S开始，然后根据目标的强度信号（红外信标）进行路径规划。第四步：主机器人全程检查跟随者的行为，即使在避障条件下也要采取适当的行动来达到目标。备注1：跟随者还检查主机器人在环境中的行为，无论是在集合模式还是领导者跟随模式，就像主机器人在步骤2中执行的那样。备注2：避障决策是由两个机器人根据各自的位置来决定其行为。机器人路径规划是基于它们的行为进行的。3. 移动机器人之间的行为控制3.1跟随领航者（领导随从）：图1 领导者-跟随者路径规划环境两个机器人一个接一个地移动，领队机器人对路径规划做出决定。跟随机器人必须跟随领头机器人。当引导机器人检测到障碍物时，需要使用所需的机构来克服。跟随机器人在超声波传感器的帮助下不断地检查领导者。当领导者面临障碍时，它通过我们的传感器的帮助克服障碍回避问题。跟随机器人不断地检查引导机器人的每一个动作，并根据引导机器人进行移动。3.2 集合：图2 环境中的交会路径规划集合方式的室内环境如图2所示。在这种情况下，两个机器人并行移动，主机器人通过从目标获取信标信号来规划路径并对跟随机器人的行为进行并行检测。当机器人面对障碍物时，领队机器人会做出决定，并根据位置场景向前移动，执行不同的动作。根据位置要求，机器人将在引导-跟随或集合方式中移动。图3 环境中备选交会路径规划3.3执行的动作集合:在不同的场景中，集合方法决定不同的动作。最初，机器人计划在障碍物出现之前执行前进动作。当任何障碍物出现在领队的左边和前面时，根据平方根技术，它倾向于向右移动。最后，跟随者通过观察领导者的行为运动向右移动。当机器人只面对前方的障碍物时，领队采取左转动作。跟随者以平方根为基础运动与领队并行，如图6所示。 图4 领导流程图图5 跟随者流程图图6 在避障状态下执行的操作机器人的动作在流程图中表示如下，最初是本地化的。领导根据情况采取行动，在接收到信标信号后，它要么在会合中执行，要么在领队跟随者进近中执行。领队的优先权倾向于执行会合的方法，当环境中有更多障碍物时，当空间仅为单个机器人定义时，它采用领队跟随者的方法。4. 基于FPGA的机器人VLSI结构VLSI体系结构由控制单元、传感器模块和电机控制逻辑、左电机和右电机组成。这里的控制单元是Xilinx板、XC3S500E-4FG320（FPGA斯巴达3E），XC6LX16-CS324（FPGA斯巴达6），控制单元控制所有设备并引导机器人到达目的地。图7 移动机器人控制单元的VLSI结构图8 电机控制逻辑单元上述结构中的输入模块由脉宽调制和匹配电路组成。传感器用于探测道路上的障碍物。传感器不断发出声波，当声波击中障碍物时，它会反射回被称为回波信号的传感器。通过计算回波信号的脉冲宽度，可以得到障碍物的距离。来自传感器的信号被发送到输入模块，匹配电路匹配来自传感器的所有信号，并向控制单元提供适当的信号。控制单元由决策模块、多路复用器和电机控制逻辑组成。控制单元根据传感器信号决定机器人必须航行的两种方法:集合和引导-跟随。在集合时，机器人彼此并行移动以到达目的地。在引导-跟随接近中，两个机器人依次移动以到达目的地。根据环境的现状，机器人从一种方法切换到另一种方法。电机控制逻辑向左电机和右电机发出信号，帮助机器人向各个方向移动。5. 移动机器人设计图9 FPGA机器人的框图框图显示了具有不同模块的机器人的结构。控制单元控制机器人以期望的路径导航。超声波传感器安装在机器人的各个位置以探测障碍物从而成功导航。电压调节器用于降低框图中所有模块的电压。带驱动电路的步进电机帮助机器人向所有需要的方向移动。这两个机器人的设计方式与上述所有模块类似。一个机器人被称为领导者，另一个是追随者。根据场景的不同，机器人转换为两种方法，即集合和领导-跟随者的概念。该机器人采用超声波传感器和2个步进电机驱动电路。该算法已在verilog 14.4中进行了编码，该设计在XC6Lx16-CS324 Xilinx斯巴达-6 FPGA板上实现，与PC/笔记本20相比，计算功耗更低。6. 结果算法的仿真结果如下。主、从机器人交会方式仿真结果如图10和图11所示。引导和跟随机器人传感器提供了关于跟随器的信息，该跟随器在集合动作中与引导器平行，如第二节所述。先导-跟随模式模拟结果如图12和13所示。在这种模式下，跟随机器人跟随在领队的后面。图10 集合模式下的领导行动图11 集合模式下的跟随器动作图12 引导-跟随模式下的引导动作图13 引导-跟随模式下的跟随动作图14 机器人在集合点 图15机器人面临障碍图16执行向左运动的机器人 图17到达集合点行为控制的硬件安装提供了实验的快照，如图14-17所示，相同设备利用率如表1所示。表1 行为控制机器人的设备利用率逻辑使用斯巴达3E斯巴达6资源百分率资源百分率无切片11175254692切片触发器数量11811256264个输入发射器控制塔的数量19172023930IOB的强度22396223967.结论本文讨论了两个具有并行处理的移动机器人之间的行为控制，以及在室内环境下利用路径规划算法实现的硬件高效方案。VLSI结构是为FPGA机器人实现集合和跟随引导方法开发的。在工业环境下，多机器人时代通过实施行为控制来实现比单个机器人更好的效果。参考文献1 Jones Y. Mori, Janier Arias-Garcia, Camilo Snchez-Ferreira, Daniel M.Munoz, Carlos H. Llanos, and J. M. S. T. Motta, “An FPGA-Based Omnidirectional Vision Sensor for Motion Detection on Mobile Robots,” International Journal of Reconfigurable Computing, vol. 2012, Article ID 148190, 16 pages, 2012.2 Sesh Commuri, V. Tadigotla. L.Sliger, “Task-based Hardware Reconfiguration in Mobile Robots Using FPGAS” SpingerScience. Journal of Intelligent and Robotic Systems Journal Article20070921-0296.3 Michael Defoort, Thierry Floquet, Annemarie Kksy, and Wilfrid Perruquetti, “Sliding-Mode Formation Control for Cooperative Autonomous Mobile Robots” IEEE Trans On Industrial Electronic.s, vol. 55, no. 11, Nov 2008.4 Y. J. Huang, J. D. Yu, B. W. Hong, C. H. Tai, and T. C. Kuo,” An Experimental Multi-Agent Robots System for Operating in Hazardous Environments” World Academy of Science, Engineering and TechnologyVol:53 2011-05-27 218 International Science Index 53, 2011/publications/1600.5 Chaaban, Y.; Hahner, J.; Muller-Schloer, C., Towards Fault-Tolerant Robust Self-Organizing Multi-agent Systems in Intersections without Traffic Lights, Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD 09. Computation World: , vol., no., pp.467,475, 15-20 Nov. 2009.6 Kanda, T.; Miyashita, T.; Osada, T.; Haikawa, Y.; Ishiguro, H., Analysis of humanoid appearances in human-robot interaction, Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on , vol., no., pp.899,906, 2-6 Aug. 2005.7 Fethi Belkhouche; Boumediene Belkhouche; A Pursuit-Rendezvous Approach for Robotic tracking” School of arts and sciences Texas A & M Int. University Laredo, Texas USA ,EECS Department New Orleans, LA 70118 USA Open Access Database 8 Collaborative Robot Exploration and Rendezvous: Algorithms, Performance Bounds and Observations Autonomous Robots, 2001, Volume 11, Number 2, Page 117 Nicholas Roy, Gregory Dudek9 Gene Eu Jan; Ki Yin Chang; Parberry, I., Optimal Path Planning for Mobile Robot Navigation, Mechatronics, IEEE/ASME Transactions on , vol.13, no.4, pp.451,460, Aug. 