Path planning of fire-escaping system for intelligent building

We present the path-planning techniques of the fire-escaping system for intelligent building, and use multiple mobile robots to present the experimental scenario. The fire-escaping system contains a supervised computer, an experimental platform, some fire-detection robots and some navigation robots. The mobile robot has the shape of a cylinder, and its diameter, height and weight are 10?cm, 15?cm and 1.5?kg, respectively. The mobile robot contains a controller module, two DC servomotors (including drivers), three IR sensor modules, a voice module and a wireless RF module. The controller of the mobile robot acquires the detection signals from reflective IR sensors through I/O pins and receives the command from the supervised computer via wireless RF interface. The fire-detection robot carries the flame sensor to detect fire sources moving on the grid-based experiment platform, and calculates the more safety escaping path using piecewise cubic Bezier curve on all probability escaping motion paths. Then the user interface uses A* searching algorithm to program escaping motion path to approach the Bezier curve on the grid-based platform. The navigation robot guides people moving to the safety area or exit door using the programmed escaping motion path. In the experimental results, the supervised computer programs the escaping paths using the proposed algorithms and presents movement scenario using the multiple smart mobile robots on the experimental platform. In the experimental scenario, the user interface transmits the motion command to the mobile robots moving on the grid-based platform, and locates the positions of fire sources by the fire-detection robots. The navigation robot guides people leaving the fire sources using the low-risk escaping motion path and moves to the exit door.     

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.     

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.     

Developing a module-based security system for an intelligent home

The security system in a workplace or home is important to human life. Unlucky events are often caused by the negligence of humans. We have developed a modulebased security system for home automation. The structure of the security system contains many modules. Each module has two types of interface (wireless RF and speech). There are active and passive modules in the security system. The active security module is a smart robot. We have designed many types of smart robot for the security system. The passive security modules include a fire security module, an intruder security module, an environment security module, a gas security module, an AC power security module, and an appliance control module. In the security module, we use multisensor fusion algorithms to decide the exact output. In these modules, we use a two-wire communication method through the wireless RF interface, and a voice alarm for serious events, and transmit the real-time status to the supervised computer. In the smart robot system, we have designed many types of smart robot for the security system. We have designed a general user interface (GUI) for the intelligent security system. The user interface can supervise these modules and the smart robots via the wireless RF device, and supervise the security system via wireless, Internet, and cell phone.     

Multi-robot-based intelligent security system

This article describes a multiple security module-based intelligent security system that has multiple communication interfaces which can be applied in home automation. The interfaces of the intelligent security system contain wired RS485, wireless RF, and Internet. The detection modules of the system have both active and passive security modules. The passive security modules contain wired security modules and wireless security modules. The control unit of all security modules is a HOLTEK microchip. Each security module has two different interfaces. They use voice modules to alarm users of an event, and to transmit real-time event signals to the supervising computer via the wired RS485 or wireless RF interface. If an event occurs, the supervising computer calculates its belief values using Dempster-Shafter evidence theory according to the passive wired and wireless security modules. If the belief value is over a set threshold, the supervising computer commands the mobile robot to move to the event location, and receives a signal from the mobile robot via the wireless RF interface. The supervising computer recognizes the final decision output using Dempster-Shafter evidence theory, and displays the detection and decision output values on the monitor of the user interface. Finally, we present some experimental results using wired passive security modules, wireless passive security modules, and active security modules for fire detection and gas leakage detection using the experimental platform of the intelligent security system.     

Formation path following control of unicycle-type mobile robots

This paper presents a control strategy for the coordination of multiple mobile robots. A combination of the virtual structure and path following approaches is used to derive the formation architecture. A formation controller is proposed for the kinematic model of two-degree-of-freedom unicycle-type mobile robots. The approach is then extended to consider the formation controller by taking into account the physical dimensions and dynamics of the robots. The controller is designed in such a way that the path derivative is left as a free input to synchronize the robot’s motion. Simulation results with three robots are included to show the performance of our control system. Finally, the theoretical results are experimentally validated on a multi-robot platform.     

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.     

