Mobile robot assistants

This article discusses dependable navigation as a basic feature for operating mobile robots among humans. The importance of each navigation module according to the previously mentioned dependability components and resulting specifications for the development of such modules is analyzed. The technical implementations of dependable localization, path planning, and obstacle avoidance systems are also presented. A discussion on the dependable execution of fetch-and-carry tasks among humans is also provided. The crucial components for accomplishing this task are specified and the requirements for the implementation are developed. A modular control architecture for task planning and execution is described, and a new method for sensor-based object manipulation is introduced. Experimental results including the long-term installation of three entertainment robots in a museum in Berlin and several short-term installations of Fraunhofer IPA's robots, Care-O-bot I and II, are also presented and evaluated.     

TAMS: development of a multipurpose three-arm aerial manipulator system

This paper describes the concept, design and realization of a novel manipulator system for a multirotor aerial robot. The proposed manipulator system consists of three robotic arms attached to a multirotor frame. To overcome the limited payload capabilities in multirotor aerial platforms, the same set of on-board manipulators are used to realize various tasks, primarily the manipulation or grasping of objects of various sizes. Furthermore, they can provide aid in complex navigation tasks by doing physical, contact-based obstacle avoidance and are able to act as adaptive landing gear in uneven terrain. The design requirements and the analysis to realize these tasks is discussed. The proposal is supported with the successful demonstration of these tasks using the aerial multi manipulator system in an outdoor environment.     

基于CAN总线的移动机器人通信系统设计

近年来,对移动机器人的研究已成为热点,尤其是对移动机器人数据通信系统的研究。由于CAN总线技术具有网络功能,因此本文利用CAN总线构建了一个移动机器人的数据通信系统,使得机器人内部可以灵活地挂接多个功能模块。本文所设计的移动机器人具有动作模块、语音交互模块、避障与导航模块等。实验表明利用CAN总线技术可以提升移动机器人数据通信的可靠性和可扩展性。     

绳驱超冗余空间柔性机械臂遥操作系统设计与实验研究

针对绳驱超冗余空间柔性机械臂的运动特点和在大范围工作空间快速运动、避障及狭小空间作业和触碰情况下精细操作等在轨遥操作任务需求,设计并研制了适应多种工况的基于手控器和虚拟现实（VR）手柄组合的遥操作人机交互系统。对狭小空间作业和避障等工况进行分析,提出了基于自由度动态组合和末端―臂形同步规划的遥操作方法。最后,完成了穿越卫星太阳帆板狭缝的典型实验,实验验证了遥操作系统的工程实用性,以及柔性机械臂遥操作运动规划方法的可行性。因此,配合使用多种人机交互方式组合的遥操作系统和相应的运动规划方法,可使操作者以更加直观自然的方式参与到遥操作系统中,有效提高操作员完成复杂遥操作任务的安全性和操作性能。     

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。     

CaRINA Intelligent Robotic Car: Architectural design and applications

This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.     

模块化超冗余度空间机械臂的设计与实验

针对空间在轨服务任务需求设计了一种9自由度的模块化超冗余空间机械臂。该机械臂由9个相同的机械臂关节构成,其关节数量可根据任务要求进行调整。模块化关节采用一体化设计,关节内部合理地布置了机械传动部分与电气部分。基于改进的Bi-RRT算法和建立的机械臂的正逆运动学模型,机械臂进行了穿越复杂障碍环境的仿真和实验,实验结果表明该机械臂可以灵活地穿越障碍环境。基于阻抗控制算法,分别采用该机械臂进行了写字实验和恒力保持实验,实验结果表明机械臂具有良好的力控制能力。实验验证了该机械臂具备在复杂空间环境中执行在轨服务的能力。     

基于LabVIEW软件的机器人避障控制系统设计

设计机器人避障控制系统,采用传统设计方式存在避障效果差的缺点,为了避免该缺点影响系统控制效果,提出了基于LabVIEW技术的机器人避障控制系统设计。根据控制系统总体结构,采用LabVIEW技术对信息进行综合处理,并汇总到DSP 微处理器模块,从而实现机器人相对定位。依据机器人避障控制系统的硬件结构,进行系统模块化设计,通过单片机执行定时中断服务子程序,以此控制系统电路,并计算出机器人距障碍物的距离。通过RS232 串行通信线缆交换海量数据,以此设计控制器结构,其中上位机直接控制移动机器人,下位机间接控制移动机器人。使用数字控制振荡器可实现高精度参数化调制,进而输出正余弦波形。使用PAR传感器,直接与数字控制振荡器相连,具有随时启动应用的特点,采用TSR传感器可对障碍物静态、动态不同工况下进行数据采集与传输。通过JTAG标准测试协议,用于芯片内部测试,同时隔离逻辑电路和芯片引脚。依据系统软件流程,设计机器人避障功能。由实验结果可知,该系统避障效果最高可达到97%,具有良好应用价值。     

Design and Control of a Hyper-Redundant Manipulator for Mobile Manipulating Unmanned Aerial Vehicles