2008.10 Rogge, J.; Aeyels, D., Sensor coverage with a multi-robot system, Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on, vol., no., pp.71, 76, 1-3 Oct. 2007.11 Cezayirli, A.; Kerestecioglu, F., Navigation of autonomous mobile robots in connected groups, Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3r International Symposium on , vol., no., pp.162,167, 12-14 March 2008.12 Belkhouche, F.; Belkhouche, B.; Rastgoufard, P., Multi-robot rendezvous in the plane, Systems, Man and Cybernetics, 2005 IEEE International Conference on , vol.3, no., pp.2293,2298 Vol. 3, 10-12 Oct. 200513 Chakravarthy, N.; Jizhong Xiao, FPGA-based Control System for Miniature Robots, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol., no., pp.3399, 3404, 9-15 Oct. 2006.14 Vachhani, L.; Sridharan, K., Robotic mapping with simple sensing and processing hardware Algorithm and architecture, 11th International Conference on Control Automation Robotics & Vision (ICARCV), 2010, vol., no., pp.1012, 1017, 7-10 Dec. 2010.15 Kumar, P.R ,K. Sridharan “VLSI-Efficient Scheme and FPGA Realization for Robotic Mapping in a Dynamic Environment” Very Large Scale Integration (VLSI) Systems, IEEE Transactions on (Volume:15 , Issue: 1 ) Jan 2007,118 ? 123.16 Chinnaiah M C ,Sanjay Dubey ,P Rajesh Kumar, T Satya Savithri “A Novel approach and Implementation of Robot path planning using Parallel processing algorithm” International Conference on Communications, Devices and Intelligent Systems (CODIS), 2012 , 345 ? 348.17 P. Rajesh Kumar ,K. Sridharan S. Srinivasan “A parallel algorithm, architecture and FPGA realization for landmark determination and map construction in a planar unknown environment” Parallel Computing Volume 32, Issue 3, March 2006, Pages 205-221.18 Taylor, K.; LaValle, S.M., I-Bug: An intensity-based bug algorithm, Robotics and Automation, 2009. ICRA 09. IEEE International Conference on, vol., no., pp.3981,3986 , 12-17 May 200919. Taylor, K.; LaValle, S.M., Intensity-based navigation with global guaranteesAutonmous robot(2014) 36:349-36420 Yongguo Mei; Yung-Hsiang Lu; Hu, Y.C.; Lee, C. S G, A case study of mobile robots energy consumption and conservation techniques, Advanced Robotics, 2005. ICAR 05. Proceedings., 12th International Conference on , vol., no., pp.492,497, 18-20 July 2005.A Novel approach in Implementation of Rendezvous behavioral control between FPGA Robots M.C.Chinnaaiah1, P Padma1,T Satya Savithri2,P Rajesh kumar31Padmasri Dr B V Raju Institute of Technology ,Narsapur ,Medak(Dt),A.P,India 2Prof ,ECE Dept,JNTU College of Engineering,JNTU Hyderabad,India. 3Prof & HOD ,ECE Dept ,PVPSIT,Vijayawada, IndiaAbstract: -In the context of multi agent robot path planning, behavioral control exhibits its importance. This paper proposes the new scheme and hardware design for key elements of behavioral control between two mobile robots. Among two robots one is considered as the leader and other as follower. Leader (IR seeker) plans the path, it starts at S and reaches the Goal (G)based on the Intensity which is transmitted from the Goal (IR Beacon). Every instant of time it checks the follower behavior and takes appropriate action depending on its position. Follower simply follows the leader and checks the behavior of leader all the way. The mobile robots have been designed using ultra sonic sensors, FPGA and stepper motors. The control unit designed to perform autonomous navigation for both the rendezvous and leader-follower methodologies. The Design is developed using verilog and implemented on Spartan 6 FPGA robots, the simulation results have been shown and implementation done successfully. This design very much useful in industry environment. Keywords: - Ultra sonic sensors, FPGA, Robots, Behavioral control, VLSI architecture. I. INTRODUCTION We can make our lives easier and automatic by using Robots. Robots are developed with dynamic and parallel processing algorithms to achieve better livelihood, same has been influenced at work places in industry also. In this regard robot mainly depended on navigation. In order to perform task the robot has to navigate after considering the given environment. Different type robots are used in industry and other applications