自由漂浮空间机器人捕获翻滚目标的力-位-型融合控制方法

翻滚目标捕获过程中,空间机器人的运动会对基座产生扰动、接触过程的冲击碰撞容易产生安全风险。因此,为了在捕获翻滚目标的同时抑制空间机械臂对基座的扰动和对目标的碰撞冲击,提出一种自由漂浮空间机器人力-位-型融合控制方法。首先将自由漂浮空间机器人的非完整冗余性、运动冗余性表征为型空间,构建由末端位姿、型状态组合的广义状态空间并建立系统的运动学和动力学模型;通过优化基座与机械臂的位-型及零空间运动,减小机械臂抓捕运动在基座产生的扰动;通过优化基座、机械臂与翻滚目标的相对位-型,减小碰撞冲击力在基座产生的姿态扰动。给定最优位-型及末端操作力,基于阻抗控制原理及零空间投影策略,实现多优先级力-位-型融合控制。最后,开展了空间机器人捕获翻滚目标的典型任务仿真,结果表明通过力-位-型融合优化与控制方法可以有效减小空间机器人捕获过程产生的基座扰动。

多机器人协同路径的时域协作-势场A*规划

为了减小多机器人协同工作路径长度并提高路径平滑度,提出了基于时域协作-势场A*算法的多机器人协同规划方法。建立了机器人工作环境的栅格模型,分析了传统A*算法辅助信息不充分的问题。为了充分利用环境中的障碍物和目标点位置等先验信息,基于势场法将障碍物和目标点信息引入到A*算法辅助信息中,提出了基于势场A*算法的单机器人路径规划方法。使用三维时空图代替二维平面图,可以有效判断出多机器人碰撞问题,并根据碰撞类型制定了避撞策略。经验证,势场A*算法规划的路径长度和平滑度均优于传统A*算法、文献[11]蚁群算法;在多机器人协同路径规划中,在规划出各机器人最优路径基础上,可以有效解决机器人之间的碰撞问题。实验结果验证了时域协作-势场A*算法在多机器人协同路径规划中的有效性和优越性。

基于领航-跟随法的多机器人编队控制

编队控制是多机器人系统研究的核心问题之一,具有广泛的研究价值。针对编队问题,通过对轮式差分驱动机器人运动模型进行分析,并设计相应的控制器。在传统的领航-跟随模型上,引入“虚拟机器人”,将跟随者机器人对领航者机器人的轨迹跟踪转换为跟随者对“虚拟领航者”的轨迹跟踪。首先,预设定实际领航者机器人的行进路径,由实际领航者引导运动方向的运动轨迹被视为主轨迹,虚拟机器人通过数据产生参考点从而生成参考轨迹,跟随者机器人通过对参考轨迹的跟踪与“虚拟领航者”构成轨迹误差跟踪系统。然后通过Lyapunov的控制理论验证控制器的理论可行性,最后在MATLAB/Simulink仿真平台进行实验,通过改变控制参数来使机器人形成相应的期望队形,在较短的时间内,跟随机器人对参考轨迹的跟踪误差趋向于0且速度收敛,验证了编队系统的可行性。

基于多策略改进的SMC-GTO电机速度跟踪控制

针对传统控制算法在农业机器人驱动电机速度控制方面存在的响应时间长、跟踪效果差以及参数整定难度大,导致驱动电机转速难以在短时间内收敛至预期值的问题,提出一种基于多策略改进的SMC-GTO电机速度跟踪控制算法。首先,为了缩短驱动电机速度响应时间,提高速度跟踪效果,设计了一种新型趋近率的改进滑模控制器(I-SMC);其次,为了快速整定滑模控制器参数,引入了多策略改进的大猩猩部队优化算法(MIGTO)。仿真结果表明:I-SMC能够将电机运行过程中的动态误差累计与过冲控制在0 rad/s内,并且可以在0.4 s内响应至预定速度。MIGTO算法在开发和探索阶段均展现出卓越的性能,尤其在整定基于新型趋近率的改进滑模控制器参数工作中表现突出。该研究通过引入I-SMC和MIGTO算法,成功改进了驱动电机速度控制方式,有效缩短了响应时间,提升了速度跟踪效果。

基于人工免疫-改进粒子群优化算法的机械臂轨迹规划研究

焊接机器人运动轨迹复杂、控制精度要求高。提出了一种满足多目标约束的轨迹规划方法。针对机器人轨迹平滑性要求,以5次非均匀有理B样条(Non-Uniform Rational B-Splines,NURBS)曲线为基础,对笛卡儿空间路径点进行参数化表达;根据工业机器人路径约束及工况需求,选取时间、能耗、跃度3个运动学指标作为目标优化函数,采用人工免疫双态粒子群进行轨迹优化;为了平衡粒子“探索”与“利用”,增加双模态模型,引入人工免疫系统,提升了粒子多样性与后期收敛能力;根据Pareto解集得到满足约束的焊接机器人各关节最优轨迹,通过Matlab仿真证明了方法的有效性;最后,针对空间相贯曲线焊缝进行了焊接试验。结果显示,规划的轨迹符合实际工程需求。

