A system for self-diagnosis of an autonomous mobile robot using an internal state sensory system: fault detection and coping with the internal condition

Considering that intelligent robotic systems work in a real environment, it is important that they themselves have the ability to determine their own internal conditions. Therefore, we consider it necessary to pay some attention to the diagnosis of such intelligent systems and to construct a system for the self-diagnosis of an autonomous mobile robot. Autonomous mobile systems must have a self-contained diagnostic system and therefore there are restrictions to building such a system on a mobile robot. In this paper, we describe an internal state sensory system and a method for diagnosing conditions in an autonomous mobile robot. The prototype of our internal sensory system consists of voltage sensors, current sensors and encoders. We show experimental results of the diagnosis using an omnidirectional mobile robot and the developed system. Also, we propose a method that is able to cope with the internal condition using internal sensory information. We focus on the functional units in a single robot system and also examine a method in which the faulty condition is categorized into three levels. The measures taken to cope with the faulty condition are set for each level to enable the robot to continue to execute the task. We show experimental results using an omnidirectional mobile robot with a self-diagnosis system and our proposed method.     

Indoor scene recognition by a mobile robot through adaptive object detection

Mobile robotics has achieved notable progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an Office or a Kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as Doors or furniture, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alternative computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and structural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mobile robot navigating in Office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques for scene recognition.     

Region with velocity constraints: map information and its usage for safe motion planning of a mobile robot in a public environment

Recently, various autonomous mobile robots have been developed for practical use. To support the coexistence of robots and humans in real environments, we propose a concept named ‘Region with Velocity Constraints (RVC),’ which is set around hazardous areas. RVCs are regions where the velocities of the robot are constrained to predefined values. Inside the RVCs, the robot has to reduce its translational velocity to avoid predicted hazards such as collisions with obstacles, and to reduce its rotational velocity to prevent undesirable motions such as sharp turns. We also propose a motion planning method for navigating the mobile robot in an environment with RVCs based on the Navigation Function and Global Dynamic Window Approach. Our method generates a trajectory satisfying both translational and rotational velocity constraints to be compatible with the surroundings. Moreover, to demonstrate the validity of our method, we performed numerical simulations and experiments.     

Present state and future of Intelligent Space—Discussion on the implementation of RT in our environment—

In the latest advances in network sensor technology and state-of-the-art mobile robots, artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. “Intelligent Space” is a platform on which we can easily implement advanced technologies to realize smart services for humans. We have developed and reported a vision system based on color information, a hand-over scheme networking multicameras, a human-following mobile robot system, a path generator based on human-watching, etc. Here, I will summarize the present state of intelligent space, and try to describe the future from the viewpoint of system integration. We are now introducing RT (robot technology) to develop intelligent-space as an actual standard platform which could be approved by the robotics community. I will discuss how to use RT in our intelligent space, and show our new results. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Design and implementation of a self-guided indoor robot based on a two-tier localization architecture

We consider building an indoor low-cost mobile robot that can be used in home applications. Due to the complicated nature of home environments, it is essential for such a robot to be self-guided in the sense that it is able to determine its current location as well as navigate to locations where it is commanded to. We propose a two-tier architecture to achieve this goal at centimeter-to-meter-level accuracy. The robot can even roam into an area which is new to it. We demonstrate a prototyping system based on an extended iRobot and the results have important implications on intelligent homes.     

Behavior-based intelligent mobile robot using an immunized reinforcement adaptive learning mechanism

In this paper, a novel immunized reinforcement adaptive learning mechanism employing a behavior-based knowledge and the on-line adapting capabilities of the immune system is proposed and applied to an intelligent mobile robot. Rather than building a detailed mathematical model of immune systems, we try to explore principles in the immune system focusing on its self-organization, adaptive capability and immune memory. Two levels of the immune system, underlying the ‘micro’ level of cell interactions, and emergent ‘macro’ level of the behavior of the system are investigated.To evaluate the proposed immunized architecture, a ‘food foraging work’ simulation environment containing a mobile robot, foods, with/without obstacles is created to simulate the real world. The simulation results validate several significant characteristics of the immunized architecture: adaptability, learning, self-organizing, and stable ecological niche approaching.     

