Balance recovery control for biped robot based on reaction null space method

A biped walking robot should be able to keep balance even in the presence of disturbing forces. This paper presents a step strategy concept of biped walking robot that is stabilized by using reaction null space method. The called ＂step strategy＂ can be modeled by means of the reaction null space method that introduced earlier to tackle dynamic interaction problems of free-floating robots, or moving base robots in general. 6-DOF biped robot model simulations are used to confirm the validity.     

Balance control of a biped robot using camera image of reference object

This paper presents a new balance control scheme for a biped robot. Instead of using dynamic sensors to measure the pose of a biped robot, this paper uses only the visual information of a specific reference object in the workspace. The zero moment point (ZMP) of the biped robot can be calculated from the robot’s pose, which is measured from the reference object image acquired by a CCD camera on the robot’s head. For balance control of the biped robot a servo controller uses an error between the reference ZMP and the current ZMP, estimated by Kalman filter. The efficiency of the proposed algorithm has been proven by the experiments performed on both flat and uneven floors with unknown thin obstacles. Recommended by Editorial Board member Dong Hwan Kim under the direction of Editor Jae-Bok Song. This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD). This research was supported by the MKE(The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA(Institute for Information Technology Advancement) (IITA-2008-C1090-0803-0006). Sangbum Park received the B.S. and M.S. degrees from Electronic Engineering of Soongsil University, Seoul, Korea, in 2004 and 2006 respectively. He has been with School of Electronic Engineering, Soongsil University since 2006, where he is currently pursuing a Ph.D. His current research interests include biped walking robot, robotics vision. Youngjoon Han received the B.S., M.S. and Ph.D. degrees in Electronic Engineering from Soongsil University, Seoul, Korea, in 1996, 1998, and 2003, respectively. He is currently an Assistant Professor in the School of Electornic Engineering at Soongsil University. His research interests include robot vision system, and visual servo control. Hernsoo Hahn received the B.S. and M.S. degrees in Electronic Engineering at Soongsil University and Younsei University, Korea in 1982 and 1983 respectively. He received the Ph.D. degree in Computer Engineering from University of Southern California in 1991, and became an Assistant Professor at the School Electroncis Engneering in Soongsil University in 1992. Currently, he is a Professor. His research interests include application of vision sensors to mobile robots and measurement systems.     

Principle and method of speed control for dynamic walking biped robots

In this paper, the method of speed control for 3D biped robots is addressed. First, the primary principle of speed control by regulation of input energy is studied, the feature of which is to regulate the speed and the step length synchronically. The method of Poincaré mapping is used to prove the stability of speed control in the common range. Second, a method of speed control for an 18 DOFs bipedal 3D robot, which is characterized by the two-point-foot, is proposed. The method is developed on the basis of the 3D walking pattern proposed previously, with the new function of speed regulation being added in. The simulations show that the performances of regular walking, acceleration, and deceleration are effective and stable, and therefore verify the feasibility of the proposed method. Furthermore, some walking features, such as the walking efficiency and lateral control, are demonstrated.     

Stability and control of dynamic walking for a five-link planar biped robot with feet

During dynamic walking of biped robots, the underactuated rotating degree of freedom (DOF) emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant.This paper addresses the asymptotic orbit stability for criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with flat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.     

Sensory-based walking motion instruction for biped humanoid robot

This paper describes a sensory-based biped walking motion instruction strategy. Visual and auditory sensors are employed to generate walking patterns according to human orders and to memorize various complete walking patterns effectively and systematically. The motion of lower-limbs for locomotion is created by an online pattern generator based on the sensory information. At the same time, the motion of the trunk and the waist for stability is generated online by a balance control method. Combining these locomotive and balance motions, a complete walking pattern is hierarchically constructed and memorized on a database. The walking instruction is conducted through computer simulation, and its effectiveness is verified.     

Stability and control of dynamic walking for a five-link planar biped robot with feet

During dynamic walking of biped robots, the underactuated rotating degree of freedom （DOF） emerges between the support foot and the ground, which makes the biped model hybrid and dimension-variant. This paper addresses the asymptotic orbit stability for dimension-variant hybrid systems （DVHS）. Based on the generalized Poincare map, the stability criterion for DVHS is also presented, and the result is then used to study dynamic walking for a five-link planar biped robot with feet. Time-invariant gait planning and nonlinear control strategy for dynamic walking with fiat feet is also introduced. Simulation results indicate that an asymptotically stable limit cycle of dynamic walking is achieved by the proposed method.     

