Robust Tracking Control for Self-balancing Mobile Robots Using Disturbance Observer

In this paper,a robust tracking control scheme based on nonlinear disturbance observer is developed for the self-balancing mobile robot with external unknown disturbances.A desired velocity control law is firstly designed using the Lyapunov analysis method and the arctan function.To improve the tracking control performance,a nonlinear disturbance observer is developed to estimate the unknown disturbance of the self-balancing mobile robot.Using the output of the designed disturbance observer,the robust tracking control scheme is presented employing the sliding mode method for the selfbalancing mobile robot.Numerical simulation results further demonstrate the effectiveness of the proposed robust tracking control scheme for the self-balancing mobile robot subject to external unknown disturbances.

On Self Recovery Mechanism and Additional Maneuverability of a Mini Mobile Robot

Ordinary mobile robots have some kind of moving mechanisms attached to one rigid body. When working on rough terrain or in other hazard environments, there existed some possibilities that the robot will be turned up side down, thus causing losses to the robot's expedition. Multi bodied mobile robots provide a solution to that problem. Using active joints between bodies, the robot can recover from turnover situation by itself. In this paper, the authors discuss the joint arrangements and the additional maneuverability resulted from joints between body segments.

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method.In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.

Self Recovery of Localization Loss for Indoor Mobile Robot

In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.

Astrocyte-Synapse Receptor Coupling in Tripartite Synapses: A Mechanism for Self-Observing Robots

A model of an intentional self-observing system is proposed based on the structure and functions of astrocyte-synapse interactions in tripartite synapses. Astrocyte-synapse interactions are cyclically organized and operate via feedforward and feedback mechanisms, formally described by proemial counting. Synaptic, extrasynaptic and astrocyte receptors are interpreted as places with the same or different quality of information processing described by the combinatorics of tritograms. It is hypothesized that receptors on the astrocytic membrane may embody intentional programs that select corresponding synaptic and extrasynaptic receptors for the formation of receptor-receptor complexes. Basically, the act of self-observation is generated if the actual environmental information is appropriate to the intended observation processed by receptor-receptor complexes. This mechanism is implemented for a robot brain enabling the robot to experience environmental information as “its own”. It is suggested that this mechanism enables the robot to generate matches and mismatches between intended observations and the observations in the environment, based on the cyclic organization of the mechanism. In exploring an unknown environment the robot may stepwise construct an observation space, stored in memory, commanded and controlled by the intentional self-observing system. Finally, the role of self-observation in machine consciousness is shortly discussed.

A New Dynamic Self-Organizing Method for Mobile Robot Environment Mapping

To solve the mapping problem for the mobile robots in the unknown environment, a dynamic growing self-organizing map with growing-threshold tuning automatically algorithm (DGSOMGT) based on Self-organizing Map is proposed. It introduces a value of spread factor to describe the changing process of the growing threshold dynamically. The method realizes the network structure growing by training through mobile robot movement constantly in the unknown environment. The proposed algorithm is based on self-organizing map and can adjust the growing-threshold value by the number of network neurons increasing. It avoids tuning the parameters repeatedly by human. The experimental results show that the proposed method detects the complex environment quickly, effectively and correctly. The robot can realize environment mapping automatically. Compared with the other methods the proposed mapping strategy has better topological properties and time property.

Piezoelectrically Actuated Biomimetic Self-Contained Quadruped Bounding Robot

This paper presents the development of a mesoscale self-contained quadruped mobile robot that employs two pieces of piezocomposite actuators for the bounding locomotion.The design of the robot leg is inspired by legged insects and animals, and the biomimetic concept is implemented in the robot in a simplified form,such that each leg of the robot has only one degree of freedom.The lack of degree of freedom is compensated by a slope of the robot frame relative to the horizontal plane.For the implementation of the self-contained mobile robot,a small power supply circuit is designed and installed on the robot.Experimental results show that the robot can locomote at about 50 mm·s^(-1)with the circuit on board,which can be considered as a significant step toward the goal of building an autonomous legged robot actuated by piezoelectric actuators.

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.

Self-Structured Organizing Single-Input CMAC Control for De-icing Robot Manipulator

This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized;that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.

助行康复机器人自适应步态控制方法研究

为了提高助行康复机器人在不同坡度上的稳定性,合理的步态控制方法显得尤为重要。这里以助行康复机器人为研究对象,提出了一种将长短期记忆网络和动态运动基元结合的步行康复机器人自适应步态控制方法。将传感器采集信息与动态运动基元中步态控制项相结合,对参考步态进行调整。同时通过更改动态运动基元的时间项和空间项对步态进行二次调整以适应不同的坡度。通过试验对该控制方法的可用性进行验证。结果表明,该方法可以在(0~20)°斜坡进行自适应步态变换,具有一定的实用价值。

