Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

动态环境下基于速度空间寻优的局部避障方法

在动态环境下的局部避障是移动机器人的一项基本功能.在各种速度空间方法,如曲率-速率法(CVM)、巷道-曲率法(LCM)和扇区-曲率法(BCM)的基础上,提出了一种适用于未知或部分未知动态环境的局部避障方法.该方法将碰撞预测模型与改进后的BCM有效结合,不仅兼备了CVM的平滑性、LCM的安全性和BCM快速性的优点,而且弥补了各种速度空间寻优方法的不足,使其能够适用于移动机器人在动态环境下的避障与导航.实际机器人的导航实验表明该算法是可行而有效的.     

Hierarchical Learning of Navigational Behaviors in an Autonomous Robot using a Predictive Sparse Distributed Memory

We describe a general framework for learning perception-based navigational behaviors in autonomous mobile robots. A hierarchical behavior-based decomposition of the control architecture is used to facilitate efficient modular learning. Lower level reactive behaviors such as collision detection and obstacle avoidance are learned using a stochastic hill-climbing method while higher level goal-directed navigation is achieved using a self-organizing sparse distributed memory. The memory is initially trained by teleoperating the robot on a small number of paths within a given domain of interest. During training, the vectors in the sensory space as well as the motor space are continually adapted using a form of competitive learning to yield basis vectors that efficiently span the sensorimotor space. After training, the robot navigates from arbitrary locations to a desired goal location using motor output vectors computed by a saliency-based weighted averaging scheme. The pervasive problem of perceptual aliasing in finite-order Markovian environments is handled by allowing both current as well as the set of immediately preceding perceptual inputs to predict the motor output vector for the current time instant. We describe experimental and simulation results obtained using a mobile robot equipped with bump sensors, photosensors and infrared receivers, navigating within an enclosed obstacle-ridden arena. The results indicate that the method performs successfully in a number of navigational tasks exhibiting varying degrees of perceptual aliasing.     

Extending obstacle avoidance methods through multiple parameter-space transformations

Obstacle avoidance methods approach the problem of mobile robot autonomous navigation by steering the robot in real-time according to the most recent sensor readings, being suitable to dynamic or unknown environments. However, real-time performance is commonly gained by ignoring the robot shape and some or all of its kinematic restrictions which may lead to poor navigation performance in many practical situations. In this paper we propose a framework where a kinematically constrained and any-shape robot is transformed in real-time into a free-flying point in a new space where well-known obstacle avoidance methods are applicable. Our contribution with this framework is twofold: the definition of generalized space transformations that cover most of the existing transformational approaches, and a reactive navigation system where multiple transformations can be applied concurrently in order to optimize robot motion decisions. As a result, these transformations allow existing obstacle avoidance methods to perform better detection of the surrounding free-space, through “sampling” the space with paths compatible with the robot kinematics. We illustrate how to design these space transformations with some examples from our experience with real robots navigating in indoor, cluttered, and dynamic scenarios. Also, we provide experimental results that demonstrate the advantages of our approach over previous methods when facing similar situations.

Image-based memory for robot navigation using properties of omnidirectional images

This paper proposes a new technique for vision-based robot navigation. The basic framework is to localise the robot by comparing images taken at its current location with reference images stored in its memory. In this work, the only sensor mounted on the robot is an omnidirectional camera. The Fourier components of the omnidirectional image provide a signature for the views acquired by the robot and can be used to simplify the solution to the robot navigation problem. The proposed system can calculate the robot position with variable accuracy (‘hierarchical localisation’) saving computational time when the robot does not need a precise localisation (e.g. when it is travelling through a clear space). In addition, the system is able to self-organise its visual memory of the environment. The self-organisation of visual memory is essential to realise a fully autonomous robot that is able to navigate in an unexplored environment. Experimental evidence of the robustness of this system is given in unmodified office environments.     

Reactive navigation in dynamic environment using a multisensorpredictor

A reactive navigation system for an autonomous mobile robot in unstructured dynamic environments is presented. The motion of moving obstacles is estimated for robot motion planning and obstacle avoidance. A multisensor-based obstacle predictor is utilized to obtain obstacle-motion information. Sensory data from a CCD camera and multiple ultrasonic range finders are combined to predict obstacle positions at the next sampling instant. A neural network, which is trained off-line, provides the desired prediction on-line in real time. The predicted obstacle configuration is employed by the proposed virtual force based navigation method to prevent collision with moving obstacles. Simulation results are presented to verify the effectiveness of the proposed navigation system in an environment with multiple mobile robots or moving objects. This system was implemented and tested on an experimental mobile robot at our laboratory. Navigation results in real environment are presented and analyzed.     

