A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

Glass Recognition and Map Optimization Method for Mobile Robot Based on Boundary Guidance

Current research on autonomous mobile robots focuses primarily on perceptual accuracy and autonomous performance.In commercial and domestic constructions,concrete,wood,and glass are typically used.Laser and visual mapping or planning algorithms are highly accurate in mapping wood panels and concrete walls.However,indoor and outdoor glass curtain walls may fail to perceive these transparent materials.In this study,a novel indoor glass recognition and map optimization method based on boundary guidance is proposed.First,the status of glass recognition techniques is analyzed comprehensively.Next,a glass image segmentation network based on boundary data guidance and the optimization of a planning map based on depth repair are proposed.Finally,map optimization and path-planning tests are conducted and compared using different algorithms.The results confirm the favorable adaptability of the proposed method to indoor transparent plates and glass curtain walls.Using the proposed method,the recognition accuracy of a public test set increases to 94.1%.After adding the planning map,incorrect coverage redundancies for two test scenes reduce by 59.84%and 55.7%.Herein,a glass recognition and map optimization method is proposed that offers sufficient capacity in perceiving indoor glass materials and recognizing indoor no-entry regions.

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

Resonant-Inductive Wireless Power Transfer for Autonomous Mobile Robots

Due to the increasing commercial interest in autonomy and sustainability,this paper reviews and presents a comprehensive summary of the resonant-inductive power transmission(RPT)technology for autonomous mobile robots.It outlines historic and recent research activities in wireless power transmission,covering the fundamental operation of microwave,capacitive and inductive power transfer technologies,state-of-the-art developments in RPT for high-power applications,current design and health standards,technological drawbacks,and possible future trends.In this paper,coupling-enhanced pad designs,adaptive tuning techniques,compensation network designs,and control techniques are explored.Major design issues such as coupling variation,frequency splitting,and bifurcation are reviewed.The difference between maximum power transfer and maximum energy efficiency is highlighted.Human exposure guidelines are summarized from documentations provided by the Institute of Electrical and Electronics Engineers(IEEE)and the International Commission on Non-ionizing Radiation Protection(ICNIRP).Other standards like WPC’s Qi and Airfuel design standards are also summarized.Finally,the possible trends of the relevant research and development,particularly dynamic charging,are discussed.The intention of this review is to encourage designs that will relieve robot operators of the burden of frequent manual recharging,and to reduce downtime and increase the productivity of autonomous mobile robots in industrial environments.

Concept of Intelligent Mechanical Design for Autonomous Mobile Robots

The concept of Intelligent Mechanical Design （IMD） is presented to show how a mechanical structure can be designed to affect robot controllability, simplification and task performance. Exploring this concept produces landmarks in the territory of mechanical robot design in the form of seven design principles. The design principles, which we call the Mecha-Telligence Principles （MTP）, provide guidance on how to design mechanics for autonomous mobile robots. These principles guide us to ask the right questions when investigating issues concerning self-controllable, reliable, feasible, and compatible mechanics for autonomous mobile robots. To show how MTP can be applied in the design process we propose a novel methodology, named as Mecha-Telligence Methodology （MTM）. Mechanical design by the proposed methodology is based on preference classification of the robot specification described by interaction of the robot with its environment and the physical parameters of the robot mechatronics. After defining new terms, we investigate the feasibility of the proposed methodology to the mechanical design of an autonomous mobile sewer inspection robot. In this industrial project we show how a passive-active intelligent moving mechanism can be designed using the MTM and employed in the field.

The Intellectualized Architecture of the Autonomous Micro- Mobile Robot Based- Behavior

Given the difficulty in hand coding task schemes, an intellectualized architecture of the autonomous micro mobile robot based behavior for fault repair was presented. Integrating the reinforcement learning and the group behavior evolution simulating the human's learning and evolution, the autonomous micro mobile robot will automatically generate the suited actions satisfied the environment. However, the designer only devises some basic behaviors, which decreases the workload of the designer and cognitive deficiency of the robot to the environment. The results of simulation have shown that the architecture endows micro robot with the ability of learning, adaptation and robustness, also with the ability of accomplishing the given task.

Mobile Robot Indoor Autonomous Navigation with Position Estimation Using RF Signal Triangulation

In the mobile robotic systems a precise estimate of the robot pose (Cartesian [x y] position plus orientation angle theta) with the intention of the path planning optimization is essential for the correct performance, on the part of the robots, for tasks that are destined to it, especially when intention is for mobile robot autonomous navigation. This work uses a ToF (Time-of-Flight) of the RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bi-dimensional indoor environment, where GPS system is out of range. It’s a new technology utilization making good use of old ultrasonic ToF methodology that takes advantage of high performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass and the result of triangulation Cartesian estimative, are fused in a Kalman filter in the way to perform optimal estimation and correct robot pose. A mobile robot platform with differential drive and nonholonomic constraints is used as base for state space, plants and measurements models that are used in the simulations and for validation the experiments.

