Evolution of fuzzy behaviors for multi-robotic system

In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.     

Lifelong Adaptation in Heterogeneous Multi-Robot Teams: Response to Continual Variation in Individual Robot Performance

Generating teams of robots that are able to perform their tasks over long periods of time requires the robots to be responsive to continual changes in robot team member capabilities and to changes in the state of the environment and mission. In this article, we describe the L-ALLIANCE architecture, which enables teams of heterogeneous robots to dynamically adapt their actions over time. This architecture, which is an extension of our earlier work on ALLIANCE, is a distributed, behavior-based architecture aimed for use in applications consisting of a collection of independent tasks. The key issue addressed in L-ALLIANCE is the determination of which tasks robots should select to perform during their mission, even when multiple robots with heterogeneous, continually changing capabilities are present on the team. In this approach, robots monitor the performance of their teammates performing common tasks, and evaluate their performance based upon the time of task completion. Robots then use this information throughout the lifetime of their mission to automatically update their control parameters. After describing the L-ALLIANCE architecture, we discuss the results of implementing this approach on a physical team of heterogeneous robots performing proof-of-concept box pushing experiments. The results illustrate the ability of L-ALLIANCE to enable lifelong adaptation of heterogeneous robot teams to continuing changes in the robot team member capabilities and in the environment.     

Multiagent-Based Multi-team Formation Control for Mobile Robots

In the field of formation control, researchers generally control multiple robots in only one team, and little research focuses on multi-team formation control. In this paper, we propose an architecture, called Virtual Operator MultiAgent System (VOMAS), to perform formation control for multiple teams of mobile robots with the capabilities and advantages of scalability and autonomy. VOMAS is a hybrid architecture with two main agents. The virtual operator agent handles high level missions and team control, and the robot agent deals with low level formation control. The virtual operator uses four basic services including join, remove, split, and merge requests to perform multi-team control. A new robot can be easily added to a team by cloning a new virtual operator to control it. The robot agent uses a simple formation representation method to show formation to a large number of robots, and it uses the concept of potential field and behavior-based control to perform kinematic control to keep formation both in holonomic and nonholonomic mobile robots. In addition, we also test the stability, robustness, and uncertainty in the simulation. This research was supported by the National Science Council under grant NSC 91-2213-E-194-003.     

A Real-Time Hybrid Architecture for Biped Humanoids with Active Vision Mechanisms

A real-time hybrid control architecture for biped humanoid robots is proposed. The architecture is modular and hierarchical. The main robot’s functionalities are organized in four parallel modules: perception, actuation, world-modeling, and hybrid control. Hybrid control is divided in three behavior-based hierarchical layers: the planning layer, the deliberative layer, and the reactive layer, which work in parallel and have very different response speeds and planning capabilities. The architecture allows: (1) the coordination of multiple robots and the execution of group behaviors without disturbing the robot’s reactivity and responsivity, which is very relevant for biped humanoid robots whose gait control requires real-time processing. (2) The straightforward management of the robot’s resources using resource multiplexers. (3) The integration of active vision mechanisms in the reactive layer under control of behavior-dependant value functions from the deliberative layer. This adds flexibility in the implementation of complex functionalities, such as the ones required for playing soccer in robot teams. The architecture is validated using simulated and real Nao humanoid robots. Passive and active behaviors are tested in simulated and real robot soccer setups. In addition, the ability to execute group behaviors in real- time is tested in international robot soccer competitions.     

基于情感与环境认知的移动机器人自主导航控制

将基于情感和认知的学习与决策模型引入到基于行为的移动机器人控制体系中, 设计了一种新的自主导航控制系统. 将动力学系统方法用于基本行为设计, 并利用ART2神经网络实现对连续的环境感知状态的分类, 将分类结果作为学习与决策算法中的环境认知状态. 通过在线情感和环境认知学习, 形成合理的行为协调机制. 仿真表明, 情感和环境认知能明显地改善学习和决策过程效率, 提高基于行为的移动机器人在未知环境中的自主导航能力     

Modular behavior-based control for team humanoids

When developing teams of humanoids — rather than an individual humanoid — there are a number of issues that become important to consider, including robustness, scalability, versatility, and also development and production costs. Therefore, we used a modern approach to AI that puts emphasis on the balance between control, electronic hardware, material, sensory system and energy in order to develop the team of Viki humanoid robots. In contrast to the top-down approach of equipping a humanoid with as many sensors, motors, power, etc., as possible, we developed a bottom-up approach to the construction of humanoids, regarding both hardware and software (modular behaviorbased control). The approach is shown with the development of the Viki humanoid team that won the RoboCup Humanoids Free Style World Championship 2002. In this paper, we focus on the main result of the behavior-based architecture with many layers of behaviors at different levels, which make it easy for both engineers to design new behaviors and for end-users to develop humanoid behaviors at different levels of complexity, dependent on the competencies of the individual end-user. With this architecture, it becomes possible to develop simple user interfaces with a user-guided implementation of our modular behavior-based approach, in order to allow any user to design performances with the humanoid robots.     

