Control of a nonholonomic mobile robot using an RBF network

This article deals with handling unknown factors, such as external disturbance and unknown dynamics, for mobile robot control. We propose a radial-basis function (RBF) network-based controller to compensate for these. The stability of the proposed controller is proven using the Lyapunov function. To show the effectiveness of the proposed controller, several simulation results are presented. Through the simulations, we show that the proposed controller can overcome the modelling uncertainty and the disturbances. The proposed RBF controller also outperforms previous work from the viewpoint of computation time, which is a crucial fact for real-time applications.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Classification of reflectors with an ultrasonic sensor for mobile robot applications

This paper describes a sensor model made up of four ultrasonic transducers able to classify reflectors (wall, edge or corner) in specular environments. The main goal has been to effect the classification from a single reading cycle: emission of ultrasounds and reception of echoes (measuring only times of flight—TOFs). Working from the four TOFs obtained after a single emission of ultrasounds (thereby facilitating its practical implementation in a mobile robot, when readings are taken while the robot is moving), an algorithm has been proposed for discriminating between edge and plane type reflectors. The configuration of the four transducers enabled dependent discriminating functions to be determined directly from the quadratic terms of the TOFs, without the need for previous geometric transformations. Special attention was given to the effect of the separation between the sensor transducers and the reading-associated noise. Finally, some considerations have been pointed out about the possibility of two transducers emitting, so allowing discrimination between walls and corners.     

Intelligent control for the vision-based indoor navigation of an A-life mobile robot

Realizing steady and reliable navigation is a prerequisite for a mobile robot, but this facility is often weakened by an unavoidable slip or some irreparable drift errors of sensors in long-distance navigation. Although perceptual landmarks were solutions to such problems, it is impossible not to miss landmarks occasionally at some specific spots when the robot moves at different speeds, especially at higher speeds. If the landmarks are put at random intervals, or if the illumination conditions are not good, the landmarks will be easier to miss. In order to detect and extract artificial landmarks robustly under multiple illumination conditions, some low-level but robust image processing techniques were implemented. The moving speed and self-location were controlled by the visual servo control method. In cases where a robot suddenly misses some specific landmarks when it is moving, it will find them again in a short time based on its intelligence and the inertia of the previous search motion. These methods were verified by the reliable vision-based indoor navigation of an A-life mobile robot.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Timing control of the mobile robot using tau-margin

Recently, autonomous robots which are designed on the basis of biological mechanism have attracted much attention. In this paper, we focus on the mechanism of timing control studied by ecological psychology, and apply the framework to timing control of a mobile robot. Experiments using real robots have been conducted and effective behaviors have been realized. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

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。     

Systematic robustness analysis of least squares mobile robot velocity estimation using a regular polygonal optical mouse array

This paper presents the robust velocity estimation of an omnidirectional mobile robot using a regular polygonal array of optical mice that are installed at the bottom of a mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived, from which the least squares estimation of the mobile robot velocity is obtained as the simple average of the optical mouse velocity readings. Second, it is shown that a redundant number of optical mice contributes to the robustness of the least squares mobile robot velocity estimation against both measurement noises and partial malfunction of optical mice. Third, the sensitivity analysis of the least squares mobile robot velocity estimation to imprecise installation of optical mice is made, from which a practical method of optical mouse position calibration is devised. Finally, some experimental results using commercial optical mice are given to demonstrate the validity and performance of the proposed mobile robot velocity estimation. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Localization of a mobile robot based on an ultrasonic sensor using dynamic obstacles

In this article, we propose a localization scheme for a mobile robot based on the distance between the robot and moving objects. This method combines the distance data obtained from ultrasonic sensors in a mobile robot, and estimates the location of the mobile robot and the moving object. The movement of the object is detected by a combination of data and the object’s estimated position. Then, the mobile robot’s location is derived from the a priori known initial state. We use kinematic modeling that represents the movement of a robot and an object. A Kalman-filtering algorithm is used for addressing estimation error and measurement noise. Throughout the computer simulation experiments, the performance is verified. Finally, the results of experiments are presented and discussed. The proposed approach allows a mobile robot to seek its own position in a weakly structured environment. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Indoor navigation of a non-holonomic mobile robot using a visual memory

When navigating in an unknown environment for the first time, a natural behavior consists on memorizing some key views along the performed path, in order to use these references as checkpoints for a future navigation mission. The navigation framework for wheeled mobile robots presented in this paper is based on this assumption. During a human-guided learning step, the robot performs paths which are sampled and stored as a set of ordered key images, acquired by an embedded camera. The set of these obtained visual paths is topologically organized and provides a visual memory of the environment. Given an image of one of the visual paths as a target, the robot navigation mission is defined as a concatenation of visual path subsets, called visual route. When running autonomously, the robot is controlled by a visual servoing law adapted to its nonholonomic constraint. Based on the regulation of successive homographies, this control guides the robot along the reference visual route without explicitly planning any trajectory. The proposed framework has been designed for the entire class of central catadioptric cameras (including conventional cameras). It has been validated onto two architectures. In the first one, algorithms have been implemented onto a dedicated hardware and the robot is equipped with a standard perspective camera. In the second one, they have been implemented on a standard PC and an omnidirectional camera is considered.