like cognitive wheel chairs are used to assist the human in indoor environment. The robotic era started with multi robot system to increase the assistance in above applications. The navigation of the multi robot depends on three concepts (1) Mapping i.e. path planning. (2) Behavioral Control, and (3) Decision making. The first stage uses information from sensors for creating a map of the environment. The second one relates the map with the sensor information, allowing the robot to self-localization in the environment. The third stage considers the path-planning problem 1. Time constraints and resource constraints are very important factors for any robotic application. The Behavioral control approach for mobile robots which can assist in industrial environment for various tasks has been presented in the paper. A coordinated control scheme based on leader?follower approaches is shown in 2 3. Even in hazardous environments, multi-agent robots perform distinct manipulation can be done such as obstacle avoidance, remote control and automatic searching, and so on 4.Self-organizing multi-agent system solving the conflict between a central planning algorithm and the autonomy of the agents 5. The Path Planning with multiple robots is difficult, because it combines two different aspects. The goal can perform two different types of motion, namely accelerating motion, and non-accelerating motion 7. The problem of a rendezvous robot is a key step in Collaborative exploration and also relative kinematic equations 812.This Rendezvous concept involves communication with sensors, autonomous navigation, and path planning 9-11. In the Implementation of robot algorithms we need the parallel processing support with less power consumption and reconfigurable features. Due to this many are preferred the FPGA robots. Different path ? can be implemented using hardware efficient schemes 13-16.The goal is indicated by different approaches using landmarks 17 and intensity based methods 18.This research work required more number of Input output ports and also required to perform parallel, so the ? This paper is organized as follows .Section II describe the main idea of the paper with algorithm steps. The next level of the paper is mentions about the behavioral Control processing and flowcharts in section III. The VLSI architecture for above algorithm is discussed in section IV. Design of the robot is represented with block diagram in section V. The outcome of the paper is shown in section VI with simulation and implementation results, followed with conclusion. II. MAIN IDEAWe present the main idea in this paper using sensor- based construction for static environment. The robots in industries reduce the employee work to a great level. An individual robot can perform many jobs but for huge works, multiple robots are considered for high accuracy. Multiple robots need communication between them to accomplish the tasks. This communication can be done by using simple sensors. The robots are locally developed in laboratory for the implementation of 1) Rendezvous and 2) Leader follower methodology. The Basic idea of this algorithm is to implement the autonomous navigation of two robots with behavioral control. The robots start from the node (S) with mutual communication using sensors. One of the robots is considered as master or leader. It does the path planning to reach the Goal (G) .The leader is built with IR seeker and the Goal (G) is ?developed as a Tower using IR beacon 19. The follower acts in both scenarios, as the Rendezvous (parallel) to the leader and follows the leader using leader-follower methodology. 