采用社会约束自适应动态窗口法的服务机器人路径规划

针对传统动态窗口法在行人密集环境下动态路径规划存在灵活性差、效率低、安全性缺乏等问题,提出一种社会交互空间下基于社会约束自适应动态窗口法(social_DWA),并采用其解决服务机器人局部路径规划问题。首先,采用非对称高斯公式对单行人以及多人群组交互空间进行模型化描述;其次,在传统动态窗口法的基础上,采用动态行人方位角约束对动态行人进行避让;改进距离评价函数,分类决策与行人、多人群组、一般障碍物的安全距离;最后,提出速度权重自适应调整策略,优化服务机器人在途经不同密集度社会交互区域时的移动速度。为验证算法有效性,在两种模拟社会场景下,先后开展了social_DWA算法与传统DWA算法、FIDWA算法的路径规划仿真对比实验。结果表明:采用social_DWA算法所消耗的运动时间在场景1中较传统DWA和FIDWA算法分别缩短了1.53、0.43 s,在场景2中较传统DWA和FIDWA算法分别缩短了26.3、2.86 s;相较于传统DWA算法和FIDWA算法,social_DWA算法能保持有效的行人安全距离,并使运行轨迹更加合理。social_DWA算法在行人避让、环境适应能力等方面具有一定的优越性。

丘陵山地柑橘果园机器人自主导航与精确控制系统设计与试验

为实现丘陵山地柑橘果园机器人的自主导航,设计了一种基于激光雷达和惯性传感器的果园自主导航系统.使用激光SLAM融合多传感器所获数据,并利用误差状态卡尔曼滤波器进行位姿优化,从而构建果园的三维全局点云地图.对全局地图进行分析和处理,确定机器人的安全行驶区域,并应用基于机器人运动学多约束条件的三次非均匀B样条曲线轨迹优化算法实现路径规划,使用NDT-ICP算法实现机器人在全局地图中的定位.为适应丘陵山地果园地形复杂性,提出了一种基于预瞄跟踪的自校正增量PID控制策略,利用递归最小二乘法实时调整PID参数,并在机器人履带式行走机构上进行了系统整合测试.试验结果表明,误差状态卡尔曼滤波器优化后的地图精度与优化前相比有明显提升.机器人以1.2 m/s速度行驶时,导航控制系统的直线行驶平均位置偏差和航向角偏差分别为0.18 m和4.2°,转弯行驶平均位置偏差和航向角偏差分别为0.38 m和16.7°,可满足丘陵山地柑橘果园智能农机自主导航作业需求.

串联工业机器人的自适应稳态增益调整技术

由于时滞控制具有模型无关性、简单性和鲁棒性等优点,在工业机器人控制中得到了广泛的应用。然而,过高的增益会导致系统响应不稳定或振荡。为了解决这一问题,提出了一种用于串联机械臂时滞控制的自适应增益调整算法。首先,通过机器人机械臂时滞控制原理分析表明了恒定增益存在的缺陷。然后,采用滑动变量和边界条件为时滞控制设计了一种自适应增益调整算法,能够在无需大量的调整参数的条件下,自动将增益调整到满足时滞控制稳定性条件的稳定值。仿真和实验结果验证了所提自适应增益调整算法的有效性。研究结果表明,与传统的时滞控制(大增益)相比,所提算法将机械手跟踪的均方根误差和最大误差分别减小了31.2%和61.4%,并且即使在初始值不稳定的情况下,仍可以提供稳定的增益,控制性能显著提高。

基于TRIZ的全断面铺锚支机器人结构创新设计

为解决煤矿巷道掘进过程中支护、铺网、钻孔及锚固等工序自动化水平低、难以高效协同等技术难题,基于TRIZ理论,开展全断面铺锚支机器人的结构创新设计。通过九屏分析法、功能模型图与因果轴分析得出现有铺锚支机器人效率不足的根本原因,并综合运用技术矛盾法、物理矛盾法以及76种标准解等方法,设计出新型全断面铺锚支机器人结构,进而通过ADAMS-AMESim联合仿真,开展其关键迈步式自移支架的机液耦合分析。结果表明:全断面铺锚支机器人具备迈步行走、铺网、支护、锚固、钻孔的平行作业;迈步式自移支架工作时推移缸的最大推架力为10940 N,最大拉架力为18510 N,实际位移与理想位移误差满足设计要求。

教育机器人在空巢老年糖尿病患者延续护理中的应用

目的 探讨空巢老年糖尿病患者运用教育机器人实施延续护理的效果。方法 选取118例空巢老年糖尿病患者作为研究对象,按入院时间分为观察组和对照组各59例,对照组接受常规延续护理方案,观察组应用教育机器人进行延续护理。6个月后对两组血糖相关指标、自我管理行为得分、糖尿病特异性生存质量得分进行比较。结果 出院后6个月复查时观察组自我管理行为得分、血糖相关指标、糖尿病特异性生存质量得分显著优于对照组(均P<0.05)。结论 应用教育机器人对空巢老年糖尿病患者进行延续护理,能够提升空巢老年糖尿病患者自我管理能力,改善血糖指标,提升生活质量。

