Guide robot intelligent navigation in urban environments

This paper presents an intelligent robotic system to guide visually impaired people in urban environments. The robot is equipped with two laser range finders, global positioning system (GPS), camera, and compass sensors. All the sensors data are processed by a single laptop computer. We have implemented different navigation algorithms enabling the robot to move autonomously in different urban environments. In pedestrian walkways, we utilize the distance to the edge (left, right, or both) to determine the robot steering command. In difference from pedestrian walkways, in open squares where there is no edge information, artificial neural networks map the GPS and compass sensor data to robot steering command guiding the visually impaired to the goal location. The neural controller is designed such as to be employed even in environments different from those in which they have been evolved. Another important advantage is that a single neural network controls the robot to reach multiple goal locations inside the open square. The proposed algorithms are verified experimentally in a navigation task inside the University of Toyama Campus, where the robot moves from the initial to goal location.     

Identifying vegetation from laser data in structured outdoor environments

The ability to reliably detect vegetation is an important requirement for outdoor navigation with mobile robots as it enables the robot to navigate more efficiently and safely. In this paper, we present an approach to detect flat vegetation, such as grass, which cannot be identified using range measurements. This type of vegetation is typically found in structured outdoor environments such as parks or campus sites. Our approach classifies the terrain in the vicinity of the robot based on laser scans and makes use of the fact that plants exhibit specific reflection properties. It uses a support vector machine to learn a classifier for distinguishing vegetation from streets based on laser reflectivity, measured distance, and the incidence angle. In addition, it employs a vibration-based classifier to acquire training data in a self-supervised way and thus reduces manual work. Our approach has been evaluated extensively in real world experiments using several mobile robots. We furthermore evaluated it with different types of sensors and in the context of mapping, autonomous navigation, and exploration experiments. In addition, we compared it to an approach based on linear discriminant analysis. In our real world experiments, our approach yields a classification accuracy close to 100%.     

Using Embodied Data for Localization and Mapping

Mobile autonomous robots have finally emerged from the confined spaces of structured and controlled indoor environments. To fulfill the promises of ubiquitous robotics in unstructured outdoor environments, robust navigation is a key requirement. The research in the simultaneous localization and mapping (SLAM) community has largely focused on optical sensors to solve this problem, and the fact that the robot is a physical entity has largely been ignored. In this paper, a hierarchical SLAM framework is proposed that takes the interaction of the robot with the environment into account. A sequential Monte Carlo filter is used to generate local map segments with a combination of visual and embodied data associations. Constraints between segments are used to generate globally consistent maps with a focus on suitability for navigation tasks. The proposed method is experimentally verified on two different outdoor robots. The results show that the approach is viable and that the rich modeling of the robot with its environment provides a new modality with the potential for improving existing visual methods and extending the availability of SLAM in domains where visual processing alone is not sufficient.     

Structure overview of vegetation detection. A novel approach for efficient vegetation detection using an active lighting system

Fully autonomous navigation has been widely investigated for several decade of years; however, a safe and reliable navigation is still a daunting challenge in terrains containing vegetation. To improve the mobility capability of recent autonomous navigation systems, an additional vegetation detection function has been proposed. Since many proposals of generating vegetation classifier as well as suggestions of using different sensors to implement the function exist, a structured overview is required for vegetation detection in the context of outdoor navigation. Therefore, this paper studies and compares the accuracy and efficiency of existing vegetation detection approaches in a structured way. Furthermore, a new vision system set-up which combines CMOS sensor and Photo Mixer Device sensor with a near-infrared lighting system is also introduced to simultaneously provide depth, near-infrared and color images at high frame rate. Those near-infrared and color information are then used to compute vegetation index or train vegetation classifier to completely realize a real-time robust vegetation detection system. In this paper, a modification of the normalized difference vegetation index is devised, which is then defined as the new standard form of vegetation index for such vision system integrated with an additional lighting system. Finally, we will show the out-performance of the proposed approach in comparison with more conventional ones.     

基于视觉的同时定位与地图构建方法综述

基于视觉的自主导航与路径规划是移动机器人研究的关键技术,对基于视觉的计算机导航与同时定位及地图构建（SLAM）方法近三十年的发展进行了总结和展望。将视觉导航分为室内导航和室外导航,并详细阐述了每一种子类型的特点和方法。对于室内视觉导航,列举了经典导航模型和技术方法,探讨了解决SLAM问题的最新进展：HTM-SLAM算法和基于特征的算法;对室外视觉导航,阐述了国际国内目前的研究动态。     

Mobile robot navigation using vision and olfaction to search for a gas/odor source

This paper presents a new approach to search for a gas/odor source using an autonomous mobile robot. The robot is equipped with a CMOS camera, gas sensors, and airflow sensors. When no gas is present, the robot looks for a salient object in the camera image. The robot approaches any object found in the field of view, and checks it with the gas sensors to see if the object is releasing gas. On the other hand, if the robot detects the presence of gas while wandering around the area, it turns toward the direction of the wind that carries the gas. The robot then looks for any visible object in that direction. These navigation strategies are implemented into the robot under the framework of the behavior-based subsumption architecture. Experimental results on the search for a leaking bottle in an indoor environment are presented to demonstrate the validity of the navigation strategies.     