CBF-Based Distributed Model Predictive Control for Safe Formation of Autonomous Mobile Robots

A distributed model predictive control(DMPC)method based on robust control barrier function(RCBF)is developed to achieve the safe formation target of multi-autonomous mobile robot systems in an uncertain disturbed environment.The first step is to analyze the safety requirements of the system during safe formation and categorize them into collision avoidance and distance connectivity maintenance.RCBF constraints are designed based on collision avoidance and connectivity maintenance requirements,and security constraints are achieved through a combination.Then,the specified safety constraints are integrated with the objective of forming a multi-autonomous mobile robot formation.To ensure safe control,the optimization problem is integrated with the DMPC method.Finally,the RCBF-DMPC algorithm is proposed to ensure iterative feasibility and stability while meeting the constraints and expected objectives.Simulation experiments illustrate that the designed algorithm can achieve cooperative formation and ensure system security.

Analytical modeling and multi-objective optimization(MOO) of slippage for wheeled mobile robot(WMR) in rough terrain

Good understanding of relationship between parameters of vehicle, terrain and interaction at the interface is required to develop effective navigation and motion control algorithms for autonomous wheeled mobile robots （AWMR） in rough terrain. A model and analysis of relationship among wheel slippage （S）, rotation angle （0）, sinkage （z） and wheel radius （r） are presented. It is found that wheel rotation angle, sinkage and radius have some influence on wheel slippage. A multi-objective optimization problem with slippage as utility function was formulated and solved in MATLAB. The results reveal the optimal values of wheel-terrain parameters required to achieve optimum slippage on dry sandy terrain. A method of slippage estimation for a five-wheeled mobile robot was presented through comparing the odometric measurements of the powered wheels with those of the fifth non-powered wheel. The experimental result shows that this method is feasible and can be used for online slippage estimation in a sandy terrain.

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.

Investigation on mobile robot navigation based on Kinect sensor

Mobile robot navigation in unknown environment is an advanced research hotspot.Simultaneous localization and mapping(SLAM)is the key requirement for mobile robot to accomplish navigation.Recently,many researchers study SLAM by using laser scanners,sonar,camera,etc.This paper proposes a method that consists of a Kinect sensor along with a normal laptop to control a small mobile robot for collecting information and building a global map of an unknown environment on a remote workstation.The information(depth data)is communicated wirelessly.Gmapping(a highly efficient Rao-Blackwellized particle filer to learn grid maps from laser range data)parameters have been optimized to improve the accuracy of the map generation and the laser scan.Experiment is performed on Turtlebot to verify the effectiveness of the proposed method.

基于ArUco码的无地图AMR视觉辅助归航方法

传统视觉导航方法多依赖于稳定的环境特征和全局一致的地图,对于环境特征及光照要求较高,同时存在无法获得精确的导航位置、收敛性无法保证等问题;因此提出了一种不依赖全局地图,仅通过识别ArUco码信息实现自主移动机器人返航的方法。机器人在出发状态中识别并以时序记录相对于视界内的ArUco码的关键位姿,使得在返航状态中通过选择视界内时序更小的ArUco码并构建局部坐标系从而实现局部定位与导航,并通过局部坐标系的连续变化,实现无全局地图返回出发点的目的。机器人对不同的出发路径实现了返回出发点的结果并实现了静态障碍物的避障功能,在全覆盖路径返航测试中节约了79.16%的路程,同时机器人在20次重复定位测试中实现了平均停止误差0.8cm。该方法展现出对于不同出发路径采用更短的返航路径的能力,具有一定的自适应性。

基于ESP32的智能履带式移动机器人的设计

随着计算机和机器人技术的不断发展,移动机器人呈现出广泛的应用前景.作为移动机器人的一种,履带式移动机器人可以在危险恶劣的环境中开展工作.研究了一种基于ESP32开发板的智能避障系统,该系统集成了多个传感器和执行器,能够实现自主避障、智能显示障碍物位置信息以及播放音乐等功能.通过详细的硬件设计、软件开发和控制系统分析,对履带式移动机器人进行了系统性的研究.实验结果表明,该机器人具有较高的稳定性和可靠性,能够实现履带式移动机器人的设定功能.研究结果对履带式移动机器人的系统设计提供了新的思路和方法,为进一步拓展移动机器人在救援及其他相关领域的应用奠定了基础.

