Speech-based formation control of the multi-robot system

The article presents multiple pattern formation control of the multi-robot system using A* searching algorithm, and avoids the collision points moving on the motion platform. We use speech recognition algorithm to decide the various pattern formations, and program mobile robots to present the movement scenario on the grid-based motion platform. We have been developed some pattern formations to be applied in game applications, such as long snake pattern formation, phalanx pattern formation, crane wing pattern formation, sword pattern formation, cone pattern formation and so on. The mobile robot contains a controller module, three IR sensor modules, a voice module, a wireless RF module, a compass module, and two DC servomotors. The controller of the mobile robot can acquire the detection signals from reflect IR sensor modules and the compass module, and decide the cross points of the aisle. The mobile robot receives the command from the supervised computer, and transmits the status of environment to the supervised computer via wireless RF interface. We develop the user interface of the multi-robot system to program motion paths for various pattern formation exchanges using the minimum displacement. Users can use speech to control the multiple mobile robots to execut pattern formation exchange. In the experimental results, users can speak the pattern formation. The speech recognition system receives the signal to decide the pattern formation. The multiple mobile robots can receive the pattern formation command from the supervised computer, and arrange the assigned pattern formation on the motion platform, and avoid other mobile robots.     

Mobile Robots Navigation Modeling in Known 2D Environment Based on Petri Nets

The paper deals with supervised robot navigation in known environments. The navigation task is divided into two parts, where one part of the navigation is done by the supervisor system i.e. the system sets the vector marks on the salient edges of the virtual environment map and guides the robot to reach these marks. Mobile robots have to perform a specific task according to the given paths and solve the local obstacles avoidance individually. The salient point’s detection, vector mark estimation and optimal path calculation are done on the supervisor computer using colored Petri nets. The proposed approach was extended to simulate a flexible manufacturing system consisting of swarm of 17 robots, 17 - warehouses and 17 - manufacturing places. Our experimental investigation showed that simulated mobile robots with proposed supervision system were efficiently moving on the planned path.     

Develop a multi-detection security system using multi-sensor fusion algorithms

The paper develops the multi-detection system using multi-level surveillance structure. The system contains active detection modules, passive detection modules, a supervised computer, an image system and an intelligent home. The passive detection modules contain wire/wireless detection modules and appliance control modules, and decide the event to be true or not using fusion algorithms, and transmit detection signals to the supervised computer via wire/wireless interface. Mobile robots are active detection modules and carry various sensors to search dangerous events. Each mobile robot transmits the real-time event signal to the supervised computer and the other mobile robots via wireless RF interface. The image system detects fire source using Otsu algorithm. The system integrates wire/wireless passive detection modules, mobile robots and image system to detect fire source using weighted average method. If the fire event occurs, the supervised computer calculates the dangerous grade using logical filter method according to the signals of detection modules in the first step, and transmits the position of the fire event to the other mobile robots. The assigned mobile robots move to the event location for double check autonomously, and transmit the detection results to the supervised computer. The supervised computer gives the final decision according to the feedback signals in the second step. Finally, we present some experimental scenarios using passive detection modules, active detection modules and image system for the fire detection in the intelligent home.     

Navigation strategies for multiple autonomous mobile robots moving in formation

The problem of deriving navigation strategies for a fleet of autonomous mobile robots moving in formation is considered. Here each robot is represented by a particle with a spherical effective spatial domain and a specified cone of visibility. The global motion of each robot in the world space is described by the equations of motion of the robot's center of mass. First, methods for formation generation are discussed. Then, simple navigation strategies for robots moving in formation are derived. A sufficient condition for the stability of a desired formation pattern for a fleet of robots each equipped with the navigation strategy based on nearest neighbor tracking is developed. The dynamic behavior of robot fleets consisting of three or more robots moving in formation in a plane is studied by means of computer simulation.     