A novel methodology for optimal single mobile robot scheduling using whale optimization algorithm

One of the fundamental requirements for creating an intelligent manufacturing environment is to develop a reliable, efficient and optimally scheduled material transport system. Besides traditional material transport solutions based on conveyor belts, industrial trucks, or automated guided vehicles, nowadays intelligent mobile robots are becoming widely used to satisfy this requirement. In this paper, the authors analyze a single mobile robot scheduling problem in order to find an optimal way to transport raw materials, goods, and parts within an intelligent manufacturing system. The proposed methodology is based on biologically inspired Whale Optimization Algorithm (WOA) and is aimed to find the optimal solution of the nondeterministic polynomial-hard (NP-hard) scheduling problem. The authors propose a novel mathematical model for the problem and give a mathematical formulation for minimization of seven fitness functions (makespan, robot finishing time, transport time, balanced level of robot utilization, robot waiting time, job waiting time, as well as total robot and job waiting time). This newly developed methodology is extensively experimentally tested on 26 benchmark problems through three experimental studies and compared to five meta-heuristic algorithms including genetic algorithm (GA), simulated annealing (SA), generic and chaotic Particle Swarm Optimization algorithm (PSO and cPSO), and hybrid GA–SA algorithm. Furthermore, the data are analyzed by using the Friedman statistical test to prove that results are statistically significant. Finally, generated scheduling plans are tested by Khepera II mobile robot within a laboratory model of the manufacturing environment. The experimental results show that the proposed methodology provides very competitive results compared to the state-of-art optimization algorithms.     

A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behaviour-based mobile robot

A virtual target approach is proposed for resolving the limit cycle problem in navigation of a behaviour-based mobile robot. Starting from the onset point of a possible limit cycle path, the real target is switched to a virtual location and the robot is navigated according to the virtual target set up temporarily and the real environment information sensed, until a switching-back condition is reached. The cause of the limit cycle is analysed and the abrupt change in target orientations at two consecutive reaction instants is then identified to be the condition for shifting the target from the real location to the virtual one. The condition for switching back to the real target is established using a specific change in the obstacle information sensed. The algorithm is described together with some particular considerations in implementation. Efficiency and effectiveness of the proposed approach are verified through simulation and experiments conducted with a Nomad 200 robot incorporating a fuzzy behaviour-based controller.     

Space and time sensor fusion using an active camera for mobile robot navigation

We propose a sensor-fusion technique where the data sets for previous moments are properly transformed and fused into the current data sets to allow accurate measurements, such as the distance to an obstacle or the location of the service robot itself. In conventional fusion schemes, measurements are dependent on the current data sets. As a result, more sensors are required to measure a certain physical parameter or to improve the accuracy of a measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized to improve the measurements. The theoretical basis is illustrated by examples, and the effectiveness is proved through simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment and a structured environment.This work was presented in part at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24–26, 2003     

Development of a stair-climbing mobile robot with legs and wheels

This paper deals with the development of a stair-climbing mobile robot with legs and wheels. The main technical issues in developing this type of robot are the stability and speed of the robot while climbing stairs. The robot has two wheels in the front of the body to support its weight when it moves on flat terrain, and it also has arms between the wheels to hook onto the tread of stairs. There are two pairs of legs in the rear of the body. Using not only the rorational torque of the arms and the wheels, but also the force of the legs, the robot goes up and down stairs. It measures the size of stairs when going up and down the first step, and therefore the measurement process does not cause this robot to lose any time. The computer which controls the motion of the robot needs no complicated calculations as other legged robots do. The mechanism of this robot and the control algorithm are described in this paper. This robot will be developed as a wheelchair with a stair climbing mechanism for disabled and elderly people in the near future. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Mobile robot localization using a self-organized visual environment representation

Owing to the upcoming applications in the field of service robotics mobile robots are currently receiving increasing attention in industry and the scientific community. Applications in the area of service robotics demand a high degree of system autonomy, which robots without learning capabilities will not be able to meet. Learning is required in the context of action models and appropriate perception procedures. In both areas flexible adaptivity is difficult to achieve especially when high bandwidth sensors (e.g. video cameras) - which are needed in the envisioned unstructured worlds - are used. This paper proposes a new methodology for image-based navigation using a self-organized visual representation of the environment. Self-organization leads to internal representations, which can be used by the robot, but are not transparent to the user. It is shown how this conceptual gap can be bridged.     