Manipulating objects using arms mounted to unmanned aerial vehicles (UAVs) is attractive because UAVs may access many locations that are otherwise inaccessible to other mobile manipulation platforms such as ground vehicles. Despite recent work, several major challenges remain to be overcome before it will be practical to manipulate objects from UAVs. Among these challenges are: (a) The constantly moving UAV platform and compliance of manipulator arms make it difficult to position the UAV and end-effector relative to an object of interest precisely enough for manipulation, and (b) The motions of the manipulator impact the stability of the host UAV, further complicating positioning. Solving these challenges will bring UAVs one step closer to being able to perform meaningful tasks such as infrastructure repair, disaster response, casualty extraction, and cargo resupply. Toward solutions to these challenges, this paper describes a hyper-redundant manipulator, manipulator control approaches and system design considerations to position the manipulator relative to objects of interest in such a way that impacts on platform stability are minimized.     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

基于遗传算法的机械臂实时避障路径规划研究

针对模块化机械臂在运行时可能与工作空间中的障碍物发生碰撞的问题, 提出一种基于遗传算法的避障路径规划算法。首先采用D-H(Denavit-Hartenberg)表示法对机械臂进行建模, 并进行运动学和动力学分析, 建立机械臂运动学和动力学方程。在此基础上, 利用遗传算法分别在单/多个障碍物工作环境中, 以运动的时间、移动的空间距离和轨迹长度作为优化指标, 实现机械臂避障路径规划的优化。通过仿真验证了基于遗传算法的机械臂避障路径规划算法的有效性与可行性, 该算法提高了运行中的机械臂有效避开工作空间中障碍物的效率。     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

基于改进RBPF算法的轮式机器人SLAM导航系统设计

传统的机器人导航系统在复杂的地形环境中常常无法引导机器人躲避突然出现的障碍物,无法精准采集数据;为此提出一种改进RBPF算法的轮式机器人SLAM导航系统,对系统硬件和软件进行设计;改进RBPF算法是一种滤波算法,将激光雷达与里程计的信息作为提议分布,提高了导航精度;系统硬件主要由导航功能模块、底盘驱动模块、控制模块组成,利用RPLIDAR A1型激光雷达设计导航功能模块,并设计底盘驱动模块和控制模块;软件设计中,以改进RBPF算法为基础,设计了轮式机器人SLAM导航系统的实现程序,应用算法代入的方式加强了普通轮式机器人导航算法对粒子计算与卡尔曼滤波的敏感程度;实验结果表明,在有障碍物的室内场景中,与传统滤波算法以及基于软件库系统相比,改进RBPF算法规划的路径更短,导航错误点出现率降低了30%左右。     

基于Arduino的家用智能垃圾桶设计

介绍了一种基于Arduino的家用智能垃圾桶。主要以Arduino UNO R3为核心,利用近红外传感器避障模块、电机驱动模块、超声波检测模块、语音识别模块、舵机驱动模块和无线通信模块搭建其硬件电路,程序调试,实现自动避障、垃圾桶盖自动闭合、手机和语音控制其移动状态等功能。     

基于S3C2410的超声波避障系统设计

通过设计基于S3C2410＋Linux系统的移动机器人平台未知环境下的避障导航系统。介绍了新型超声波传感器的工作原理以及机器人平台整个软件实现流程,并结合开发的超声波驱动程序实现了机器人的避障功能。     

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

This paper proposes a new approach for solving the problem of obstacle avoidance during manipulation tasks performed by redundant manipulators. The developed solution is based on a double neural network that uses Q-learning reinforcement technique. Q-learning has been applied in robotics for attaining obstacle free navigation or computing path planning problems. Most studies solve inverse kinematics and obstacle avoidance problems using variations of the classical Jacobian matrix approach, or by minimizing redundancy resolution of manipulators operating in known environments. Researchers who tried to use neural networks for solving inverse kinematics often dealt with only one obstacle present in the working field. This paper focuses on calculating inverse kinematics and obstacle avoidance for complex unknown environments, with multiple obstacles in the working field. Q-learning is used together with neural networks in order to plan and execute arm movements at each time instant. The algorithm developed for general redundant kinematic link chains has been tested on the particular case of PowerCube manipulator. Before implementing the solution on the real robot, the simulation was integrated in an immersive virtual environment for better movement analysis and safer testing. The study results show that the proposed approach has a good average speed and a satisfying target reaching success rate.     

A heterogeneous modular robotic design for fast response to a diversity of tasks

This paper describes a heterogeneous modular robot system design which attempts to give a quick solution to a diversity of tasks. The approach is based on the use of an inventory of three types of modules i.e., power and control module, joint module and specialized module. Each module type aims to balance versatility and functionality. Their design permits rapid and cost effective design and fabrication. They are interchangeable in different ways to form different robot or system configurations. Depending on the task, the operator decides what type of robot can provide the best performance within the mission. A spherical joint module is described and used to build different robots, hence, forward and inverse kinematics models are obtained. Finally, from the modules described in this work, several robot configurations such as robotic arms, leg-based robots and wheel-based robots are assembled to demonstrate the execution of manipulation and locomotion tasks.     

State space models of remote manipulation tasks

A state variable formulation of the remote manipulation problem is presented, applicable to human-supervised or autonomous computer-manipulators. A discrete state vector, containing position variables for the manipulator and relevant objects, spans a quantized state space comprising many static configurations of objects and hand. A manipulation task is a desired new state. State transitions are assigned costs and are accomplished by commands: hand motions plus grasp, release, push, twist, etc. In control theory terms the problem is to find the cheapest control history (if any) from present to desired state. A method similar to dynamic programming is used to determine the optimal history. The system is capable of obstacle avoidance, grasp rendezvous, incorporation of new sensor data, remembering results of previous tasks, and so on.