基于激光雷达与双目视觉的移动机器人SLAM研究

为解决移动机器人在环境未知条件下,利用单一传感器自主导航时不能及时定位、构建地图不精确的问题,提出采用一种改进RBPF算法,在计算提议分布时将移动机器人的观测数据(视觉信息与激光雷达信息)和里程计信息融合;针对一般视觉图像特征点提取算法较慢的问题,采用基于ORB算法对视觉图像进行处理以加快视觉图像处理速度的方法;最后通过在安装有开源机器人操作系统(ROS)的履带式移动机器人进行实验,验证了采用该方法可构建可靠性更高、更精确的2D栅格图,提高了移动机器人SLAM的鲁棒性.     

基于生物认知的移动机器人路径规划方法

针对移动机器人在非结构环境下的导航任务,受哺乳动物空间认知方式的启发,提出一种基于生物认知进行移动机器人路径规划的方法．结合认知地图特性,模拟海马体的情景记忆形成机理,构建封装了场景感知、状态神经元及位姿感知相关信息的情景认知地图,实现了机器人对环境的认知．基于情景认知地图,以最小事件距离为准则,提出事件序列规划算法用于实时导航过程．实验结果表明,该控制算法能使机器人根据不同任务选择最佳规划路径．     

A team of mobile robots and monitoring sensors—from concept to experiment

This paper describes the underlying concepts, architecture and implementation of a robotic system consisting of heterogenous mobile robots and stationary sensors, cooperating in a task of collective perception and world modeling. The navigation capability of a group of robots can be improved by sharing available information about the state of the environment (the environment model) and information about the relative position estimates. The information sharing can be especially beneficial to the robots when there are also some stationary monitoring sensors (e.g. cameras) available in the environment, which can serve as external navigation aids. In the article, information processing performed by individual members of the team—robots and sensors—is analyzed and a unifying multi-agent blackboard architecture is described. For information sharing between robots and monitoring sensors, a framework based on the idea of the Contract Net Protocol is proposed. The communication backbone provides agents with unified communication interfaces. The experimental set-up is described. The results of tests validating the correctness of the design on the tasks of cooperative localization and world-model building are reported. A discussion and comparison to other multi-robot systems closes the article.     

Mobile Robot Navigation for Moving Obstacles with Unpredictable Direction Changes,Including Humans

In many service applications, mobile robots need to share their work areas with obstacles. Avoiding moving obstacles with unpredictable direction changes, such as humans, is more challenging than avoiding moving obstacles whose motion can be predicted. Precise information on the future moving directions of humans is unobtainable for use in navigation algorithms. Furthermore, humans should be able to pursue their activities unhindered and without worrying about the robots around them. An enhanced virtual force field-based mobile robot navigation algorithm (termed EVFF) is presented for avoiding moving obstacles with unpredictable direction changes. This algorithm may be used with both holonomic and nonholonomic robots. It incorporates improved virtual force functions and an improved method for selecting the sense of the detour force to better avoid moving obstacles. For several challenging obstacle configurations, the EVFF algorithm is compared with five state-of-the-art navigation algorithms for moving obstacles. The navigation system with the new algorithm generated collision-free paths consistently. Methods for solving local minima conditions are proposed. Experimental results are also presented to further verify the avoidance performance of this algorithm.     

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.     

Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles

We present a sliding mode based strategy for a unicycle-like robot navigation and guidance. The proposed navigation law is applied to the problems of patrolling the border of a moving and deforming domain and reaching a target through a dynamic environment cluttered with moving obstacles. Mathematically rigorous analysis of the proposed approach is provided. The convergence and performance of the algorithm is demonstrated via experiments with real robots and extensive computer simulation.     

Dynamic Analysis and Distributed Control of the Tetrobot Modular Reconfigurable Robotic System

Reconfigurable robotic systems can be adapted to different tasks or environments by reorganizing their mechanical configurations. Such systems have many redundant degrees of freedom in order to meet the combined demands of strength, rigidity, workspace kinematics, reconfigurability, and fault tolerance. In order to implement these new generations of robotic system, new approaches must be considered for design, analysis, and control. This paper presents an efficient distributed computational scheme which computes the kinematics, dynamics, redundancy resolution, and control inputs for real-time application to the control of the Tetrobot modular reconfigurable robots. The entire system is decomposed into subsystems based on a modular approach and Newton's equations of motion are derived and implemented using a recursive propagation algorithm. Two different dynamic resolution of redundancy schemes, the centralized Jacobian method and the distributed virtual force method, are proposed to optimize the actuating forces. Finally, distributed dynamic control algorithms provide an efficient modular implementation of the control architecture for a large family of configurations.     