部分未知环境中移动机器人动态路径规划方法

针对部分未知环境,提出一种基于粒子滤波的动态路径规划方法.将全局最优路径视为受机器人运动及环境影响的变化量,采用粒子滤波算法,利用机器人运动信息预测路径,并利用实时环境信息更新路径,通过在线跟踪全局最优路径获得不断更新的全局优化路径.将传统全局路径规划先规划后执行的模式改为边规划边执行的模式,既减少了等待时间,又为机器人的移动误差及部分未知环境提供了较强的适应能力.仿真及实验验证,该方法的有效性.     

A study of an autonomous mobile robot for a sewer inspection system

This article describes a prototype autonomous mobile robot, KANTARO, designed for inspecting sewer pipes. It is able to move autonomously in 200–300-mm-diameter sewer pipes, to turn smoothly through 90° at a junction, and to go down a 5-cm step. KANTARO carries all the resources required, such as a control unit, a camera, a 2D laser, and an IR sensor. Damage or abnormalities in sewer pipes are detected based on recorded sensory data. KANTARO has demonstrated its effectiveness in inspection and in autonomous navigation in a dry sewer test field at the FAIS–Robotics Development Support Office (FAIS–RDSO). This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Mobile robot navigation in a partially structured static environment, using neural predictive control

This paper presents a way of implementing a model-based predictive controller (MBPC) for mobile robot navigation when unexpected static obstacles are present in the robot environment. The method uses a nonlinear model of mobile robot dynamics, and thus allows an accurate prediction of the future trajectories. An ultrasonic ranging system has been used for obstacle detection. A multilayer perceptron is used to implement the MBPC, allowing real-time implementation and also eliminating the need for high-level data sensor processing. The perceptron has been trained in a supervised manner to reproduce the MBPC behaviour. Experimental results obtained when applying the neural-network controller to a TRC Labmate mobile robot are given in the paper.     

A relative localisation system of a mobile robot

This paper describes a localisation system applied to vehicle displacements on irregular grounds and at moderate speed (about 1 m/s). It is composed of a gyrometer and a Doppler sensor, which give, by integration, the attitude and position of the vehicle supporting them, without contact with the ground. The precision of the obtained localisation is about 2% for ranges of about a hundred meters.     

Design of a teaching pendant program for a mobile shipbuilding welding robot using a PDA

Teaching pendant is a handheld device by which a human can control a robot. The main functions of a teaching pendant are moving the robot, teaching it about the locations, running robot programs, and jogging the axes. A teaching pendant is usually connected to the robot by a cable. The cable connection and the size of the teaching pendant generally do not pose a problem when the robot controller is separate from the robot. However, a large teaching pendant connected by a cable is not suitable for a self-propelled mobile robot with an internal controller. This paper describes the communication network of a personal data assistant (PDA) as a wireless teaching pendant for a mobile shipbuilding welding robot with embedded controller system that welds and moves autonomously inside the double hull structure of a ship. A double hull is a closed structure that has only a few access holes. It is very difficult and dangerous to weld components inside a double hull structure because of fumes, poisonous gas, and high temperatures. Using a wireless teaching pendant has the following advantages: (1) there are no limits to the welding activities that can take place, (2) the safety level increases because no workers are in close proximity to the robot, (3) workers are far away from the dangerous environmental conditions, (4) it is possible to reduce the weight of the cable connected to the robot, and (5) it is possible to reduce the weight of the robot because of the reduced load of the teaching pendant and the cable. We demonstrate the functionality and performance capabilities of our wireless teaching pendant through field-testing experiments.     

未知环境下的移动机器人环境建模研究

环境建模技术是移动机器人自主导航研究中的一个关键问题。为了实现机器人在未知环境下的自主导航,本文在分析当前普遍采用的一些环境建模方法及其缺点的基础上,提出一种基于多传感器信息融合的环境建模方法。实验结果表明该方法有效地克服了传感器的累计误差,有效地提高了环境建模的准确性。此方法的可行性和有效性通过Pioneer3-DX移动机器人得到了实验验证。