2.1 Algorithm steps: Step 1: The robot estimates the localization with sensor information. Step 2: Leader or master robot checks the behavior of the follower or slave, either it is in rendezvous or follower mode. Step 3: The master starts from S and then path planning is performed based on Intensity signal (IR Beacon) of the Goal G. Step 4: Master checks behavior of the follower all the way and takes the appropriate action to reach the goal even in the Obstacle avoidance condition. Remarks 1: Follower also checks the behavior of the master robot in the environment, either in rendezvous or leader follower mode as the master performs in step 2. Remarks 2: The obstacle avoidance decision is made by both robots with their behavior according to the position. The robots path planning is performed based on their behavior as follows III. Behavioral Controls between Mobile Robots3.1 Leader-Follower: Fig 1.Leader follower Path planning environment The two robots moves one after the other and leader robot takes the decision over path planning. The follower robot has just to follow the leader robot. When the leader robot detects the obstacle, it takes the required mechanism to overcome. The follower robot continuously checks the leader with the help of ultrasonic sensors. Through the path when the leader faces an obstacle, it overcomes Obstacle avoidance issues with the help of US sensors. Every movement of the leader robot is continually checked by the follower robot and moves according to the leader robot. 3.2 Rendezvous: Fig 2.Rendezvous Path planning in environment The indoor environment for rendezvous approach is shown in figure2. In this case the two robots moves in parallel and master robot plans the path planning by retrieving the beacon signal from goal. It parallelly checks the behavior of follower robot. When the robot faces an obstacle, the leader robot takes a decision and and perform different actions depending on the place scenario moves forward. Depending on place requirement the robots will move either in leader- follower or in rendezvous approach. Fig 3.Alternative Rendezvous Path planning in environment 3.3 Actions performed Rendezvous: Different actions are driven in rendezvous approach in different scenarios. Initially robots plan to perform in forward action until the Obstacle appears. When any Obstacle appears on left and front side of the leader it prefers to move right based on square root technique. Eventually the follower moves to right with behavioral movement by observing the leader. When robots face the Obstacle only in front side, then leader takes left turn action. The follower moves with square root based movement and comes to parallel position with the leader as shown in fig 6. L1F1SGSGLfLfLfSGFLLFLLFLLFFFLLFF?Fig 4.Leader flow chart Fig 5.Follower flow chart?Fig 6.Action Performed in Obstacle avoidance condition The robots actions are represented in the flow charts as follows, initially they localize. The Leader takes an action depending on the scenario. It has either to perform in rendezvous or leader follower approaches after collecting the beacon signal .The priority of the leader prefers to perform rendezvous approach, when the environment is with more obstacles and when the space is defined only for a single robot it takes the leader- follower approach. IV. VLSI Architecture for FPGA based RobotsThe VLSI Architecture consists of control unit, sensor module, and Motor control logic, Left motor, Right motor. Here the control unit is Xilinx board XC3S500E-4FG320 (FPGA Spartan 3E), XC6LX16-CS324 (FPGA Spartan 6) The control unit controls all the devices and navigates the robot to reach the destination. Fig 7.VLSI architecture of Mobile ?Fig 8.Motor Control Logic unitThe input module in the above architecture consists of PWM and match circuit. Sensors are used to sense the obstacles in the path. The Sensor continually emits sound waves. When a sound wave hit the obstacle it reflects back to the sensor known as echo signal. The distance of the obstacle can be obtained by calculating the pulse width of the echo signal. The signal from the sensor is given to the input module. Matching circuits matches all the signals from the sensors and gives appropriate signal to the control unit. The control unit consists of decision module, multiplexer and motor control logic. Depending on the sensor signals the control unit decides the two approaches Rendezvous and leader -follower in which the robot has to navigate. In rendezvous the robots move in parallely to each other to reach the destination. In leader-follower approach the two robots move one after the other in ordered to reach the destination. Depending on the current situation of the environment, the robots switch from one approach to another approach. The motor