INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.     

Guidance of a mobile robot using an array of static cameras located in the environment

This paper presents a new proposal for positioning and guiding mobile robots in indoor environments. The proposal is based on the information provided by static cameras located in the movement environment. This proposal falls within the scope of what are known as intelligent environments; in this case, the environment is provided with cameras that, once calibrated, allow the position of the robots to be obtained. Based on this information, control orders for the robots can be generated using a radio frequency link. In order to facilitate identification of the robots, even under extremely adverse ambient lighting conditions, a beacon consisting of four circular elements constructed from infrared diodes is mounted on board the robots. In order to identify the beacon, an edge detection process is carried out. This is followed by a process that, based on the algebraic distance, obtains the estimated ellipses associated with each element of the beacon. Once the beacon has been identified, the coordinates of the centroids for the elements that make up the beacon are obtained on the various image planes. Based on these coordinates, an algorithm is proposed that takes into account the standard deviation of the error produced in the various cameras in ascertaining the coordinates of the beacon’s elements. An odometric system is also used in guidance that, in conjunction with a Kalman Filter, allows the position of the robot to be estimated during the time intervals required to process the visual information provided by the cameras.

室外未知环境下的AGV地貌主动探索感知

智能机器人对复杂地貌环境的识别一直是机器人应用领域研究的前沿问题,移动机器人在不同的地貌上采取的运动方式并非一成不变,所以选择的运动方式对于迅速准确识别所处地貌的类型至关重要。针对该问题本文提出了一种基于贝叶斯框架的主动感知探索方法,使移动机器人能够主动探索有兴趣的运动方式并且感知识别和运动之间的匹配关系,可以优化在地貌识别之中的模糊不确定性;为了进一步验证实验的可靠性,还使用了被动感知策略来比较和分析不同策略之间的差异。实验结果表明:主动感知方法能够规划出有效的地貌识别动作序列,能够引导移动机器人主动感知目标地貌,该框架对于室外未知环境下主动感知后的地貌识别效果优于被动感知。     

Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.     

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.     

Multiple-Target Tracking by Spatiotemporal Monte Carlo Markov Chain Data Association

We propose a framework for tracking multiple targets, where the input is a set of candidate regions in each frame, as obtained from a state-of-the-art background segmentation module, and the goal is to recover trajectories of targets over time. Due to occlusions by targets and static objects, as also by noisy segmentation and false alarms, one foreground region may not correspond to one target faithfully. Therefore, the one-to-one assumption used in most data association algorithms is not always satisfied. Our method overcomes the one-to-one assumption by formulating the visual tracking problem in terms of finding the best spatial and temporal association of observations, which maximizes the consistency of both motion and appearance of trajectories. To avoid enumerating all possible solutions, we take a data-driven Markov Chain Monte Carlo (DD-MCMC) approach to sample the solution space efficiently. The sampling is driven by an informed proposal scheme controlled by a joint probability model combining motion and appearance. Comparative experiments with quantitative evaluations are provided.     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

Knee rehabilitation using an intelligent robotic system

There is an increasing trend in using robots for medical purposes. One specific area is the rehabilitation. There are some commercial exercise machines used for rehabilitation purposes. However, these machines have limited use because of their insufficient motion freedom. In addition, these types of machines are not actively controlled and therefore can not accommodate complicated exercises required during rehabilitation. In this study, a rule based intelligent control methodology is proposed to imitate the faculties of an experienced physiotherapist. These involve interpretation of patient reactions, storing the information received, acting according to the available data, and learning from the previous experiences. Robot manipulator is driven by a servo motor and controlled by a computer using force/torque and position sensor information. Impedance control technique is selected for the force control.     

