移动机器人向目标点运动的变增益控制方法

针对控制量受限的移动机器人，研究其向目标点运动的控制问题，提出了移动机器人有效控制的概念，并采用交增益控制方法，使系统的状态单调收敛，最终到达目标点．该方法可有效避免系统收敛于极限环上的情况，仿真结果验证了交增益控制器的有效性。     

危险环境消防侦察机器人视轴规划算法研究

针对自主研发的危险环境消防侦察机器人展开研究。此机器人为履带式车身,装载一3自由度机械,高清侦察相机固定在机械臂末端。研究内容为在已知环境下自主规划机械臂规避障碍物,并调节相机视轴方向,实现对环境重点区域的锁定。首先对机械臂建立指数积（POE）正运动学模型,使用Minkowski和与三维凸包检测算法作为碰撞检测算法,使用蒙特卡洛方法在构型空间采样。再通过采样数据匹配末端视角方向,在末端建立虚拟连杆,使用碰撞检测算法保证实现视线无遮挡。仿真实验证明,使用此算法能够快速将相机视轴锁定至重点区域并保证无遮挡,且机械臂末端相机处于最佳位姿。     

A comparative study on some navigation schemes of a real robot tackling moving obstacles

A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.     

移动机器人视觉导航中基于Hough变换的直线检测与跟踪

移动机器人的视觉导航有多种方法,本文主要在直线信息丰富的室内环境中,对移动机器人导航采用基于Hough变换的直线检测与跟踪的方法,实验仿真结果表明,该方法是可行的。     

Adaptive behavior navigation of a mobile robot

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities     

Mobile robot navigation by a Distributed Vision System

This paper proposes aDistributed Vision System (DVS), which is an intelligent infrastructure for mobile robots consisting of manyvision agents (VAs) embedded in an environment. The system monitors the environment from various viewing points with the VAs, maintains the dynamic environment models, and provides various information to robots. Based on this concept, we have developed a prototype of the DVS which consists of sixteen vision agents and simultaneously navigates two robots in a model town.     

Vision for mobile robot navigation: a survey

Surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment     

Evolution of homing navigation in a real mobile robot

In this paper we describe the evolution of a discrete-time recurrent neural network to control a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely on the physical robot without human intervention. We show that the autonomous development of a set of behaviors for locating a battery charger and periodically returning to it can be achieved by lifting constraints in the design of the robot/environment interactions that were employed in a preliminary experiment. The emergent homing behavior is based on the autonomous development of an internal neural topographic map (which is not pre-designed) that allows the robot to choose the appropriate trajectory as function of location and remaining energy.     

Positioning and navigation of mobile robot

Positioning tracking is not a new idea as we have been seen from the ability of the GPS (Global Positioning System) to track the position of the object, in general with acceptable accuracy but the cost of GPS installation is expensive. However, in the case of detecting the exact position in signal-blocked closed environment (e.g. inside building, forest, mines and others); the accuracy factor provide by GPS is low. Thus, this project presents a positioning tracking system that is able to track the movement of an object within a small area or inside buildings. A complete set of the positioning tracking system consists of a pair of computer mechanical mouse and a microcontroller. From the position displayed on the computer screen, the position of the object can be located. The pair of mouse detects each movement of the object and sends the movement data to microcontroller. Linear, angular displacement and positioning calculation are also being discussed. From the results, it’s shown that the positioning system is applicable. However, some small errors are also occurred but in acceptable range. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Range measurements by a mobile robot using a navigation line

A continuous, straight-edged line is used for the visual navigation of an autonomous mobile robot in a factor environment. This line, which resides on the floor and contrasts with background, may also be used to determine range information. Two methods are developed for determining the range of an object in the sensor's field of view. The effects of various error conditions in the system geometry on each ranging method are determined. Equations are derived which yield the percent error in calculating ranges given estimates of these error conditions. Numerical examples using typical sensor parameters are given.     

Visual strategies for the control of steering toward a goal

We have shown that people steer toward a target by aligning their heading with the target when target egocentric direction is not available for steering [24]. Here we examined what visual strategies people use to steer toward a target when target egocentric direction is available for steering. The display simulated a participant walking over a ground plane with a target placed off to one side. The participant’s simulated heading in the display was displaced 10° away from the participant’s straight ahead. A textured ground display that provided dense global optic flow and an empty ground display that provided nearly no flow were tested. Participants were instructed to use a joystick to control their simulated self-motion in the display to (a) steer toward the target, (b) center the target at their straight ahead, or (c) minimize the target drift on the screen. We found that participants produced similar heading error profiles when they were instructed to steer toward the target or to center the target straight ahead, but not when they were instructed to minimize the target movement on the screen. Furthermore, regardless of the instructions received, final heading errors were about 5° smaller with the textured than with the empty ground display, indicating the effect of optic flow on the control performance. We conclude that when target egocentric direction is available for steering, people do not steer toward the target by canceling its optical drift. Optic flow contributes to steering toward a target even when control could be based on egocentric direction alone.     