Path planning of a multiple mobile robot system

We present path-planning techniques for a multiple mobile robot system. The mobile robot has the shape of a cylinder, and its diameter, height, and weight are 8 cm, 15 cm, and 1.5 kg, respectively. The controller of the mobile robot is an MCS-51 chip, and it acquires detection signals from sensors through I/O pins. It receives commands from the supervising computer via a wireless RF interface, and transmits the status of the robots to the supervising computer via a wireless RF interface. The mobile robot system is a module-based system, and contains a controller module (including two DC motors and drivers), an obstacle detection module, a voice module, a wireless RF module, an encoder module, and a compass detection module. We propose an evaluation method to arrange the position of the multiple mobile robot system, and develop a path-planning interface on the supervising computer. In the experimental results, the mobile robots were able to receive commands from the supervising computer, and to move their next positions according to the proposed method.     

An autonomous mobile robot for carrying food trays to the aged and disabled

Our research objective is to realize sensor-based navigation for car-like mobile robots. We adopt the generalized Voronoi graph (GVG) for the robot's local path and a map representation. It has the advantage to describe the mobile robot's path for sensor-based navigation from the point of view of completeness and safety. However, it is impossible to apply the path to car-like mobile robots directly, because the limitation of the minimum turning radius for a car-like robot may prevent it from following the GVG exactly. To solve this problem, we propose a local smooth path-planning algorithm for car-like mobile robots. Basically, an initial local path is generated by a conventional path-planning algorithm using GVG theory and it is modified smoothly by a Bezier curve to enable the car-like robots to follow it by maximizing our evaluation function. In this paper, we introduce a local smooth path-planning algorithm based on the GVG and explain the details of our evaluation function. Simulation and experimental results support the validity of the algorithm.     

A* search algorithm applied to a Chinese chess game

This article presents an A* search algorithm to be applied to path planning in a Chinese chess game, and uses multiple mobile robots to present the scenario. The mobile robots have a cylindrical shape, and their diameter, height, and weight are 8 cm, 15 cm, and 1.5 kg, respectively. The controller of the mobile robots is a MCS-51 chip. We play the Chinese chess game using multiple mobile robots according to the evaluation algorithm of the game, and calculate the displacement by the encoder of a DC servomotor. The A* search algorithm can solve the shortest-path problem for the mobile robots from the starting point to the target point on the chess board. The simulated results found the shortest path for the mobile robots (chess pieces) moving to target points from their starting points in a collision-free environment. Finally, we implemented the experimental results on a Chinese chess board using mobile robots. Users can play the Chinese chess game using the supervising computer via a wireless RF interface. The scenario of the feedback of the Chinese chess game to the user interface uses an image system.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

The autonomous mobile robot SENARIO: a sensor aided intelligentnavigation system for powered wheelchairs

The SENARIO project is develoing a sensor-aided intelligent navigation system that provides high-level navigational aid to users of powered wheelchairs. The authors discuss new and improved technologies developed within SENARIO concerning task/path planning, sensing and positioning for indoor mobile robots as well as user interface issues. The autonomous mobile robot SENARIO, supports semi- or fully autonomous navigation. In semi-autonomous mode the system accepts typical motion commands through a voice-activated or standard joystick interface and supports robot motion with obstacle/collision avoidance features. Fully autonomous mode is a superset of semi-autonomous mode with the additional ability to execute autonomously high-level go-to-goal commands. At its current stage, the project has succeeded in fully supporting semi-autonomous navigation, while experiments on the fully autonomous mode are very encouraging     

基于达芬奇的移动机器人开发平台设计

针对传统移动机器人的实时性差和扩展性差的局限性,在达芬奇技术的基础上,通过裁减定制,去除冗余的功能,设计了一种移动机器人的开发平台。该机器人系统包括移动机器人需要的视觉系统,并有丰富的运动控制接口以及驱动模块。同时,设计了多传感器融合、无线网络通信、路径规划、运动控制、人机界面等移动机器人的测试软件和应用模块。该移动机器人平台也具有模块化、硬件体积小、功耗低、可移植、可扩展、实时性强等优点。     

Concurrent Map Building and Behavior Learning for Navigation in Unknown Environments

In this paper, we report a robust and low-cost navigation algorithm for an unknown environment based on integration of a grid-based map building algorithm with behavior learning. The study focuses on mobile robots that utilize ultrasonic sensors as their prime interface with the outside world. The proposed algorithm takes into account environmental information to augment the readings from the low angular accuracy sonar measurements for behavior learning. The environmental information is obtained by an online grid-based map learning design that is concurrently operating with the behavior learning algorithm. The proposed algorithm is implemented and tested on an in-house-built mobile robot, and its performance is verified through online navigation in an indoor environment.     