Towards autonomous robotic servicing: Using an integrated hand-arm-eye system for manipulating unknown objects

Executing complex robotic tasks including dexterous grasping and manipulation requires a combination of dexterous robots, intelligent sensors and adequate object information processing. In this paper, vision has been integrated into a highly redundant robotic system consisting of a tiltable camera and a three-fingered dexterous gripper both mounted on a puma-type robot arm. In order to condense the image data of the robot working space acquired from the mobile camera, contour image processing is used for offline grasp and motion planning as well as for online supervision of manipulation tasks. The performance of the desired robot and object motions is controlled by a visual feedback system coordinating motions of hand, arm and eye according to the specific requirements of the respective situation. Experiences and results based on several experiments in the field of service robotics show the possibilities and limits of integrating vision and tactile sensors into a dexterous hand-arm-eye system being able to assist humans in industrial or servicing environments.     

Integrated tracking and accident avoidance system for mobile robots

In the intelligent transportation field, various accident avoidance techniques have been applied. One of the most common issues with these is the collision, which remains an unsolved problem. To this end, we developed a Collision Warning and Avoidance System (CWAS), which was implemented in the wheeled mobile robot. Path planning is crucial for a mobile robot to perform a given task correctly. Here, a tracking system for mobile robots that follow an object is presented. Thus, we implemented an integrated tracking system and CWAS in a mobile robot. Both systems can be activated independently. Using the CWAS, the robot is controlled through a remotely controlled device, and collision warning and avoidance functions are performed. Using the tracking system, the robot performs tasks autonomously and maintains a constant distance from the followed object. Information on the surroundings is obtained through range sensors, and the control functions are performed through the microcontroller. The front, left, and right sensors are activated to track the object, and all the sensors are used for the CWAS. The proposed system was tested using the binary logic controller and the Fuzzy Logic Controller (FLC). The efficiency of the robot was improved by increasing the smoothness of motion via the FLC, achieving accuracy in tracking and increasing the safety of the CWAS. Finally, simulations and experimental outcomes have shown the usefulness of the system.     

Remote navigation of a mobile robot in an RFID-augmented environment

In the current article, we address the problem of constructing radiofrequency identification (RFID)-augmented environments for mobile robots and the issues related to creating user interfaces for efficient remote navigation with a mobile robot in such environments. First, we describe an RFID-based positioning and obstacle identification solution for remotely controlled mobile robots in indoor environments. In the robot system, an architecture specifically developed by the authors for remotely controlled robotic systems was tested in practice. Second, using the developed system, three techniques for displaying information about the position and movements of a remote robot to the user were compared. The experimental visualization techniques displayed the position of the robot on an indoor floor plan augmented with (1) a video view from a camera attached to the robot, (2) display of nearby obstacles (identified using RFID technology) on the floor plan, and (3) both features. In the experiment, test subjects controlled the mobile robot through predetermined routes as quickly as possible avoiding collisions. The results suggest that the developed RFID-based environment and the remote control system can be used for efficient control of mobile robots. The results from the comparison of the visualization techniques showed that the technique without a camera view (2) was the fastest, and the number of steering motions made was smallest using this technique, but it also had the highest need for physical human interventions. The technique with both additional features (3) was subjectively preferred by the users. The similarities and differences between the current results and those found in the literature are discussed.     

Localization With Limited Sensing

Localization is a fundamental problem for many kinds of mobile robots. Sensor systems of varying ability have been proposed and successfully used to solve the problem. This paper probes the lower limits of this range by describing three extremely simple robot models and addresses the active localization problem for each. The robot, whose configuration is composed of its position and orientation, moves in a fully-known, simply connected polygonal environment. We pose the localization task as a planning problem in the robot's information space, which encapsulates the uncertainty in the robot's configuration. We consider robots equipped with: 1) angular and linear odometers; 2) a compass and contact sensor and; 3) an angular odometer and contact sensor. We present localization algorithms for models 1 and 2 and show that no algorithm exists for model 3. An implementation with simulation examples is presented.     