多机器人主—从行星式编队控制

研究了二阶积分器描述的多机器人主—从行星式编队控制问题,提出了将多机器人编队分解为每个机器人对各自具有时变速度的虚拟机器人的跟踪控制,使得每个机器人相对于虚拟机器人的位置与速度跟踪误差收敛为零且彼此不相碰撞,此时编队系统收敛到理想队形.在统一的算法框架下,分别实现了跟随者以领航者为中心的公转运动编队(revolution formation,RF)模式和跟随者与领航者保持期望距离、期望速度的编队(desiredformation,DF)模式.公转运动编队(RF)模式适用于异构多机器人系统的环境探索任务;保持期望距离、期望速度的编队(DF)模式适用于自主水下机器人(AUV)、无人机(UAV)等合作与协调任务.应用李亚普诺夫稳定性理论对控制算法的稳定性进行了分析,并通过计算机仿真验证了该方法的有效性.     

A decentralized approach for the conflict-free motion of multiple mobile robots

This article presents a novel approach to decentralized motion planning and conflict-resolution for multiple mobile robots. The proposed multi-robot motion planning is an on-line operation, based on cost wave propagation within a discretized configuration space-time. By use of the planning method, a framework for negotiation is developed, which permits quick decentralized and parallel decision making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multi-robot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.     

A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations

This paper presents a distributed smooth time-varying feedback control law for coordinating motions of multiple nonholonomic mobile robots of the Hilare-type to capture/enclose a target by making troop formations. This motion coordination is a cooperative behavior for security against invaders in surveillance areas. Each robot in this control law has its own coordinate system and it senses a target/invader, other robots and obstacles, to achieve this cooperative behavior without making any collision. Each robot especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this control law is supported by computer simulations.     

Incremental statistical learning for integrating motion primitives and language in humanoid robots

We describe a novel approach that allows humanoid robots to incrementally integrate motion primitives and language expressions, when there are underlying natural language and motion language modules. The natural language module represents sentence structure using word bigrams. The motion language module extracts the relations between motion primitives and the relevant words. Both the natural language module and the motion language module are expressed as probabilistic models and, therefore, they can be integrated so that the robots can both interpret observed motion in the form of sentences and generate the motion corresponding to a sentence command. Incremental learning is needed for a robot that develops these linguistic skills autonomously . The algorithm is derived from optimization of the natural language and motion language modules under constraints on their probabilistic variables such that the association between motion primitive and sentence in incrementally added training pairs is strengthened. A test based on interpreting observed motion in the forms of sentence demonstrates the validity of the incremental statistical learning algorithm.     

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。     

移动机器人的队形控制及其主动避障

在这篇论文中, 我们利用一个统一的算法框架来解决移动机器人的队形控制和主动避障问题, 使得编队中的从机器人在避开障碍物的同时, 能够与被跟踪的主机器人保持期望的相对距离或相对方位. 在现有的关于主—从跟踪编队控制的文献中, 为了实现对主机器人快速准确的跟踪, 从机器人在跟踪控制时需要主机器人在惯性坐标系下的绝对运动速度作为队形跟踪控制器的输入. 然而, 在一些环境中, 主机器人的绝对运动状态很难获得. 这里, 我们将利用主—从机器人之间的相对速度来建立机器人编队系统的运动学模型. 基于这个模型的编队控制方法将不再需要测量主机器人的绝对运动速度. 进一步地, 上述的建模和控制方法被扩展为一个移动机器人的动态避障方法, 该方法利用机器人与障碍物之间相对运动状态作为避障控制器的信息输入. 利用由三个非完整移动机器人组成的多机器人系统, 验证了所提出编队控制方法的有效性.     

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.     

多移动机器人组成任意队形的控制研究

本文将多移动机器人组成队形的整体行为分解为机器人个体分别向对应目标点运动的子行为加以研究．提出了基于两层监控模式的分布式控制框架，监控模块作为统一的指挥协调中枢，确定各机器人合理的对应目标点，避免机器人在目标区域内绕路，使得多移动机器人能够快速流畅地组成任意队形．实验证明了控制方法的有效性．     

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.     