A behavior-based mobile robot architecture for Learning from Demonstration

Autonomous mobile robots (AMRs), to be truly flexible, should be equipped with learning capabilities, which allow them to adapt effectively to a dynamic and changing environment. This paper proposes a modular, behavior-based control architecture, which is particularly suited for “Learning from Demonstration” experiments in the spatial domain. The robot learns sensory-motor behaviors online by observing the actions of a person, another robot or another behavior. Offline learning phases are not necessary but might be used to trim the attained representation. First results applying RBF-approximation, growing neural cell structures and probabilistic models for progress estimation, are presented.     

Coupled behaviors in the reactive control of cooperating mobile robots

A group of stimulus-response functions have been combined in a behavior-based reactive control architecture for groups of cooperating mobile robots. A successful object relocation task has been achieved with two coupled robots using the Behavior Synthesis Architecture. However, as task complexity increases preplanning the behavior parameters is increasingly difficult. A mechanism for on-line parameter adjustment is therefore necessary (although a learning process could be used, this often requires extensive processing ability on the robot). This paper outlines the system of Behavior Synthesis and a possible method of adaptively modifying the dynamic coupling between behaviors. The aim of this approach is to emulate the process of 'attention span' as displayed by most animals, in which current sensory input has a higher priority and this decreases the importance of behaviors not essential to the current situation.     

基于运动图式的多机器人合作追捕

为实现多个机器人合作追捕目标机器人,以基于运动图式的反应式控制结构为基础,设计追捕机器人的4种基本行为：奔向目标,避开障碍物,避让队友以及收缩包围,为避免机器人陷入死锁状态,引入随机漫游行为。通过基本行为的矢量合成和机器人之间的局部交互作用,实现多机器人的协作行为。仿真试验验证了该方法的有效性。     

Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle; this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.     

基于混合结构的机器人Agent控制结构的研究

该文面向分布Agent多移动机器人系统,提出了一种适合于多移动机器人的机器人Agent分层式体系结构,包括状态监测层、决策规划层、协调控制层和行为控制层,其中状态监测层主要实现整个系统对外部环境的状态监测。决策规划层设定系统的全局目标和单个机器人的局部目标,合理快速地完成任务的分解和分配,实现机器人之间任务级之间的协作。协调控制层完成机器人之间的运动协调。行为控制器主要采用基于行为的方法实现具体的运动控制。该结构应用于RoboCup环境下的分布多机器人系统中,满足复杂的、动态的应用环境和系统要求。     

多移动机器人系统个体控制体系结构

本文面向多移动机器人系统,提出了一种适合于移动机器人个体的分层式体系结构, 包括系统监控层、协作规划层和行为控制层三个层次．其中系统监控层主要实现人对系统的 实时监控功能;协作规划层在与其它机器人相应层的交互过程中建立系统的分层式组织形式 ,合理快速地完成任务的分解和分配,实现了机器人之间的任务级协作;行为控制层主要采 用基于行为的方法实现具体的运动控制．该结构满足了移动机器人渐趋复杂的应用环境和日 益增大的系统规模的要求．     

Solving function distribution and behavior design problem for cooperative object handling by multiple mobile robots

This paper addresses the function distribution and behavior design problem for a multirobot system which incorporates a behavior-based dynamic cooperation strategy for object handling. The proposed multiple robot system is composed of a managing robot and homogeneous behavior-based robots. The cooperation strategy in this system is realized in two steps: designing the distributed robot's cooperative behavioral attributes according to the robot's abilities, and organizing these behavioral attributes so that team cooperation is realized. For indicating an incremental style of local behavior construction, an advanced design of cooperative behavior for coping with unknown disturbance is addressed. Additionally, two extended cooperation strategies designed for a path tracking task are described. These three strategies are based on the same concept on performing manipulation in coordination. Therefore, by considering the function distribution among the managing robot and worker robots, and considering behavior design of each worker robot, the proposed system is able to achieve the object handling task with different performances according to the task requirement, such as with or without path tracking and with or without contact with the environment. Experimental results demonstrate the applicability of the proposed system.     

L-ALLIANCE: Task-oriented multi-robot learning in behavior-based systems

A large application domain for multi-robot teams involves task-oriented missions, in which potentially heterogeneous robots must solve several distinct tasks. Previous research addressing this problem in multi-robot systems has largely focused on issues of efficiency, while ignoring the real-world situated robot needs of fault tolerance and adaptivity. This paper addresses this problem by developing an architecture called L-ALLIANCE that incorporates task-oriented action selection mechanisms into a behavior-based system, thus increasing the efficiency of robot team performance while maintaining the desirable characteristics of fault tolerance and adaptivity. We present our investigations of several competing control strategies and derive an approach that works well in a wide variety of multi-robot task-oriented mission scenarios. We provide a formal model of this technique to illustrate how it can be incorporated into any behavior-based system.     