control logic gives the signals to the left motor and right motor which helps the robot to move in all directions. V. Design of Mobile Robots Fig 9: Block Diagram of an FPGA Robot ?The block diagram shows the construction of a robot with different modules. The control unit controls robot to navigate in a desired path. The ultrasonic sensors are placed in all sides of the robot to detect the obstacles for successful navigation. Voltage regulator is used to step-down the voltage to all modules in the block diagram. The stepper motors with driving circuits help the robot to move in all required directions. The two robots are designed in the similar manner with all the above modules. One robot is said to be a leader and the second is a follower. Depending on scenario the robots switch into two methodologies namely Rendezvous and leader-follower concept. The FPGA Robot is developed with ultrasonic sensors and 2 stepper motors with driving circuits. This algorithm has been coded in Verilog 14.4.The design was implemented on XC6LX16-CS324 Xilinx Spartan-6FPGAboard .It consumes less power for computation than a PC/Laptop 20. VI. RESULTSThe simulation results of Algorithm are shown below. The leader and follower robots rendezvous mode simulation results was as shown in fig 10 & 11. The leader & follower robot sensors information presents about the follower that it is following parallel to leader in rendezvous action as presents in the section II. The Leader-follower mode simulation results are shown in fig 12 & 13. In this mode the follower robot is followed behind the leader. Fig 10: Leader actions in rendezvous mode Fig 11: Follower actions in rendezvous mode Fig 12: Leader actions in Leader-Follower mode Fig 13: Follower actions in Leader-Follower mode Fig 14 Robots are in Rendezvous Fig 15 Robots facing Obstacle Fig 16 Robots performing Left action Fig 17 Reached to Rendezvous The hardware Implementation of the behavioral control furnished with snapshots of the experiment as shown in fig 14-17, for the same device utilization presented in Table 1.?Table 1 Device utilization of the behavioral control robots Logic Utilization Spartan 3ESpartan 6 ResourcesPercentageResourcesPercentageNo of Slices 1175254692No of Slice Flip Flops 1181125626No of 4 input ?19172023930Number of ?2239622396VII Conclusion This paper discusses behavioral control between the two mobile robots with parallel processing and hardware efficient scheme using path planning algorithm in indoor environment.VLSI architecture is developed for FPGA Robots to perform the rendezvous and leader follower methodologies. In industrial environment multi robot era has influenced to achieving better results than individual robot by implementing behavioral control.References 1 Jones Y. Mori, Janier Arias-Garcia, Camilo Snchez-Ferreira, Daniel M. ? ? ? ? ? ? ? ? ? ? ? ?-Based Omnidirectional Vision Sensor for Motion Detection on Mobile ? International Journal of Reconfigurable Computing, vol. 2012, Article ID 148190, 16 pages, 2012. ?-based Hardware ?Intelligent and Robotic Systems Journal Article20070921-0296. 3 Michael Defoort, Thierry Floquet, Annemarie Kksy, and Wilfrid ?-Mode Formation Control for Cooperative Autonomous ?2008. 4 Y. J. Huang, J. D. Yu, B. W. Hong, C. H. Tai, and T. C. Ku?Experimental Multi-Agent Robot System for Operating in Hazardous ?TechnologyVol:53 2011-05-27 218 International Science Index 53, 2011 /publications/1600. 5 Chaaban, Y.; Hahner, J.; Muller-Schloer, C., Towards Fault-Tolerant Robust Self-Organizing Multi-agent Systems in Intersections without Traffic Lights, Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, 2009. COMPUTATIONWORLD 09. Computation World: , vol., no., pp.467,475, 15-20 Nov. 2009. 6 Kanda, T.; Miyashita, T.; Osada, T.; Haikawa, Y.; Ishiguro, H., Analysis of humanoid appearances in human-robot interaction, Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on ,vol., no., pp.899,906, 2-6 Aug. 2005. 7 Fethi Belkhouche; Boumediene Belkhouche; A Pursuit-Rendezvous ?Int. University Laredo, Texas USA ,EECS Department New Orleans, LA70118 USA Open Access Database 8 Collaborative Robot Exploration and Rendezvous: Alg
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