Knowledge model-based heterogeneous multi-robot system implemented by a software platform

In order to build a heterogeneous multi-robot system that can be regarded as a primitive prototype of a future symbiotic autonomous human-robot system, this paper presents a knowledge model-based heterogeneous multi-robot system implemented by a software platform. With using frame-based knowledge representation, a knowledge model is constructed to describe the features of heterogeneous robots as well as their behaviors according to human requests. The required knowledge for constructing a heterogeneous multi-robot system can be therefore integrated in a single model and shared by robots and users. Based on the knowledge model, the heterogeneous multi-robot system is defined in the Software Platform for Agents and Knowledge Management (SPAK) by use of XML format. With the use of SPAK, the cooperative operation of heterogeneous multi-robot system can be flexibly carried out. The proposed system not only can integrate heterogeneous robots and various techniques for robots, but also can automatically perform human-robot interaction and plan robot behaviors taking into account different intelligence of robots corresponding to human requests. In this paper, an actual heterogeneous multi-robot system comprised by humanoid robots (Robovie, PINO), mobile robot (Scout) and entertainment robot dog (AIBO) is built and the effectiveness of the proposed system is verified by experiment.     

Conflict free coordinated path planning for multiple robots using a dynamic path modification sequence

In this paper, a practically viable approach for conflict free, coordinated motion planning of multiple robots is proposed. The presented approach is a two phase decoupled method that can provide the desired coordination among the participating robots in offline mode. In the first phase, the collision free path with respect to stationary obstacles for each robot is obtained by employing an A* algorithm. In the second phase, the coordination among multiple robots is achieved by resolving conflicts based on a path modification approach. The paths of conflicting robots are modified based on their position in a dynamically computed path modification sequence (PMS). To assess the effectiveness of the developed methodology, the coordination among robots is also achieved by different strategies such as fixed priority sequence allotment for motion of each robot, reduction in the velocities of joints of the robot, and introduction of delay in starting of each robot. The performance is assessed in terms of the length of path traversed by each robot, time taken by the robot to realize the task and computational time. The effectiveness of the proposed approach for multi-robot motion planning is demonstrated with two case studies that considered the tasks with three and four robots. The results obtained from realistic simulation of multi-robot environment demonstrate that the proposed approach assures rapid, concurrent and conflict free coordinated path planning for multiple robots.     

Master-followed Multiple Robots Cooperation SLAM Adapted to Search and Rescue Environment

The master-followed multiple robots interactive cooperation simultaneous localization and mapping (SLAM) schemes were designed in this paper, which adapts to search and rescue (SAR) cluttered environments. In our multi-robots SLAM, the proposed algorithm estimates each of multiple robots’ current local sub-map, in this occasion, a particle represents each of moving multi-robots, and simultaneously, also represents the pose of a motion robot. The trajectory of the robot’s movement generated a local sub-map; the sub-maps can be looked on as the particles. Each robot efficiently forms a local sub-map; the global map integrates over these local sub-maps; identifying SAR objects of interest, in which, each of multi-robots acts as local-level features collector. Once the object of interest (OOI) is detected, the location in the global map could be determined by the SLAM. The designed multi-robot SLAM architecture consists of PC remote control center, a master robot, and multi-followed robots. Through mobileRobot platform, the master robot controls multi-robots team, the multiple robots exchange information with each other, and then performs SLAM tasks; the PC remote control center can monitor multi-robot SLAM process and provide directly control for multi-robots, which guarantee robots conducting safety in harsh SAR environments. This SLAM method has significantly improved the objects identification, area coverage rate and loop-closure, and the corresponding simulations and experiments validate the significant effects.     

Robot Navigation Based on Discrimination of Artificial Fields: Application to Single Robots

The paper introduces a method for local navigation of mobile robots based on the discrimination of multiple artificial fields, which correspond to targets, obstacles, robots and, if this is the case, robot collectives. Instead of just adding up all potentials, the robot discerns the pertinent potentials at its location and applies a set of motion decisions at each moment. Satisfactory results are obtained. This is the first paper of a more extensive work dealing with individual robots, unorganized groups of robot and robot formations. Here, the method is introduced, with examples for a single robot and for several independent robots.