开放式三自由度全方位移动机器人实验平台

随着机器人应用的不断发展，移动机器人逐渐成为一个十分活跃的分支，尤其是移 动机器人作为自主智能控制的实验平台，对其控制系统的开放性提出了越来越高的要求．本 文就我们自行设计的面向用户的移动机器人硬件系统和相应的软件平台框架做一下较全面的 介绍，并着重分析软件控制系统的开放性．本系统作为开放的实验平台和教学机器人是适宜 的．     

An adaptive particle filter for soft fault compensation of mobile robots

Soft fault compensation plays an important role in mobile robot locating, mapping, and navigating. It is difficult to achieve fast and accurate compensation for mobile robots because they are usually highly non-linear, non-Gaussian systems with limited computation and memory resources. An adaptive particle filter is presented to compensate two kinds of soft faults for mobile robots, i.e., noise or factor faults of dead reckoning sensors and slippage of wheels. Firstly, the kinematics models and the fault models are discussed, and five kinds of residual features are extracted to detect soft faults. Secondly, an adaptive particle filter is designed for fault compensation, and two kinds of adaptive scheme are discussed： 1） the noise variances of linear speed and yaw rate are adjusted according to residual features; 2） the particle number is adapted according to Kullback-Leibler divergence （KLD） of two approximate distribution denoted with two particle sets with different particles, i.e., increasing particle number if the KLD is large and decreasing particle number if the KLD is small. The theoretic proof is given and experimental results show the efficiency and accuracy of the presented approach.     

Reactive Visual Navigation Based on Omnidirectional Sensing – Path Following and Collision Avoidance

Described here is a visual navigation method for navigating a mobile robot along a man-made route such as a corridor or a street. We have proposed an image sensor, named HyperOmni Vision, with a hyperboloidal mirror for vision-based navigation of the mobile robot. This sensing system can acquire an omnidirectional view around the robot in real time. In the case of the man-made route, road boundaries between the ground plane and wall appear as a close-looped curve in the image. By making use of this optical characteristic, the robot can avoid obstacles and move along the corridor by tracking the close-looped curve with an active contour model. Experiments that have been done in a real environment are described.     

智能神经元网络时间序列预测模型的研究

人工神经元网络的研究技术在理论和实际应用上已经比较成熟,在信号处理系统中也采用该技术进行非线性时间序列信号的预测分析。但是由于该理论黑箱模型的特点,无法引入先验知识,从而预测精度难以提高。针对该问题,文中提出了智能神经网络的动态预测模型,引入智能神经元,建立区别于传统神经网络的预测模型,达到了较为理想的预测效果。并以工业生产参数的时间序列预测——某油井生产过程中MinCurrent参数值,作为实验模型,对该方法进行了验证,结果表明了该模型预测精度较高、计算速度快。     

Reinforcement based mobile robot navigation in dynamic environment

In this paper, a new approach is developed for solving the problem of mobile robot path planning in an unknown dynamic environment based on Q-learning. Q-learning algorithms have been used widely for solving real world problems, especially in robotics since it has been proved to give reliable and efficient solutions due to its simple and well developed theory. However, most of the researchers who tried to use Q-learning for solving the mobile robot navigation problem dealt with static environments; they avoided using it for dynamic environments because it is a more complex problem that has infinite number of states. This great number of states makes the training for the intelligent agent very difficult. In this paper, the Q-learning algorithm was applied for solving the mobile robot navigation in dynamic environment problem by limiting the number of states based on a new definition for the states space. This has the effect of reducing the size of the Q-table and hence, increasing the speed of the navigation algorithm. The conducted experimental simulation scenarios indicate the strength of the new proposed approach for mobile robot navigation in dynamic environment. The results show that the new approach has a high Hit rate and that the robot succeeded to reach its target in a collision free path in most cases which is the most desirable feature in any navigation algorithm.     

Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment

Learning of an autonomous mobile robot for path generation includes the use of previous experience to obtain the better path within its work space. When the robot is moving in its search space for target seeking, each task requires different form of learning. Therefore, the modeling of an efficient learning mechanism is the hardest problem for an autonomous mobile robot. To solve this problem, the present research work introduced an adaptive learning-based motion planner using artificial immune system, called adaptive immune-based path planner. Later the developed adaptive mechanism has been integrated to the innate immune-based path planner in order to obtain the better results. To verify the effectiveness of the proposed adaptive immune-based motion planner, simulation results as well as experimental results are presented in various unknown environments.     

Position estimation of a mobile robot using images of a moving target in intelligent space with distributed sensors

Latest advances in hardware technology and state-of-the-art of mobile robots and artificial intelligence research can be employed to develop autonomous and distributed monitoring systems. A mobile service robot requires the perception of its present position to co-exist with humans and support humans effectively in populated environments. To realize this, a robot needs to keep track of relevant changes in the environment. This paper proposes localization of a mobile robot using images recognized by distributed intelligent networked devices in intelligent space (ISpace) in order to achieve these goals. This scheme combines data from the observed position, using dead-reckoning sensors, and the estimated position, using images of moving objects, such as a walking human captured by a camera system, to determine the location of a mobile robot. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the ISpace. Using the a priori known path of a moving object and a perspective camera model, the geometric constraint equations that represent the relation between image frame coordinates for a moving object and the estimated robot's position are derived. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot, and the Kalman filtering scheme is used for the estimation of the mobile robot location. The proposed approach is applied for a mobile robot in ISpace to show the reduction of uncertainty in determining the location of a mobile robot, and its performance is verified by computer simulation and experiment.     

Research into the application of AI robots in community home leisure interaction

This paper focuses on comprehensive application of artificial intelligence robots for community-based leisure interaction. We propose a multiple-layer perceptron network to design and implement the intelligent interactive home robot system, which includes establishment of an environment map, autonomous navigation, obstacle-avoidance control and human–machine interaction, to complete the positioning and perception functions required by the robot in the home environment. With this system, community residents use an interactive interface to manipulate robots remotely and create an environmental map. In order for the robot to adapt in this changing environment, the robot needs to have a completely autonomous navigation and obstacle-avoidance-control system. In this study, a long-distance obstacle-avoidance fuzzy system and a short-distance anti-fall obstacle-avoidance fuzzy system were used to enable the robot to accommodate unforeseen changes. This technology proved itself capable of navigating a home environment, ensuring that the robot could instantaneously dodge nearby obstacles and correcting the robot’s path of travel. At the same time, it could prevent the robot from falling off a high dropping point and thereby effectively control the robot’s movement trajectory. After combining the above-mentioned multi-sensor and image recognition functions, the intelligent interactive home robot showed that it clearly has the ability to integrate vision, perception and interaction, and we were able to verify that the robot has the necessary adaptability in changing environments and that the design of such interactive robots can be an asset in the home.

     

Motion planning for mobile robots using a laser range finder

We have designed a mobile robot with a distribution structure for intelligent life space. The mobile robot was constructed using an aluminum frame. The mobile robot has the shape of a cylinder, and its diameter, height, and weight are 40 cm, 80 cm, and 40 kg, respectively. There are six systems in the mobile robot, including structure, an obstacle avoidance and driving system, a software development system, a detection module system, a remote supervision system, and others. In the obstacle avoidance and driving system, we use an NI motion control card to drive two DC servomotors in the mobile robot, and detect obstacles using a laser range finder and a laser positioning system. Finally, we control the mobile robot using an NI motion control card and a MAXON driver according to the programmed trajectory. The mobile robot can avoid obstacles using the laser range finder, and follow the programmed trajectory. We developed the user interface with four functions for the mobile robot. In the security system, we designed module-based security devices to detect dangerous events and transmit the detection results to the mobile robot using a wireless RF interface. The mobile robot can move to the event position using the laser positioning system.     