基于多目标均衡优化的2PRU&1PRS-XY型混联机器人尺度设计方法

面向复杂结构件机器人加工装备设计需求,提出一种2PRU&1PRS-XY型混联机器人。为将该混联机器人应用于高性能机械加工任务,在其工作空间、运动/力传递性以及灵巧度分析的基础上,定义了用于工作全域性能评价的传递稳定性指标与精度差异性指标,提出了一种基于多目标均衡优化思想的尺度设计方法。该方法采用标准化方法简化多参数设计空间,应用响应面法与主成分分析法提高优化求解效率,结合主客观组合赋权法与基于TOPSIS的Pareto前沿法实现多指标综合性能评分。运用所提方法,得到了4种兼顾优化求解效率和多目标均衡优化的尺度参数设计方案,实现了2PRU&1PRS-XY型混联机器人主要尺度参数的优化设计,为设计者提供了具有不同工程设计倾向的多目标均衡决策参考。

A Survey of Multi-robot Regular and Adversarial Patrolling

Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling.Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection probability.Under each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and approaches.Existing problems and open questions are presented accordingly.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution（DE） algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

Motion Error Compensation of Multi-legged Walking Robots

Existing errors in the structure and kinematic parameters of multi-legged walking robots,the motion trajectory of robot will diverge from the ideal sports requirements in movement.Since the existing error compensation is usually used for control compensation of manipulator arm,the error compensation of multi-legged robots has seldom been explored.In order to reduce the kinematic error of robots,a motion error compensation method based on the feedforward for multi-legged mobile robots is proposed to improve motion precision of a mobile robot.The locus error of a robot body is measured,when robot moves along a given track.Error of driven joint variables is obtained by error calculation model in terms of the locus error of robot body.Error value is used to compensate driven joint variables and modify control model of robot,which can drive the robots following control model modified.The model of the relation between robot＇s locus errors and kinematic variables errors is set up to achieve the kinematic error compensation.On the basis of the inverse kinematics of a multi-legged walking robot,the relation between error of the motion trajectory and driven joint variables of robots is discussed.Moreover,the equation set is obtained,which expresses relation among error of driven joint variables,structure parameters and error of robot＇s locus.Take MiniQuad as an example,when the robot MiniQuad moves following beeline tread,motion error compensation is studied.The actual locus errors of the robot body are measured before and after compensation in the test.According to the test,variations of the actual coordinate value of the robot centroid in x-direction and z-direction are reduced more than one time.The kinematic errors of robot body are reduced effectively by the use of the motion error compensation method based on the feedforward.

Intelligent learning technique based-on fuzzy logic for multi-robot path planning

Soccer robot system is a tremendously challenging intelligent system developed to mimic human soccer competition based on the multi discipline research: robotics, intelligent control, computer vision, etc. robot path planning strategy is a very important subject concerning to the performance and intelligence degree of the multi robot system. Therefore, this paper studies the path planning strategy of soccer system by using fuzzy logic. After setting up two fuzziers and two sorts of fuzzy rules for soccer system, fuzzy logic is applied to workspace partition and path revision. The experiment results show that this technique can well enhance the performance and intelligence degree of the system.

基于支重轮-履带-地面多体耦合的履带机器人振动响应研究

构建支重轮-履带-地面多体耦合系统是研究履带机器人行驶平顺性的重要环节。以单侧五支重轮履带机器人为研究对象,在非线性悬挂系统模型以及考虑履带滤波作用的路面激励的基础上,建立了履带机器人振动理论估算模型。然后,基于履带机器人在不同等级路面及不同行驶速度工况下的仿真实验,利用均方根值定量分析了行驶速度、路面激励对机器人车体及其支重轮垂向振动的影响。最后,通过开展外路实验来验证七自由度半车的振动理论估算模型和动力学仿真模型。结果表明:随着行驶速度和路面不平度的提高,履带机器人车体的垂向振动近似呈线性增大,履带对路面激励有滤波作用。履带机器人车体质心及支重轮1,3,5的垂向振动加速度对路面激励最为敏感,车体垂向振动加速度功率谱密度幅值在频率为19 Hz左右时最大。由于支重轮5靠近驱动段,履带与主动轮啮合产生的多边形效应使其垂向振动相对于其他支重轮偏大。理论建模、仿真分析及外路实验相结合的方法为履带机器人在不同路况下的振动响应特性研究提供了新思路。

Theory and Application of Multi-robot Service-oriented Architecture

In order to solve the problem of heterogeneity in multi-robot cooperation, a new service-oriented architecture is proposed for multi-robot cooperation. Service provision and energy consumption are the basic cooperative behaviors. A set of basic concepts of robot service are proposed. A layered multi-robot service-oriented architecture is designed. Finally, the experiments illustrate the superiority of the proposed architecture which makes robot's underlying functional components be transparently encapsulated and the services in upper layer be transparently invoked, which will effectively avoid the impact of heterogeneous characteristics in multi-robot cooperation and facilitate the system construction, expansion, restructuring and maintenance. © 2014 Chinese Association of Automation.