跨行自走式除草机器人设计及仿真分析——基于EDEM

杂草去除是农业生产中不可避免的问题,因此设计了一种跨行自走式除草机器人,能够同时实现行间和株间除草,且行间除草的同时能够起到深松土壤的作用。通过曲柄摇杆机构实现除草刀的摆动,来避开农作物。利用EDEM软件对固定除草深度和速度下不同入土角度的除草过程进行仿真分析,结果表明:相同除草深度和速度下,除草刀具受力随着入土角度的增大而先减小后增大;行间除草刀具选取45°入土角为最优工作参数,株间除草刀具选取60°入土角为最优工作参数;通过仿真计算土壤蓬松度和土壤扰动系数,得到的结果是土壤蓬松度在10%~40%之间、土壤扰动系数大于50%,符合松土壤扰动效果的评价指标。

足式机器人腿部关节改进单神经网络PID控制算法研究

为了满足液压足式机器人在复杂环境中实现精确、快速的腿部关节控制需求,把单神经网络PID能够实时调节参数的优点运用到足式机器人液压机械腿关节的控制中,在单神经网络PID的基础上增加机械腿关节的位置和速度控制算法,形成改进单神经网络PID,实现了对神经元比例参数自调整、PID参数的自整定,能够较好地适应内、外参数的变化,增强了腿部关节的快速性、精确性。在Simulink中进行建模仿真以及在设计的以STM32为中央处理芯片的控制平台上进行实验测试,结果表明:改进单神经网络PID在足式液压机器人的腿部关节控制中具有响应速度快、超调量小、控制精度高、鲁棒性强等优点。

基于电磁超声的工业管道自驱式壁厚连续扫查内检测系统

腐蚀减薄是工业管道定期检验重点关注的损失模式,文中针对该问题研发了一种自驱式管道壁厚连续扫查内检测系统。文中设计了一种收发双线圈构型的电磁超声传感器,研制了自驱式管道内检测机器人与高裕量随动贴合的传感器搭载装置。在测试管道上开展了在线壁厚扫查实验,实验结果表明:自驱式电磁超声内检测系统在管道主要结构处均能稳定检测壁厚,检测数据可靠,缺陷定位准确,适用于传统外检测不可达等服役工况下钢质管道的壁厚动态测量。

可重构仿生四足机器人倾覆后恢复机理与特性研究

农业环境起伏多变,边界模糊,大多呈非结构化分布。四足机器人在复杂农业环境中作业时,易出现因倾覆失去运动能力的情况,因此机器人需具备倾覆后自我恢复能力。传统四足机器人倾覆后恢复多数依靠腿部运动来实现,而可重构四足机器人,可通过躯干与腿部协调运动来实现倾覆后自我恢复。本文基于可重构躯干构型多变,得到了多种仿生形态的可重构四足机器人,规划了基于可重构理论的倾覆后恢复机理。对比倾覆后可重构机器人利用躯干拱起折叠和单侧翻转折叠两种恢复方式,分析R1型、R2型可重构四足机器人恢复的运动特性。利用软件ADAMS进行仿真,对仿真数据进行分析,证明了可重构躯干比刚性躯干更有效地减少了恢复过程中的冲击。最后设计样机并进行实验,验证机理实施的可行性与稳定性,研究结果表明其可降低实现静态自我恢复的难度。

冗余铺放机器人的自运动流形及逆解优化研究

针对用于复合材料铺放成型的八自由度冗余机器人系统的逆运动学问题,提出了一种求解其自运动流形及逆解优化的算法。首先,基于位姿分离思想定义了位置子流形和姿态子流形,采用几何求解法得到了基于自运动变量的参数化方程形式的位置子流形和姿态子流形;然后,根据机器人的关节约束和实际铺放工艺过程,对给定的铺放位姿进行了位置子流形和姿态子流形配对的仿真,并用流形中若干组逆解通过正向运动学得到了末端位姿矩阵(其结果均与给定位姿相符,从而验证了自运动流形求解过程的正确性);最后,在完成自运动流形求解的基础上,从机械臂运动的平滑性和关节运动变化量两个方面出发,提出了一种基于自运动变量参数化方程的全局优化目标函数;以飞机尾椎模型为实验研究对象,对其中一条铺放路径进行了逆解全局优化仿真,得到了自运动变量和机器人各关节角在铺放路径上的优化曲线,将仿真结果与一种多目标优化算法进行了比较。研究结果表明:针对相同的铺放路径,采用上述优化算法后关节角变化总量降低了11.25%;该算法能够有效地求解八自由度冗余铺放机器人系统的自运动流形,基于全局优化目标函数,其能够在自运动流形中寻找出自运动变量和机器人各关节的最优解。该算法同样也适用于其他位姿解耦的冗余机器人系统逆解问题的求解。