室外自主移动机器人AMOR的导航技术

在非结构化环境,移动机器人行驶运动规划和自主导航是非常挑战性的问题。基于实时的动态栅格地图,提出了一个快速的而又实效的轨迹规划算法,实现机器人在室外环境的无碰撞运动导航。AMOR是自主研发的室外运动移动机器人,它在2007年欧洲C-ELROB大赛中赢得了野外自主侦察比赛的冠军。它装备了SICK的激光雷达,用来获取机器人运动前方的障碍物体信息,建立实时动态的环境地图。以A*框架为基础的改造算法,能够在众多的路径中快速地找到最佳的安全行驶路径,实现可靠的自主导航。所有的测试和比赛结果表明所提方案是可行的、有效的。     

自主飞行机器人导航系统设计

自主飞行机器人系统是以微型直升机模型为载体的复杂系统。在该系统中导航系统采集各传感器数据得到机器人当前飞行姿态、空间位置以及相应的监控信息,控制模块依此监控信息按照给定策略计算并发出控制信号,实现飞行机器人的自主控制。本文对自主飞行机器人导航系统设计及功能实现做出了详细阐述。首先,给出了本自主飞行机器人的系统构造;其次,给出了导航系统的硬件组成部分以及各部分所完成的功能任务;最后阐述了导航系统的功能实现,包括飞行姿态和空间位置的获取。     

一种基于学习向量量化神经网络的图象分割方法

基于视觉传感器实现道路信息的理解是目前移动机器人自主导航的重要研究方向,其中道路图象的正确分割是提取有效路径信息的关键。该文针对复杂、干扰因素多的室外环境下传统方法难以实现道路图象正确分割的问题,提出了一种基于LV Q神经网络的道路图象分割方法。该方法通过选取道路图象的归一化色彩分量为特征向量,应用基于LV Q学习算法的神经网络分类器进行道路与非道路识别;为解决环境噪声对神经网络输出的影响,本文设计了串行级联式四阶形态滤波器实现对神经网络输出的分割图象的滤波处理。通过对实测图象进行分割处理验证了该方法的有效性和鲁棒性,可用于室外环境下机器人的实时视觉导航控制。     

Outdoor autonomous navigation using SURF features

In this article, we propose a speeded-up robust features (SURF)-based approach for outdoor autonomous navigation. In this approach, we capture environmental images using an omni-directional camera and extract features of these images using SURF. We treat these features as landmarks to estimate a robot’s self-location and direction of motion. SURF features are invariant under scale changes and rotation, and are robust under image noise, changes in light conditions, and changes of viewpoint. Therefore, SURF features are appropriate for the self-location estimation and navigation of a robot. The mobile robot navigation method consists of two modes, the teaching mode and the navigation mode. In the teaching mode, we teach a navigation course. In the navigation mode, the mobile robot navigates along the teaching course autonomously. In our experiment, the outdoor teaching course was about 150 m long, the average speed was 2.9 km/h, and the maximum trajectory error was 3.3 m. The processing time of SURF was several times shorter than that of scale-invariant feature transform (SIFT). Therefore, the navigation speed of the mobile robot was similar to the walking speed of a person.     

基于增强转移网络（ATN）的室外移动机器人道路图像理解

道路图像理解是室外移动机器人视觉导航自主驾驶研究中的一个关键技术 ,由于基于视觉导航的室外移动机器人自主驾驶时 ,对实时性和鲁棒性要求很高 ,因此 ,为了满足室外移动机器人自主驾驶的实时性和鲁棒性要求 ,将人工智能研究句法分析中的一个形式体系——增强转移网络 (ATN )成功地应用于室外移动机器人的道路理解中 ,进而提出了基于 ATN的室外移动机器人道路图像理解算法 ,该算法在统一的 ATN构建思想指导下 ,针对不同的道路情况 ,不仅可以灵活地构建出不同的道理理解 ATN网络 ,还可达到本质上的统一及应用上的灵活。经实验检验 ,该算法在满足系统要求的鲁棒性条件下 ,具有非常高的实时性 ,即能充分地满足自主移动机器人高速自主导航的需要     

Monocular Vision for Mobile Robot Localization and Autonomous Navigation

This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.     