基于深度强化学习的欠驱动仿生机器鳗鱼控制研究

水下仿生机器人具有高效率、高机动性、低噪声等优点,针对仿生机器鳗鱼存在设计复杂、控制难度大等问题,该文提出了一种新型欠驱动机器鳗鱼的控制方法。首先,基于主动加被动的仿生机构推进原理,设计了两段主动体与两段被动顺从体相结合的机器鳗鱼仿生机构;然后,在仿真环境中进行建模,利用深度强化学习算法进行数据收集和训练,选择表现良好的神经网络在仿真环境中进行控制测试,从而得到机器鳗鱼的控制函数;最后,通过对比实验,验证了该文设计方法的可行性以及控制函数的有效性,实现了对机器鳗鱼的控制。     

异构多目标差分-动态窗口算法 及其在移动机器人中的应用

为了实现在多移动机器人和多窄通道的复杂动态环境中机器人的节能运动规划,提出异构多目标差分-动态窗口法(heterogeneous multi-objective differential evolution-dynamic window algorithm,HMODE-DWA).首先,建立行驶时间、执行器作用力和平滑度的3目标优化模型,设计具有碰撞约束的异构多目标差分进化算法来获得3个目标函数的最优解,进而在已知的静态环境中获得帕累托前沿,利用平均隶属度函数获得起点与终点间最优的全局路径;其次,定义基于环境缓冲区域的模糊动态窗口法使机器人完成动态复杂环境中避障,利用所提出的HMODE-DWA算法动态避障的同时实现节能规划.仿真和实验结果表明,所提出的混合路径规划控制策略能够有效降低移动机器人动态避障过程中的能耗.     

Distributed trajectory generation for cooperative multi-arm robotsvia virtual force interactions

A trajectory generation method for multi-arm robots through cooperative and competitive interactions among multiple end-effecters is proposed. The method can generate the trajectories of the multiple arms in a distributed manner based on a concept of a virtual interaction force which represents an interaction between an end-effector and an environment. It is shown that the method is effective not only for simple cooperative tasks such as positioning a common object, but also for more complicated tasks including relative motions among arms.     

Evolving a modular neural network-based behavioral fusion usingextended VFF and environment classification for mobile robot navigation

A local navigation algorithm for mobile robots is proposed that combines rule-based and neural network approaches. First, the extended virtual force field (EVFF), an extension of the conventional virtual force field (VFF), implements a rule base under the potential field concept. Second, the neural network performs fusion of the three primitive behaviors generated by EVFF. Finally, evolutionary programming is used to optimize the weights of the neural network with an arbitrary form of objective function. Furthermore, a multinetwork version of the fusion neural network has been proposed that lends itself to not only an efficient architecture but also a greatly enhanced generalization capability. Herein, the global path environment has been classified into a number of basic local path environments to which each module has been optimized with higher resolution and better generalization. These techniques have been verified through computer simulation under a collection of complex and varying environments     

A Coordinated Navigation Strategy for Multi-Robots to Capture a Target Moving with Unknown Speed

The paper proposes an algorithm for multi-robot coordination and navigation in order to intercept a target at a long distance. For this purpose, a limit cycle based algorithm using a neural oscillator with phase differences is proposed. The state of target is unknown, under the assumption that it is stationary or in motion with constant unknown speed along a straight line. Using the proposed algorithm, a group of robots is intended to move towards the target in such a way that the robots surround it. While moving to the target, self-collision between the robots is avoided. Moreover, a collision avoidance with static obstacles as well as dynamic target is realized. The robots reach the target at a desired distance, keeping uniformly distributed angles around the target. The algorithm is further extended so that a static interception point for the target can be estimated in place of pursuing a dynamic target, which is referred to as a virtual target in this paper. In other words, the robots move towards the virtual target instead of the actual target. The robots ultimately encircle the actual target when they arrive at the virtual target. The effectiveness of the proposed method is verified through simulation results.     

A Human–Robot Cooperative Learning System for Easy Installation of Assistant Robots in New Working Environments

One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcalá, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human–robot interfaces (basically speech) and user–robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.     

