An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

Reflex-oscillations in evolved single leg neurocontrollers for walking machines

As a prerequisite for developing neural control for walking machines that are able to autonomously navigate through rough terrain, artificial structure evolution is used to generate various single leg controllers. The structure and dynamical properties of the evolved (recurrent) neural networks are then analysed to identify elementary mechanisms of sensor-driven walking behaviour. Based on the biological understanding that legged locomotion implies a highly decentralised and modular control, neuromodules for single, morphological distinct legs of a hexapod walking machine were developed by using a physical simulation. Each of the legs has three degrees of freedom (DOF). The presented results demonstrate how extremely small reflex-oscillators, which inherently rely on the sensorimotor loop and e.g. hysteresis effects, generate effective locomotion. Varying the fitness function by randomly changing the environmental conditions during evolution, neural control mechanisms are identified which allow for robust and adaptive locomotion. Relations to biological findings are discussed.     

一种基于自由空间法的虚拟人行走规划方法

本文提出了一种基于自由空间法的虚拟人行走规划方法。该方法以虚拟环境的表示为基础,首先将虚拟环境离散化成为环境图;然后用启发式A＊搜索算法进行路径搜索,产生从初始位置到目标位置的最优路径,引导虚拟人对环境进行漫游;最后设计实现了一个仿真演示实例。仿真结果表明,所提算法简便易行,能够满足虚拟人在复杂环境下导
航和漫游的要求。     

A Survey of Underwater Multi-Robot Systems

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.     

基于未知环境碰撞冲突预测的群机器人多目标搜索研究

群机器人在未知动态环境下进行多目标搜索时,存在碰撞预测和搜索效率不高等问题。提出了一种碰撞几何锥和改进惯性权重的粒子群优化算法相结合的多目标搜索策略。首先,根据静、动态威胁物的不同分别引入碰撞锥(CC)和速度障碍法(VO),提出了简化复杂障碍物的膨胀几何法(SG)和一种改进CC和VO的碰撞几何锥模型(CGC);有效解决了复杂不规则威胁物的避碰预测问题,并根据CGC模型作出威胁评估报告以确定最优避障方向。其次,提出一种改进惯性权重的粒子群优化算法(IWPSO),提高了搜索效率同时有效解决了粒子群优化算法易陷入局部最优的问题。最后,将两种改进的方法(CGC-IWPSO)相结合以实现群机器人的多目标任务搜索,相比于简化虚拟受力(SVF)、自适应机器人蝙蝠算法(ARBR)、具有运动学约束的粒子群算法(KCPSO),本文方法在搜索时耗、能耗以及避障次数上分别至少减少了15.59%、 10.14%、 14.12%。     

Robotic harvesting of the occluded fruits with a precise shape and position reconstruction approach

Occlusion is one of the key factors affecting the success rate of vision-based fruit-picking robots. It is important to accurately locate and grasp the occluded fruit in field applications, However, there is yet no universal and effective solution. In this paper, a high-precision estimation method of spatial geometric features of occluded targets based on deep learning and multisource images is presented, enabling the selective harvest robot to envision the whole target fruit as if its occlusions do not exist. First, RGB, depth and infrared images are acquired. And pixel-level matched RGB-D-I fusion images are obtained by image registration. Second, aiming at the problem of detecting the occluded tomatoes in the greenhouse, an extended Mask-RCNN network is designed to extract the target tomato. The target segmentation accuracy is improved by 7.6%. Then, for partially occluded tomatoes, a shape and position restoration method is used to recover the obscured tomato. This algorithm can extract tomato radius and centroid coordinates directly from the restored depth image. The mean Intersection over Union is 0.895, and the centroid position error is 0.62 mm for the occluded rate under 25% and the illuminance between 1 and 12 KLux. And hereby a dual-arm robotic harvesting system is improved to achieve a picking time of 11 s per fruit, an average gripping accuracy of 8.21 mm, and an average picking success rate of 73.04%. The proposed approach realizes a high-fidelity geometrics reconstruction instead of mere image style restoration, which endows the robot with the ability to see through obstacles in the field scenes and improves its operational success rate in its result.     

虚拟人运动建模中的逆向运动学方法研究

针对当前虚拟人运动建模中缺少环境约束这一问题,文中提出了一种基于环境约束的逆向运动学求解方法。主要研究了在环境约束下,如何运用逆向运动学方法使人体的步行运动更加真实直观。首先提出了人体的层次结构模型和关节模型,其次对CCD算法的实现方法进行分析,最后结合CCD算法提出一种在环境约束下调整人体步行运动的方法。解决了运动捕捉方法中动作单一的问题,使虚拟人能够根据环境信息调整现有的动作,最终能够使虚拟人在平坦地面的运动和上下台阶运动都更加灵活真实。     

欠驱动步行机器人实时仿真系统设计

开发了异构多软件平台融合的机器人实时仿真系统。在欠驱动步行机器人动力学建模中引入虚拟样机技术,利用ADAMS软件生成动力学模型,利用MATLAB软件设计控制器,二者通过接口实现联合仿真;在开发人机界面及连接各个模块的接口中引入MATLAB和VC＋＋混合编程技术,完成对硬件的驱动控制及系统平台的操作。该仿真系统实现对欠驱动步行机器人进行理论步态仿真、控制方法的开发及实验研究,对实体机器人的控制调试、实时控制算法的设计。     

Dynamic Motion Planning for Mobile Robots Using Potential Field Method

The potential field method is widely used for autonomous mobile robot path planning due to its elegant mathematical analysis and simplicity. However, most researches have been focused on solving the motion planning problem in a stationary environment where both targets and obstacles are stationary. This paper proposes a new potential field method for motion planning of mobile robots in a dynamic environment where the target and the obstacles are moving. Firstly, the new potential function and the corresponding virtual force are defined. Then, the problem of local minima is discussed. Finally, extensive computer simulations and hardware experiments are carried out to demonstrate the effectiveness of the dynamic motion planning schemes based on the new potential field method.