群集系统的软控制

考虑欧氏空间中群集动态系统的"软控制"问题,即在不改变群集系统中个体间现有规则的条件下,通过增加一个或多个可控智能体干扰群集个体的行为,使群集中心转移到期望的位置.给出了可控智能体的控制规则,并利用粒子群(PSO)算法讨论了群集系统软控制的最优收敛问题.实例研究表明了"软控制"在群集系统中的可行性.     

基于认知模型的室内移动服务机器人人机耦合协同作业机制

针对老年人和残疾人这类特殊用户群体与服务机器人构成的人机智能系统,提出了基于ACT-R(理性思维的适应性控制)认知架构模型的室内移动服务机器人人机耦合协同作业机制.基于ACT-R认知架构对人机一体化室内移动服务机器人人机协同作业系统进行了总体设计,利用简单自然的人机效应通道,设计了基于ACT-R认知架构的人机耦合界面;通过人-机-环境空间感知耦合,提出并建立了室内移动服务机器人人机一体化协同决策作业机制.最后在室内环境下进行移动服务机器人人机协同作业实验,系统安全高效地完成了作业任务,验证了该机制的有效性.     

四轮移动机器人的前后轮双里程仪系统的误差分析

四轮移动机器人的驾驶和驱动系统的左右轮分别装上里程仪，里程仪的读数反映了车轮转过的角度和转向，在车轮直径固定的情况下，便可计算出车轮的移动距离．根据左右车轮的记数值，可以计算出车体的位置和方位．本文对前后轮双里程仪系统进行了对比分析，在相同的误差下，驱动轮双里程仪系统优于前轮驾驶角和双里程仪器系统，这对实际工作有指导作用．     

移动机器人路径规划综述

移动机器人是目前科学技术发展最活跃的领域之一,在工业、农业、医疗等行业广泛应用,还在城市安全、国防和空间探测领域得到更广的应用。要实现机器人在未知环境下自主作业,具备实时、自主、识别高风险区域和风险管理的能力,路径规划是一个重要环节,规划水平的高低,在一定程度上标志着机器人的智能水平,因此研究机器人路径规划对提高机器人的智能化水平、加快工程化应用具有重要的意义。文章重点分别从全局路径规划和局部路径规划角度对机器人路径规划的研究方法进行了分析与总结,同时分析研究了基于仿生学的智能算法的遗传算法、蚁群算法、粒子群算法,最后展望了移动机器人的未来发展趋势。     

Motion segmentation and qualitative dynamic scene analysis from an image sequence

This article deals with analysis of the dynamic content of a scene from an image sequence irrespective of the static or dynamic nature of the camera. The tasks involved can be the detection of moving objects in a scene observed by a mobile camera, or the identification of the movements of some relevant components of the scene relatively to the camera. This problem basically requires a motion-based segmentation step. We present a motion-based segmentation method relying on 2-D affine motion models and a statistical regularization approach which ensures stable motion-based partitions. This can be done without the explicit estimation of optic flow fields. Besides these partitions are linked in time. Therefore, the motion interpretation process can be performed on more than two successive frames. The ability to follow a given coherently moving region within an interval of several images of the sequence makes the interpretation process more robust and more comprehensive. Identification of the kinematic components of the scene is induced from an intermediate layer accomplishing a generic qualitative motion labeling. No 3-D measurements are required. Results obtained on several real-image sequences corresponding to complex outdoor situations are reported.     

全区域覆盖移动机器人无标识工作环境的边界建立与识别

针对工作在户外环境中的全区域覆盖移动机器人,提出在无人为标识的工作环境中,建立边界和识别边界的新方案. 设计组合传感器定位系统实现工作区域的边界建立和识别. 基于分段策略建立工作边界地图,并对其性能与连续获取策略进行比较分析. 设计RBF神经网络以实时获取精确的定位信息. 实验证明, 定位系统和方案能够保证工作区域边界的准确建立和成功识别,机器人能顺利完成全区域覆盖任务,而且具有一定的工程实用性.