移动机器人导航路径的自主学习粒子群规划方法

为了减小移动机器人行驶路径长度,提出了基于自主学习粒子群算法的导航路径规划方法。以减小路径长度为目标建立了路径规划模型;为了防止机器人发生碰撞,给出了障碍物膨化处理方法。在粒子群算法中引入了由多种粒子学习策略组成的学习策略池,并给出了粒子对学习策略进行选择的自主学习策略,从而提出了具有较强进化能力的自主学习粒子群算法。经算法性能测试,自主学习粒子群算法的优化能力强于传统粒子群算法和文献[11]改进粒子群算法;将自主学习粒子群算法应用于简单场景和复杂场景的路径规划,该算法规划的路径均值和标准差均小于传统粒子群算法,验证了自主学习粒子群算法在机器人路径规划中的优越性。

基于激光雷达的自主移动机器人实验教学平台设计

为培养高素质复合型的“新工科”人才,满足相关课程的实验教学需求,设计了基于激光雷达的自主移动机器人实验教学平台。平台涉及机械设计、机器人操作系统开发、同时定位与地图构建(SLAM)、路径规划等相关理论知识。利用平台开发了Rviz可视化界面、SLAM仿真、室外建图、路径规划等实验项目。实验结果表明,自主移动机器人软硬件系统的基本性能良好,相关SLAM算法、路径规划方法具有可行性。通过该平台开展实验教学,可以显著提升学生解决工程实际问题能力和创新实践能力。

室内环境下改进的混合路径规划算法

为了解决室内非结构化复杂环境下的机器人在路径规划时常常出现目标点不可达、规划过程产生折角偏移、规划过程无法及时规避动态障碍物等问题,提出一种改进的混合室内路径规划算法。该算法将改进的全局路径规划与改进的局部路径规划算法相融合。首先,优化传统A-Star算法的启发因子,减少搜索范围和节点,再通过角平分线切点法对传统A-Star算法进行平滑处理。其次,综合路径与环境信息,采用改进的人工势场算法进行局部路径规划,通过修正斥力场参数来解决目标点不可达问题,同时构造了动态的势力场函数,使其具备决解决动态障碍物的能力。最后,对混合算法进行实际环境的路径规划实验,比起传统的混合算法文中提出的混合算法在路径规划长度上减少11.4%,运行时间减少11.1%,少经过34个冗余节点,结果表明该融合算法可以有效解决室内非结构化复杂的路径规划问题。

基于轨迹引导的移动机器人导航策略优化算法

针对在杂乱、障碍物密集的复杂环境下移动机器人使用深度强化学习进行自主导航所面临的探索困难,进而导致学习效率低下的问题,提出了一种基于轨迹引导的导航策略优化(TGNPO)算法。首先,使用模仿学习的方法为移动机器人训练一个能够同时提供专家示范行为与导航轨迹预测功能的专家策略,旨在全面指导深度强化学习训练;其次,将专家策略预测的导航轨迹与当前时刻移动机器人所感知的实时图像进行融合,并结合坐标注意力机制提取对移动机器人未来导航起引导作用的特征区域,提高导航模型的学习性能;最后,使用专家策略预测的导航轨迹对移动机器人的策略轨迹进行约束,降低导航过程中的无效探索和错误决策。通过在仿真和物理平台上部署所提算法,实验结果表明,相较于现有的先进方法,所提算法在导航的学习效率和轨迹平滑方面取得了显著的优势。这充分证明了该算法能够高效、安全地执行机器人导航任务。

激光SLAM技术下移动机器人自主导航优化研究

在进行自主导航时,移动机器人躲避障碍物的能力较差,使得完成任务需要的时间较长。为此,提出一种激光SLAM技术下移动机器人自主导航优化方法。利用图优化理论,获取全部位姿拓展节点对应的子节点,通过闭环检测,计算误差最小的位姿,根据SPA算法更新位姿,完成移动机器人的位姿估计。利用激光SLAM技术,输出优化后移动机器人位姿,使用A*算法,得到最优通行路径,引入DWA算法展开轨迹计算,实现移动机器人自主导航优化。实验结果表明,所提方法能够准确估计移动机器人位姿,在自主导航优化后,移动机器人自主导航路径长度仅为1 900 mm,转角次数仅为2次,运行周期仅为190次,平均速度高达280 mm/s,具有较好的移动机器人自主导航优化效果。

基于EKF的AMR锂电池SOC动态估计研究

AMR在科研和军事方面的应用决定了其需要一个能够准确、实时估计出锂电池SOC的估计值。由于AMR工作中的放电具有很强的动态性,使得传统估计方法用于锂电池SOC的动态估计效果很不理想。本文描述了EKF方法在AMR锂电池SOC动态估计中的应用。模拟AMR工况进行动态放电试验。试验结果表明,该估计方法能够实时而准确地得到SOC值。

移动机器人订单拣选系统优化研究综述

在物流4.0以及电子商务时代背景下,仓储配送中心在供应链中扮演着至关重要的角色,其中,订单拣选系统作为仓储作业的核心,备受学术界和工业界的关注。首先,介绍移动机器人(Autonomous Mobile Robots,AMR)订单拣选系统的类型,重点关注AMR-assisted系统、AMR-pick系统和移动机器人履行系统的优化内容和优化目标;其次,针对上述三类系统,从布局设计、存储分配、订单分批、任务分配、路径规划以及联合优化等方面综述其研究进展;最后,总结当前研究中存在的不足,并提出若干进一步研究内容。