Multiple camera image interface for assisting in the control of mobile robots

A simple interface is described that makes it easier for operators of mobile robots to use multiple images provided by distributed cameras. While the images provided by multiple cameras located on ceilings or walls can provide global position information for use in navigating mobile robots, a human operator must be able to understand the working area by observing the multiple images, which is burdensome. Accounting for this, we propose a simple interface for operating the mobile robot through multiple camera images. To assist human recognition of the relationship between multi-displayed images from cameras located in unknown wide working areas, we designed and implemented the interface to have the following main features: (i) indication of overlap areas from cameras to support human recognition of working areas and (ii) indication of rough routes for robot navigation to assist robot operation from captured images. The effectiveness of the interface and that of the entire robot system were demonstrated experimentally.     

Distributing a robotic system on a network: the ETHNOS approach

Cognitive activity in intelligent robotic systems has often been modeled as a set of communicating intelligent distributed agents or modules. Some examples in this field are blackboard architectures, hybrid models or subsumption architectures. The rapid progress of communication technology offers the possibility of distributing computation not only on different processes but on a network of computers. This both results in greater available computational power and it allows the robot to merge with the environment it operates in. In suitable intelligent buildings a mobile robot may open doors, turn on/off lights or even avoid obstacles based not only on its sensors and actuators but on the interaction with other robotic entities. In addition the range of robot interactions is now only limited by the network and thus the robot can operate remotely on the environment. Similarly, users can issue commands to remote robots and receive feedback in real-time. In this paper we propose a global approach to distributing a robotic system over a computer network. The approach is named ETHNOS (Expert Tribe in a Hybrid Network Operating System) because it is based on a novel operating system we developed specifically for distributed intelligent robotics. The paper focuses on its characteristics that make it well suited for network robotics applications. It also illustrates an example of a real application in the field of mobile robotics.     

面向多区域扫描的机器人作业序列规划与自主避障研究

针对移动式机器人多区域扫描作业任务,本文基于人工智能思想研究机器人作业序列规划和自主避障方法,从而保证机器人自主完成使命任务。采用激光测距系统LMS200作为移动机器人环境测量传感器,开发了机器人与LMS200的串口通信程序;提出采用人工智能遍历算法实现机器人自主作业序列规划,保证规划结果在时间上是最优的;针对复杂环境中存在未知障碍的问题,设计了基于人工势场法的机器人自主避障算法,并通过仿真试验进行了算法验证,结果表明：本文所研究方法是有效且可行的。     

New Potential Functions with Random Force Algorithms Using Potential Field Method

Autonomous mobile robot path planning is a common topic for robotics and computational geometry. Many important results have been found, but a lot of issues are still veiled. This paper first describes new problem of symmetrically aligned robot-obstacle-goal (SAROG) when using potential field methods for mobile robot path planning. In addition, we consider constant robot speed for practical use. The SAROG and the constant speed involve two potential risks: robot-obstacle collision and local minima trap. For dealing with the two potential risks, we analyze the conditions of the collision and the local minima trap, and propose new potential functions and random force based algorithms. For the algorithm verification, we use WiRobot X80 with three ultrasonic range sensor modules.     

基于深度强化学习的移动机器人动态路径规划算法

为了在复杂舞台环境下使用移动机器人实现物品搬运或者载人演出,提出了一种基于深度强化学习的动态路径规划算法。首先通过构建全局地图获取移动机器人周围的障碍物信息,将演员和舞台道具分别分类成动态障碍物和静态障碍物。然后建立局部地图,通过LSTM网络编码动态障碍物信息,使用社会注意力机制计算每个动态障碍物的重要性来实现更好的避障效果。通过构建新的奖励函数来实现对动静态障碍物的不同躲避情况。最后通过模仿学习和优先级经验回放技术来提高网络的收敛速度,从而实现在舞台复杂环境下的移动机器人的动态路径规划。实验结果表明,该网络的收敛速度明显提高,在不同障碍物环境下都能够表现出好的动态避障效果。