A Decentralized Architecture for Multi-Robot Systems Based on the Null-Space-Behavioral Control with Application to Multi-Robot Border Patrolling

This paper presents a control architecture for multi-robot systems. The proposed architecture has been developed in the framework of the Null-Space-based-Behavioral (NSB) control, a competitive-collaborative behavior-based control approach. The standard NSB statically determines a set of suitably defined elementary tasks (behaviors) and their priorities, i.e., they cannot be dynamically changed according to mission requirements and environmental constraints. In this paper, a three layer architecture has been designed in order to avoid such a drawback. The single robotic unit (agent) performing the mission is placed on the lower layer. In the middle layer, suitably defined elementary behaviors are defined; these elementary behaviors are then combined, via the NSB approach, in more complex actions. The upper layer is a Supervisor in charge of dynamically selecting the proper action to be executed. As further contribution, the architecture has been applied to the multi-robot border patrolling mission to generate a decentralized, deterministic and non-communicative solution that is robust to faults, and prevents collisions, even in the case of high robot density. Finally, the simulations on a team composed by a large number of robots, and experiments on a real setup, composed by three Pioneer-3DX robots, are provided.     

一种基于行为的移动机器人目标人跟踪控制方法

针对室内复杂环境,对于智能服务移动机器人,设计一个连续稳定的目标人跟踪算法是必要的.为此提出一种室内环境下基于行为的移动机器人对运动目标人进行跟踪的控制方法,该方法综合优先级裁决方法与模糊行为融合法选取移动机器人的控制行为,较好地解决跟踪过程中存在多种行为以及行为冲突问题,在完成运动避障的同时保持对运动目标的跟踪.对避...     

Adaptive behaviors of reactive mobile robot with Bayesian inference in nonstationary environments

This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.     

基于主动视觉和超声的机器人目标跟踪系统

运动目标跟踪技术是未知环境下移动机器人研究领域的一个重要研究方向。该文提出了一种基于主动视觉和超声信息的移动机器人运动目标跟踪设计方法,利用一台SONY EV-D31彩色摄像机、自主研制的摄像机控制模块、图像采集与处理单元等构建了主动视觉系统。移动机器人采用了基于行为的分布式控制体系结构,利用主动视觉锁定运动目标,通过超声系统感知外部环境信息,能在未知的、动态的、非结构化复杂环境中可靠地跟踪运动目标。实验表明机器人具有较高的鲁棒性,运动目标跟踪系统运行可靠。     

Planetary Cliff Descent Using Cooperative Robots

Future robotic planetary exploration will need to traverse geographically diverse and challenging terrain. Cliffs, ravines, and fissures are of great scientific interest because they may contain important data regarding past water flow and past life.Highly sloped terrain is difficult and often impossible to safely navigate using a single robot. This paper describes a control system for a team of three robots that access cliff walls at inclines up to 70°. Two robot assistants, or anchors, lower a third robot, called the rappeller, down the cliff using tethers. The anchors use actively controlled winches to first assist the rappeller in navigation about the cliff face and then retreat to safe ground.This paper describes the coordination of these three robots so they function as a team to explore the cliff face. Stability requirements for safe operation are identified and a behavior-based control scheme is presented. Behaviors are defined for the system and command fusion methods are described. Controller stability and sensitivity are examined. System performance is evaluated with simulation, a laboratory system, and testing in field environments.     

Multi-robot cooperation-based mobile printer system

This paper proposes a mobile printer system (MPS) based on multi-robot cooperation. The system consists of multiple mobile robots, a wireless LAN system, a graphic user interface (GUI), and a host computer. The GUI comprises a user input section, a task allocation optimization section, and a control and communication section. Its operation is as follows: a user draws a picture on an input window of the GUI, and then the host computer commands client printer-robots to reproduce the same on a paper in a finite time.To control multiple robots during this process, two kinds of multi-robot control architectures along with a collision-free arbitration configuration are proposed. One is a decentralized control architecture, which employs subsumption architecture based on behavior-based robotics. The robots continue to seek the nearest line and to draw it repeatedly until all lines are drawn. This architecture needs no pre-planning and is fault-tolerant. Another is a centralized control architecture, which employs an evolutionary algorithm (EA). The host computer optimizes the task (finding time-optimal path) allocation for each robot by using an evolutionary algorithm and sends the optimized job sequence to the robots. To minimize the elapsed time in drawing all the lines, an evolutionary algorithm with a representation of an individual suitable for the MPS is employed for the task optimization. This architecture can minimize the elapsed time effectively and offers the option of distance-optimality in addition to time-optimality.The proposed architectures are simulated and real experiments with three omni-directional robots are carried out to demonstrate the effectiveness and the applicability of the proposed mobile printer system.