Real-time path planning of autonomous robots in a two-dimensional unknown dynamic navigation environment

This paper deals with the real-time path planning of an autonomous mobile robot in two-dimensional, unknown, dynamic multiple robot navigation space. In particular, a collision-free navigation path planning strategy is presented in real time by using a heuristichuman like approach. The heuristic scheme used here is based on thetrial and error methodology with the attempt to minimize the cost of the navigation efforts, when time plays a significant role. Past built-up navigation experience and current extracted information from the surrounding environment are used for the detection of other moving objects (robots) in the same navigation environment. Moreover, the determination of asecure navigation path is supported by a set of generic traffic priority rules followed by the autonomous robots moving in the same environment. Simulated results for two moving objects in the same navigation space are also presented.     

An affordable modular mobile robotic platform with fuzzy logic control and evolutionary artificial neural networks

Autonomous robotics projects encompass the rich nature of integrated systems that includes mechanical, electrical, and computational software components. The availability of smaller and cheaper hardware components has helped make possible a new dimension in operational autonomy. This paper describes a mobile robotic platform consisting of several integrated modules including a laptop computer that serves as the main control module, microcontroller‐based motion control module, a vision processing module, a sensor interface module, and a navigation module. The laptop computer module contains the main software development environment with a user interface to access and control all other modules. Programming language independence is achieved by using standard input/output computer interfaces including RS‐232 serial port, USB, networking, audio input and output, and parallel port devices. However, with the same hardware technology available to all, the distinguishing factor in most cases for intelligent systems becomes the software design. The software for autonomous robots must intelligently control the hardware so that it functions in unstructured, dynamic, and uncertain environments while maintaining an autonomous adaptability. This paper describes how we introduced fuzzy logic control to one robot platform in order to solve the 2003 Intelligent Ground Vehicle Competition (IGVC) Autonomous Challenge problem. This paper also describes the introduction of hybrid software design that utilizes Fuzzy Evolutionary Artificial Neural Network techniques. In this design, rather than using a control program that is directly coded, the robot's artificial neural net is first trained with a training data set using evolutionary optimization techniques to adjust weight values between neurons. The trained neural network with a weight average defuzzification method was able to make correct decisions to unseen vision patterns for the IGVC Autonomous Challenge. A comparison of the Lawrence Technological University robot designs and the design of the other competing schools shows that our platforms were the most affordable robot systems to use as tools for computer science and engineering education. © 2004 Wiley Periodicals, Inc.     

基于路径跟踪的拖车式移动机器人动态窗口法

针对目前局部路径规划算法只适用于单车体机器人的问题,提出了一种针对拖车式移动机器人的动态窗口法。首先,利用多车体结构的路径跟踪方程实现对拖车式移动机器人的运动控制;然后,利用评价函数同时对牵引车和拖车进行评价并根据权重相加;最后,针对拖车结构特性,添加了运动过程中牵引车与拖车的夹角约束,保证运动轨迹的稳定性。仿真实验表明:拖车式移动机器人的运动控制可满足收敛性,同时所提算法实现了拖车式移动机器人局部路径规划的任务,且在运动过程中夹角变化均未超出限制。该研究对拖车式移动机器人的自主导航有极大的参考价值。     

Probabilistic Road Map sampling strategies for multi-robot motion planning

This paper presents a Probabilistic Road Map (PRM) motion planning algorithm to be queried within Dynamic Robot Networks—a multi-robot coordination platform for robots operating with limited sensing and inter-robot communication.

First, the Dynamic Robot Networks (DRN) coordination platform is introduced that facilitates centralized robot coordination across ad hoc networks, allowing safe navigation in dynamic, unknown environments. As robots move about their environment, they dynamically form communication networks. Within these networks, robots can share local sensing information and coordinate the actions of all robots in the network.

Second, a fast single-query Probabilistic Road Map (PRM) to be called within the DRN platform is presented that has been augmented with new sampling strategies. Traditional PRM strategies have shown success in searching large configuration spaces. Considered here is their application to on-line, centralized, multiple mobile robot planning problems. New sampling strategies that exploit the kinematics of non-holonomic mobile robots have been developed and implemented. First, an appropriate method of selecting milestones in a PRM is identified to enable fast coverage of the configuration space. Second, a new method of generating PRM milestones is described that decreases the planning time over traditional methods. Finally, a new endgame region for multi-robot PRMs is presented that increases the likelihood of finding solutions given difficult goal configurations.