Dynamical analysis and performance evaluation of a biped robot under multi-source random disturbances

During bipedal walking,it is critical to detect and adjust the robot postures by feedback control to maintain its normal state amidst multi-source random disturbances arising from some unavoidable uncertain factors.The radical basis function（RBF）neural network model of a five-link biped robot is established,and two certain disturbances and a randomly uncertain disturbance are then mixed with the optimal torques in the network model to study the performance of the biped robot by several evaluation indices and a specific Poincar′e map.In contrast with the simulations,the response varies as desired under optimal inputting while the output is fluctuating in the situation of disturbance driving.Simulation results from noise inputting also show that the dynamics of the robot is less sensitive to the disturbance of knee joint input of the swing leg than those of the other three joints,the response errors of the biped will be increasing with higher disturbance levels,and especially there are larger output fluctuations in the knee and hip joints of the swing leg.

Genetic Algorithm Based Combinatorial Auction Method for Multi-Robot Task Allocation

An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.

基于时间-冲击最优的机械臂喷涂轨迹优化

针对机械臂喷涂作业时因冲击过大造成关节振动影响喷涂质量问题,提出了一种机械臂的最优喷涂轨迹规划方法。采用5次非均匀B样条函数构建机械臂运动轨迹,以串联机械臂运行时间和脉动冲击为目标函数建立数学模型,通过改进多目标灰狼算法进行优化设计并获得Pareto最优解集并运用归一化权重目标函数获取期望解。选择KUKA KR_16型串联机械臂作为仿真对象,针对曲面工件进行喷涂仿真实验。结果表明,5次B样条插值曲线可以有效构建高阶平滑的喷涂轨迹,改进的多目标灰狼算法能够实现满足约束条件的多目标优化,且优化效果优于NSGA-Ⅱ算法,为后续喷涂轨迹研究提供了新思路。

基于NSGA-Ⅱ的串联机器人几何参数公差的多目标优化分配

为了提高机器人末端执行器的几何定位精度,在机器人精度的初始设计阶段合理分配几何参数公差,提出了一种带精英策略的快速非支配排序遗传算法(NSGA-Ⅱ)的以成本与精度为目标的多目标公差优化分配方法.以ROKAE XB7型6自由度串联机器人为研究对象,分别基于遗传算法(GA)的最小成本单目标公差优化分配方法和NSGA-Ⅱ的多目标公差优化分配方法对DH(Denavit-Hartenberg)参数的公差优化分配.在精度设计目标和遗传算法参数设置相同的情况下,与基于遗传算法的最小成本的几何参数公差优化分配相比,基于NSGA-Ⅱ的多目标公差优化分配能够给出不同制造成本和不同精度设计要求的一系列最优解,在得到同等制造成本和机器人精度的情况下,公差的容错松弛率相对较高,参数公差优化分配的结果更优.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

Free gait generation method for omnidirectional locomotion on abrupt terrain with multi-legged biomimetic robot

In order to achieve omnidirectional locomotion on rough terrain with multi-legged biomimetic robot,a free gait generation approach is proposed based on local rules.The phase coordinates of each operation leg was established according to the motion task and a universal depiction of leg-end locomotion was implemented;the mathematical relation of gait pattern and walking velocity of multi-legged robot was put forward;combined polynomial curve was adopted to generate the leg-end trajectory,which was capable of accomplishing walking missions and accommodating to landform conditions;a distributed network of local rules for gait control was constructed based on a set of local rules operating between adjacent legs.In the simulation experiments,adaptive regulation of inter-leg phase sequence,omnidirectional locomotion and ground accommodation were realized.Moreover,statically stable free gait was obtained simultaneously,which provided multi-legged robot with the capability of walking on irregular terrain reliably and expeditiously.

Adaptive genetic algorithm for path planning of loosely coordinated multi-robot manipulators

Adaptive genetic algorithm A SA GA, a novel algorithm, which can dynamically modify the parameters of Genetic Algorithms in terms of simulated annealing mechanism, is proposed for path planning of loosely coordinated multi robot manipulators. Over the task space of a multi robot, a strategy of decoupled planning is also applied to the evolutionary process, which enables a multi robot to avoid falling into deadlock and calculating of composite C space. Finally, two representative tests are given to validate A SA GA and the strategy of decoupled planning.