Sensing error for a mobile robot using line navigation

The use of a contrasting line for the visual navigation of autonomous mobile robots in a factory environment is developed. Minimum and maximum linewidths are determined analytically by considering sensor geometry, field of view, and error conditions present in the system. The effects of these error conditions on the width of the line, as seen in the image plane, determines the optimal linewidth. Numerical examples using typical sensor parameters are given.     

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.

Differential GPS and odometry-based outdoor navigation of a mobile robot

This paper describes outdoor navigation for a mobile robot by using differential GPS (DGPS) and odometry in a campus walkway environment. The robot position is estimated by fusion of DGPS and odometry. The GPS receiver measures its position by radio waves from GPS satellites. The error of GPS measurement data increases near high buildings and trees because of multi-path and forward diffractions. Thus, it is necessary to pick up only accurate DGPS measurement data when the robot position is modified by fusing DGPS and odometry. In this paper, typical DGPS measurement data observed near high buildings and trees are reported. Then, the authors propose a novel position correction method by fusing GPS and odometry. Fusion of DGPS and odometry is realized using an extended Kalman filter framework. Moreover, outdoor navigation for a mobile robot is accomplished by using the proposed correction method.     

Biologically inspired visual odometer for navigation of a flying robot

Experimental research in biology has uncovered a number of different ways in which flying insects use cues derived from optical flow for navigational purposes, such as safe landing, obstacle avoidance and dead reckoning. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of bees. Specifically, we focus on the mechanisms of course stabilization behavior and visually mediated odometer by using a biological model of motion detector for the purpose of long-range goal-directed navigation in 3D environment. The performance tests of the proposed navigation method are conducted by using a blimp-type flying robot platform in uncontrolled indoor environments. The result shows that the proposed mechanism can be used for goal-directed navigation. Further analysis is also conducted in order to enhance the navigation performance of autonomous aerial vehicles.     

Learning sensor-based navigation of a real mobile robot in unknownworlds

In this paper, we address the problem of navigating an autonomous mobile robot in an unknown indoor environment. The parti-game multiresolution learning approach is applied for simultaneous and cooperative construction of a world model, and learning to navigate through an obstacle-free path from a starting position to a known goal region. The paper introduces a new approach, based on the application of the fuzzy ART neural architecture, for on-line map building from actual sensor data. This method is then integrated, as a complement, on the parti-game world model, allowing the system to make a more efficient use of collected sensor information. Then, a predictive on-line trajectory filtering method, is introduced in the learning approach. Instead of having a mechanical device moving to search the world, the idea is to have the system analyzing trajectories in a predictive mode, by taking advantage of the improved world model. The real robot will only move to try trajectories that have been predicted to be successful, allowing lower exploration costs. This results in an overall improved new method for goal-oriented navigation. It is assumed that the robot knows its own current world location-a simple dead-reckoning method is used for localization in our experiments. It is also assumed that the robot is able to perform sensor-based obstacle detection (not avoidance) and straight-line motions. Results of experiments with a real Nomad 200 mobile robot are presented, demonstrating the effectiveness of the discussed methods.     

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.     

Design of a robot capable of moving on a vertical wall

The development of a mobile robot which can work on the vertical walls of tall buildings, the side walls of large ships, etc. has been expected for a long time. A magnetic force or vacuum pressure is available to sustain the robot on a vertical wall, and wheels, crawlers and some other walking mechanisms can be used as the methods of moving. Many combinations of these mechanisms will be developed for various applications. Two kinds of robot model were built and tested. The air was sucked from the peripheral nozzle of the suction cup to the fan and crawlers were used as the moving system. There are two dangerous situations-slipping and falling, and their limits are determined. It is important to decrease the shock of an impulsive load, such as a falling body colliding with the robot on the wall. In such a case, a guard with a shock absorber is useful for avoiding this danger, and its optimum design condition is derived. The aerodynamic matching between the fan and suction cup is also important to understand the safety conditions and to design an active controller to avoid dangerous situations. It is investigated using the above models.     

Vision-based interception of a moving target with a nonholonomic mobile robot

A novel vision-based scheme is presented for driving a nonholonomic mobile robot to intercept a moving target. The proposed method has a two-level structure. On the lower level, the pan–tilt platform carrying the on-board camera is controlled so as to keep the target as close as possible to the center of the image plane. On the higher level, the relative position of the target is retrieved from its image coordinates and the camera pan–tilt angles through simple geometry, and used to compute a control law which drives the robot to the target. Various possible choices are discussed for the high-level robot controller, and the associated stability properties are rigorously analysed. The proposed visual interception method is validated through simulations as well as experiments on the mobile robot MagellanPro.     

移动机器人导航控制研究的重要进展—-评专著《未知环境中移动机器人导航控制理论与方法》

蔡自兴、贺汉根、陈虹等教授的新著《未知环境中移动机器人导航控制理论与方法》已在科学出版社问世,成为《21世纪先进制造技术丛书》的一枝新秀,作为该丛书的主编,我深感荣幸。专著是三位教授和研究组成员,在智能机器人领域多年来辛苦耕耘,取得丰硕成果的基础上,精心凝练写成的,高屋建瓴,叶茂根深。它的出版值得大家庆贺。