基于轮式机器人的实时3D栅格地图构建

移动机器人在各种任务中需要进行建图、定位和路径规划，但是目前的视觉SLAM只能输出相机的运动轨迹图，而不能生成用于路径规划和导航的地图。因此，在ORB_SLAM2的基础上，与RGB-D相机相结合，提出了一种实时3D栅格地图构建算法。建立了一个逆传感器模型（Inverse Sensor Model，ISM）；针对ISM模型，重新构建了3D栅格地图的算法；联合ORB_SLAM2进行数据集实验、仿真环境实验和实时构建实验。经实验验证，该算法能够利用ORB_SLAM2实时构建出具有尺度的3D栅格地图，且能够清晰地显示障碍物位置，验证了该算法的有效性。     

集成几何约束与物性仿真技术的虚拟装配

针对传统装配定位导航中零部件位姿的突变问题,利用线性插值与四元数球形插值 的方法,求解零部件约束定位变换过程的渐变运动;同时利用导纳式力觉交互控制模式,解决了 约束定位变换后的控制信号分离问题;在装配运动导航阶段,将约束几何元素具象化为力觉约束 模型,以满足虚拟装配对力觉反馈的需求。针对虚拟装配对物性仿真的需求,将开源物理引擎 Bullet 集成到仿真系统中,提出几何约束与物性仿真技术相结合的装配流程,最后在自主研发的 虚拟装配原型系统中进行了应用实例验证。     

Whole-body motion planning and control of a quadruped robot for challenging terrain

Quadruped robots working in jungles, mountains or factories should be able to move through challenging scenarios. In this paper, we present a control framework for quadruped robots walking over rough terrain. The planner plans the trajectory of the robot's center of gravity by using the normalized energy stability criterion, which ensures that the robot is in the most stable state. A contact detection algorithm based on the probabilistic contact model is presented, which implements event-based state switching of the quadruped robot legs. And an on-line detection of contact force based on generalized momentum is also showed, which improves the accuracy of proprioceptive force estimation. A controller combining whole body control and virtual model control is proposed to achieve precise trajectory tracking and active compliance with environment interaction. Without any knowledge of the environment, the experiments of the quadruped robot SDUQuad-144 climbs over significant obstacles such as 38 cm high steps and 22.5 cm high stairs are designed to verify the feasibility of the proposed method.     

基于USARSim和ROS的无人平台编队仿真系统

针对越野非结构化环境下的地面无人平台(Unmanned ground vehicle, UGV)编队仿真系统存在功能模块不完善及算法集成测试困难等问题, 为便于有效测试地面无人平台编队协同控制方法性能及其适用的任务场景, 降低编队协同系统的开发成本, 本文提出了一种基于USARSim (Unified System for Automation and Robotics Simulator)和ROS (Robot Operating System)的地面无人平台编队协同仿真系统. 该仿真系统由人机交互界面、基于ROS架构的地面无人平台控制系统和基于USARSim的虚拟仿真场景三个部分组成, 其测试对象为地面无人平台编队协同控制算法. 通过充分利用ROS中集成的开源导航算法和USARSim中丰富的机器人及环境模型, 该系统为研究地面无人平台编队协同控制算法提供了新的思路和快速验证工具. 以领航者?跟随者编队控制方法为例进行该仿真系统的性能测试, 实验结果表明, 该仿真系统能够在外界条件一致的情况下完成对编队协同控制方法的性能测试, 系统稳定可靠.     

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.     

Bilateral teleoperation of a group of mobile robots for cooperative tasks

A visual and force feedback-based teleoperation scheme is proposed for cooperative tasks. The bilateral teleoperation system includes a haptic device, an overhead camera and a group of wheeled robots. The commands of formation and average velocities of the multiple robots are generated by the operator through the haptic device. The state of the multiple robots and the working environment is sent to the human operator. The received information contains the feedback force through the haptic device and visual information returned by a depth camera. The feedback force based on the difference between the desired and actual average velocities is presented. The wave variable method is employed in the bilateral teleoperation of multiple mobile robots with time delays. The effectiveness of the bilateral teleoperation system is demonstrated by experiments. The robots in the slave side are able to follow the commands from the master side to interact with the environments, including moving in different formations and pushing a box. The results show that the scheme enables the operator to manipulate a group of robots to complete cooperative tasks freely.     

Functional Modeling for Monitoring of Robotic System

With the expansion of robotic applications in the industrial domain, it is important that the robots can execute their tasks in a safe and reliable way. A monitoring system can be implemented to ensure the detection of abnormal situations of the robots and report the abnormality to their human supervisors or cooperators. In this work, we focus on developing a modeling framework for monitoring robotic system based on means-end analysis and the concept of action phases from action theory. A circular cascaded action phase structure is proposed for building the model of cyclical robotic events. This functional model provide a formal way of decompose robotic tasks and analyze each level of conditions for an action to be executed successfully. It can be used for monitoring robotic systems by checking the preconditions in the action phases and identifying the failure modes. The proposed method is demonstrated by using a simulated robotic manipulation system. The simulation results demonstrate the feasibility of the developed functional model in finding errors during the execution monitoring.