Introducing autonomous aerial robots in industrial manufacturing

Although ground robots have been successfully used for many years in manufacturing, the capability of aerial robots to agilely navigate in the often sparse and static upper part of factories makes them suitable for performing tasks of interest in many industrial sectors. This paper presents the design, development, and validation of a fully autonomous aerial robotic system for manufacturing industries. It includes modules for accurate pose estimation without using a Global Navigation Satellite System (GNSS), autonomous navigation, radio-based localization, and obstacle avoidance, among others, providing a fully onboard solution capable of autonomously performing complex tasks in dynamic indoor environments in which all necessary sensors, electronics, and processing are on the robot. It was developed to fulfill two use cases relevant in many industries: light object logistics and missing tool search. The presented robotic system, functionalities, and use cases have been extensively validated with Technology Readiness Level 7 (TRL-7) in the Centro Bahía de Cádiz (CBC) Airbus D&S factory in fully working conditions.     

基于向量场的移动机器人动态路径规划

由于简洁、高效等优点,人工势场法已应用于自主移动机器人的在线实时路径规划,并受到广泛关注.目前,人工势场法在处理静态环境、动态匀速环境下的路径规划方面已有许多成果,但是,机器人在全变速环境下进行在线实时路径规划时,会出现路径冗余、避碰不及等现象.为此,将目标关于机器人的相对加速度因素引入引力势场函数中;在斥力势场函数的基础上融合避碰预测、减速避障策略;最终,机器人能够避免大量无谓避障,当与障碍物相对速度较大时能提前避障,且快速跟踪到目标.仿真结果验证了所提方法的有效性.     

基于接触时间的移动机器人安全导航

自主移动机器人在动态未知环境中导航存在安全隐患。为了提高导航安全性,提出一种基于接触时间的导航环境表示方法,即接触时间空间。将接触时间作为导航环境中危险评价指标,利用线速度和角速度计算机器人与障碍物之间任意两点接触时间,通过算法将导航过程中的构型空间映射为接触时间空间。将接触时间空间应用于经典行为动力学导航方法,与经典行为动力学导航方法和结合速度障碍物的行为动力学的导航方法仿真对比,结果表明接触时间空间可以提高自主移动机器人的导航安全性。     

INTELLIGENT SENSOR FUSION AND LEARNING FOR AUTONOMOUS ROBOT NAVIGATION

ABSTRACT

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment.     

基于VSLAM的自主移动机器人三维同时定位与地图构建

移动机器人在探索未知环境且没有外部参考系统的情况下,面临着同时定位和地图构建（SLAM）问题。针对基于特征的视觉SLAM（VSLAM）算法构建的稀疏地图不利于机器人应用的问题,提出一种基于八叉树结构的高效、紧凑的地图构建算法。首先,根据关键帧的位姿和深度数据,构建图像对应场景的点云地图;然后利用八叉树地图技术进行处理,构建出了适合于机器人应用的地图。将所提算法同RGB-D SLAM（RGB-Depth SLAM）算法、ElasticFusion算法和ORB-SLAM（Oriented FAST and Rotated BRIEF SLAM）算法通过权威数据集进行了对比实验,实验结果表明,所提算法具有较高的有效性、精度和鲁棒性。最后,搭建了自主移动机器人,将改进的VSLAM系统应用到移动机器人中,能够实时地完成自主避障和三维地图构建,解决稀疏地图无法用于避障和导航的问题。     

基于声纳环传感器的机器人避障研究

任何一种移动机器人要实现未知环境自主导航都必须有效而可靠地感知环境信息,而超声波传感器在检测障碍物距离信息方面应用十分广泛。介绍了旅行家II号声纳环传感系统的设计与实现原理,并对声纳的精度进行了测试。在此基础上,提出了移动机器人一种简单避障策略,并运用2种基本避障实验:静态避障和动态避障,验证了该避障策略的正确性和有效性。     

Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments

This paper presents a novel reactive collision avoidance method for mobile robots moving in dense and cluttered environments. The proposed method, entitled Tangential Gap flow (TGF), simplifies the navigation problem using a divide and conquer strategy inspired by the well-known Nearness-Diagram Navigation (ND) techniques. At each control cycle, the TGF extracts free openings surrounding the robot and identifies the suitable heading which makes the best progress towards the goal. This heading is then adjusted to avoid the risk of collision with nearby obstacles based on two concepts namely, tangential and gap flow navigation. The tangential navigation steers the robot parallel to the boundary of the closest obstacle while still emphasizing the progress towards the goal. The gap flow navigation safely and smoothly drives the robot towards the free area in between obstacles that lead to the target. The resultant trajectory is faster, shorter and less-oscillatory when compared to the ND methods. Furthermore, identifying the avoidance maneuver is extended to consider all nearby obstacle points and generate an avoidance rule applicable for all obstacle configurations. Consequently, a smoother yet much more stable behavior is achieved. The stability of the motion controller, that guides the robot towards the desired goal, is proved in the Lyapunov sense. Experimental results including a performance evaluation in very dense and complex environments demonstrate the power of the proposed approach. Additionally, a discussion and comparison with existing Nearness-Diagram Navigation variants is presented.     

From The Guest Editor's - Computational Geometry in Navigation and Path Planning

The six contributions to this special issue focus on computational geometry in navigation and path planning. The issue presents the applications of computational geometry in obstacle avoidance, path planning with minimum clearance, autonomous mobile robot navigation, exploration of unknown polygonal environments, and it explores the practical relevance to the geographical information systems, mobile networks, risk avoidance, security, games, and online system design.     

基于机器人听觉的自主声源搜索策略

提出了融合机器人听觉和超声避障的自主声源搜索策略．搜索策略按优先级分成3个模块：声源确认、超声避障、声源定位搜索, 通过优先级的判断确定当前执行模块．声源定位基于改进的时延估计定位方法实现,机器人在搜索并接近声源过程中利用超声避障． 在室内环境下测试系统,实验结果证明在混响环境下机器人可以定位声源并且可以绕过障碍物接近并确认声源,该方法具有实时实现的有效性和应用性．     

基于学习机制的移动机器人动态场景自适应导航方法

针对在单一学习机制中,移动机器人自主导航一般只适用于静态场景,适应性差的问题,提出一种动态场景自适应导航方法．该方法通过激光测距仪（LRF）获取周围环境的距离信息,在基于增量判别回归（IHDR）算法的单一学习机制导航的基础上,提出了最远距离优先机制的局部避障环节．该导航方法克服了传统导航方法对环境模型的过度依赖,并且本文提出的基于最远距离优先机制的局部避障算法,解决了基于单一学习机制的导航方法对动态场景适应能力不足的问题．本文将动态场景自适应导航方法应用到了MT-R机器人中,与基于单一学习机制的导航方法进行了对比实验,并且运用提出的局部避障算法,对实验中的激光数据进行了算法性能分析．实验结果证实了该方法的可行性,并显示了该方法在动态场景下的良好表现．     

AS-R移动机器人的动态避障与路径规划研究

针对移动机器人的动态避障和路径规划问题,以AS-R移动机器人为平台,设计了一种基于行为分析的动态避障策略。根据避障问题将移动机器人整个运行过程中的行为划分成趋向目标行为、避障行为、沿墙走行为及紧急避障4种行为,有效实现了机器人的动态避障,并解决了两种避障难题:左右摆动问题和凹型障碍物问题。利用多传感器结合检测方法,通过红外传感器减少盲区和镜面反射带来的误差,通过间隔采样或分组采样技术避免多路串扰问题;对均值滤波与中值滤波进行实验对比后,提出一种递推型中值滤波方法,从而提高了数据在空间和时间上的连续性,有效地减少了超声波随机串扰信号及其它干扰信号,进而提高了探测模块的准确度。最后设计了几种复杂环境下机器人的动态避障和路径规划,并验证了所提方法的有效性。     

Global Navigation in Dynamic Environments Using Case-Based Reasoning

This paper presents a global navigation strategy for autonomous mobile robots in large-scale uncertain environments. The aim of this approach is to minimize collision risk and time delays by adapting to the changes in a dynamic environment. The issue of obstacle avoidance is addressed on the global level. It focuses on a navigation strategy that prevents the robot from facing the situations where it has to avoid obstacles. To model the partially known environment, a grid-based map is used. A modified wave-transform algorithm is described that finds several alternative paths from the start to the goal. Case-based reasoning is used to learn from past experiences and to adapt to the changes in the environment. Learning and adaptation by means of case-based reasoning permits the robot to choose routes that are less risky to follow and lead faster to the goal. The experimental results demonstrate that using case-based reasoning considerably increases the performance of the robot in a difficult uncertain environment. The robot learns to take actions that are more predictable, minimize collision risk and traversal time as well as traveled distances.