Combining the DRN platform with these new sampling strategies, on-line centralized multi-robot planning is enabled. This allows robots to navigate safely in environments that are both dynamic and unknown. Simulations and real robot experiments are presented that demonstrate: (1) speed improvements accomplished by the sampling strategies, (2) centralized robot coordination across Dynamic Robot Networks, (3) on-the-fly motion planning to avoid moving and previously unknown obstacles and (4) autonomous robot navigation towards individual goal locations.     

基于行为优先与模糊控制的移动机器人导航

由于动态未知环境下自主移动机器人的导航具有较大困难,为实现自主机器人在动态未知环境下的无碰撞运行,文中将行为优先级控制与模糊逻辑控制相结合,提出4种基本行为控制策略:目标寻找、避障、跟踪和解锁.针对'U'型和'V'型障碍物运行解锁问题,提出了行走路径记忆方法,并通过构建虚拟墙来避免机器人再次走入此类区域.仿真实验表明,所提出的控制策略可有效地运用于复杂和未知环境下自主移动机器人的导航,且具有较好的鲁棒性和适应性.     

Planning smooth paths for mobile robots in an unknown environment

This paper addresses the path planning problem for autonomous mobile robots operating in an unknown environment with obstacles. Paths are formed based on third-order Bezier splines and, then, are corrected on the move as a robot detects obstacles with its onboard sensors. During this correction, the initial path between two reference points is divided into two segments (described by Bezier splines) in such a way as to allow the robot to move at a safe distance from a detected obstacle along a smooth resultant trajectory. In this case, the use of smooth paths ensures a high levels of accuracy and velocity of mobile robots during their operation.     

Navigation integration of a mobile robot in dynamic environments

This article presents a design and experimental study of navigation integration of an intelligent mobile robot in dynamic environments. The proposed integration architecture is based on the virtual‐force concept, by which each navigation resource is assumed to exert a virtual force on the robot. The resultant force determines how the robot will move. Reactive behavior and proactive planning can both be handled in a simple and uniform manner using the proposed integration method. A real‐time motion predictor is employed to enable the mobile robot to deal in advance with moving obstacles. A grid map is maintained using on‐line sensory data for global path planning, and a bidirectional algorithm is proposed for planning the shortest path for the robot by using updated grid‐map information. Therefore, the mobile robot has the capacity to both learn and adapt to variations. To implement the whole navigation system efficiently, a blackboard model is used to coordinate the computation on board the vehicle. Simulation and experimental results are presented to verify the proposed design and demonstrate smooth navigation behavior of the intelligent mobile robot in dynamic environments. ©1999 John Wiley & Sons, Inc.     

双臂搬运机器人反应式导航控制系统设计

为增强双臂搬运机器人在作业任务过程中的行进避障能力,使其运动行为得到连续有效控制,设计双臂搬运机器人的反应式导航控制系统。根据单片机与电机电路的连接形式,选择合适的ARM微处理器元件与PIC单片机结构,再联合HN-9移动平台、智能导航平台、ROS操作平台,完善反应式导航子模块的运行能力,实现控制系统的硬件单元设计。求取绝对位姿向量、相对位姿向量的计算结果,以此作为自变量系数,确定速度雅可比指标,并推断得出动力学递推表达式,完成对双臂搬运机器人的协调控制,联合相关硬件应用结构,实现双臂搬运机器人反应式导航控制系统的设计。对比实验结果：反应式导航控制系统可使机器人准确躲避行进障碍物,且躲避过程中机器人完成作业任务的能力不会受到影响,符合连续有效控制机器人搬运行为的实际应用需求。     

基于总体势减小的动态调度技术解决多机器人的路径规划

本文提出了一种解决多机器人路径规划与协调问题的新方法：基于总体势减小的优 先级动态调度策略．文中引入了总体势的概念,机器人从起始点向目标点运动过程中,始终 沿着总体势减小的方向进行,逐步引导机器人导航任务的完成．