A Highly-Maneuverable Demining Autonomous Robot: an Over-Actuated Design

Based on the well-known advantages of using an over-actuated mechanism for robots, this research proposes a holonomic highly-maneuverable autonomous robot design for demining service applications. The proposed approach provides an interesting compromise between the design requirements of the demining robot applications and the over-actuated autonomous robots. The robot body is mainly divided into two parts: the first part provides the robot with its required locomotion and it consists of a driving/steering subsystem with four driving wheels (4WD), four steering mechanisms (4SW), and a passive suspension subsystem. The second part is a manipulator with three degrees of freedom that is designed based on two parallelogram mechanisms. The proposed design insures many advantages over existing designs, including stability, maneuverability, autonomous navigation, and simplicity of the control effort constraints. The robot model and its corresponding stability analysis were conducted and simulated in order to evaluate the motion of the robot over different environments rough terrains and slanted surfaces. Moreover, a prototype of the proposed robot was developed and built and different types of sensors were used in order to help it take precise actuation decisions for navigation and control. The prototype was experimentally tested for different scenarios and environments in order to validate the proposed design. The testing results demonstrated decent performance of the robot in autonomous navigation and in localizing the detected objects.     

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.     

Weaver: Hexapod robot for autonomous navigation on unstructured terrain

Legged robots are an efficient alternative for navigation in challenging terrain. In this paper we describe Weaver, a six‐legged robot that is designed to perform autonomous navigation in unstructured terrain. It uses stereo vision and proprioceptive sensing based terrain perception for adaptive control while using visual‐inertial odometry for autonomous waypoint‐based navigation. Terrain perception generates a minimal representation of the traversed environment in terms of roughness and step height. This reduces the complexity of the terrain model significantly, enabling the robot to feed back information about the environment into its controller. Furthermore, we combine exteroceptive and proprioceptive sensing to enhance the terrain perception capabilities, especially in situations in which the stereo camera is not able to generate an accurate representation of the environment. The adaptation approach described also exploits the unique properties of legged robots by adapting the virtual stiffness, stride frequency, and stride height. Weaver's unique leg design with five joints per leg improves locomotion on high gradient slopes, and this novel configuration is further analyzed. Using these approaches, we present an experimental evaluation of this fully self‐contained hexapod performing autonomous navigation on a multiterrain testbed and in outdoor terrain.     

Development,integration, and field evaluation of an autonomous citrus-harvesting robot

Citrus harvesting is a labor-intensive and time-intensive task. As the global population continues to age, labor costs are increasing dramatically. Therefore, the citrus-harvesting robot has attracted considerable attention from the business and academic communities. However, robotic harvesting in unstructured and natural citrus orchards remains a challenge. This study aims to address some challenges faced in commercializing citrus-harvesting robots. We present a fully integrated, autonomous, and innovative solution for citrus-harvesting robots to overcome the harvesting difficulties derived from the natural growth characteristics of citrus. This solution uses a fused simultaneous localization and mapping algorithm based on multiple sensors to perform high-precision localization and navigation for the robot in the field orchard. Besides, a novel visual method for estimating fruit poses is proposed to cope with the randomization of citrus growth orientations. Further, a new end-effector is designed to improve the success and conformity rate of citrus stem cutting. Finally, a fully autonomous harvesting robot system has been developed and integrated. Field evaluations showed that the robot could harvest citrus continuously with an overall success rate of 87.2% and an average picking time of 10.9 s/fruit. These efforts provide a solid foundation for the future commercialization of citrus-harvesting robots.     

Navigation with constraints for an autonomous mobile robot

This paper describes a navigation planning algorithm for a robot capable of autonomous navigation in a structured, partially known and dynamic environment. This algorithm is applied to a discrete workspace composed of a network of places and roads. The environment specification associates temporal constraints with any element of the network, and recharge or relocalisation possibilities with places. A mission specification associates several constraints with each navigation task (energy, time, position uncertainty and distance).

The algorithm computes an optimal path for each navigation task according to the optimization criterion and constraints. We introduce the notion of efficient path applied to a new best first search algorithm solving a multiple constraints problem. The path determination relies on a state representation adapted to deal with environment constraints. We then prove that the complexity chracteristics of our algorithm are similar to those of the A* algorithm.

The planner described in this paper has been implemented on a Spare station for a Robuter mobile platform equipped with ultra-sonic range sensors and an active stereo vision system. It was developed for the MITHRA family of autonomous surveillance robots as part of project EUREKA EU 110.     

Autonomous mobile robot navigation and learning

Research focused on the development and experimental validation of intelligent control techniques for autonomous mobile robots able to plan and perform a variety of assigned tasks in unstructured environments is presented. In particular, an autonomous mobile robot, HERMIES-IIB intelligence experiment series, is described. It is a self-powered, wheel-driven platform containing an onboard 16-node Ncube hypercube parallel processor interfaced to effectors and sensors through a VME-based system containing a Motorola 68020 processor, a phased sonar array, dual manipulator arms, and multiple cameras. Research on navigation and learning is examined     

基于多传感器的可变形机器人自主控制方法研究

将GPS、电子罗盘、倾角仪、码盘传感器应用到可变形机器人自主运动控制中。针对可变形机器人自身结构特点，提出了一种基于多传感器信息融合的可变形机器人在野外环境中自主控制的方法。该方法主要实现了在非结构环境中机器人的自主变形、自主避障和自主导航定位的功能。实验验证了该方法的有效性。