基于deep Q-network双足机器人非平整地面行走稳定性控制方法

针对双足机器人在非平整地面行走时容易失去运动稳定性的问题,提出一种基于一种基于价值的深度强化学习算法DQN（Deep Q-Network）的步态控制方法。首先通过机器人步态规划得到针对平整地面环境的离线步态,然后将双足机器人视为一个智能体,建立机器人环境空间、状态空间、动作空间及奖惩机制,该过程与传统控制方法相比无需复杂的动力学建模过程,最后经过多回合训练使双足机器人学会在不平整地面进行姿态调整,保证行走稳定性。在V-Rep仿真环境中进行了算法验证,双足机器人在非平整地面行走过程中,通过DQN步态调整学习算法,姿态角度波动范围在3°以内,结果表明双足机器人行走稳定性得到明显改善,实现了机器人的姿态调整行为学习,证明了该方法的有效性。     

基于Fuzzy–CPG的双足机器人适应性行走控制

为提高双足机器人的环境适应性,本文提出了一种基于模糊控制与中枢模式发生器(CPG)的混合控制策略,称之为Fuzzy–CPG算法.高层控制中枢串联模糊控制系统,将环境反馈信息映射为行走步态信息和CPG幅值参数.低层控制中枢CPG根据高层输出命令产生节律性信号,作为机器人的关节控制信号.通过机器人运动,获取环境信息并反馈给高层控制中枢,产生下一步的运动命令.在坡度和凹凸程度可变的仿真环境中进行混合控制策略的实验验证,结果表明,本文提出的Fuzzy–CPG控制方法可以使机器人根据环境的变化产生适应的行走步态,提高了双足机器人的环境适应性行走能力.     

基于神经网络的自由漂浮空间机器人关节空间鲁棒跟踪控制

针对存在不确定性以及干扰的自由漂浮空间机器人关节空间轨迹跟踪问题,提出了一种基于鲁棒控制思想的神经网络鲁棒控制方法.对于控制器中由系统惯性参数不确定性引起的非线性不确定项,利用径向基函数(RBF)神经网络进行逼近,并且利用鲁棒控制器使系统镇定并保证从干扰到跟踪误差的增益小于或等于给定的指标.最后,对本文提出的控制方案进...     

Central pattern generator parameter search for a biped walking robot using nonparametric estimation based Particle Swarm Optimization

A parameter search for a Central Pattern Generator (CPG) for biped walking is difficult because there is no methodology to set the parameters and the search space is broad. These characteristics of the parameter search result in numerous fitness evaluations. In this paper, nonparametric estimation based Particle Swarm Optimization (NEPSO) is suggested to effectively search the parameters of CPG. The NEPSO uses a concept experience repository to store a previous position and the fitness of particles in a PSO and estimated best position to accelerate a convergence speed. The proposed method is compared with PSO variants in numerical experiments and is tested in a three dimensional dynamic simulator for bipedal walking. The NEPSO effectively finds CPG parameters that produce a gait of a biped robot. Moreover, NEPSO has a fast convergence property which reduces the evaluation of fitness in a real environment. Recommended by Editorial Board member Euntai Kim under the direction of Editor Jae-Bok Song. Jeong-Jung Kim received the B.S. degree in Electronics and Information Engineering from Chonbuk National University in 2006 and the M.S. degree in Robotics from Korea Advanced Institute of Science and Technology in 2008. He is currently working toward a Ph.D. at the Korea Advanced Institute of Science and Technology. His research interests include biologically inspired robotics and machine learning. Jun-Woo Lee received the B.S. degree in Electronics, Electrical and Communication Engineering from Pusan National University in 2007. He is currently working toward an M.S. in the Korea Advanced Institute of Science and Technology. His research interests include swarm intelligence and machine learning. Ju-Jang Lee was born in Seoul, Korea, in 1948. He received the B.S. and M.S. degrees from Seoul National University, Seoul, Korea, in 1973 and 1977, respectively, and the Ph.D. degree in Electrical Engineering from the University of Wisconsin, in 1984. From 1977 to 1978, he was a Research Engineer at the Korean Electric Research and Testing Institute, Seoul. From 1978 to 1979, he was a Design and Processing Engineer at G. T. E. Automatic Electric Company, Waukesha, WI. For a brief period in 1983, he was the Project Engineer for the Research and Development Department of the Wisconsin Electric Power Company, Milwaukee. He joined the Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, in 1984, where he is currently a Professor. In 1987, he was a Visiting Professor at the Robotics Laboratory of the Imperial College Science and Technology, London, U.K. From 1991 to 1992, he was a Visiting Scientist at the Robotics Department of Carnegie Mellon University, Pittsburgh, PA. His research interests are in the areas of intelligent control of mobile robots, service robotics for the disabled, space robotics, evolutionary computation, variable structure control, chaotic control systems, electronic control units for automobiles, and power system stabilizers. Dr. Lee is a member of the IEEE Robotics and Automation Society, the IEEE Evolutionary Computation Society, the IEEE Industrial Electronics Society, IEEK, KITE, and KISS. He is also a former President of ICROS in Korea and a Counselor of SICE in Japan. He is a Fellow of SICE and ICROS. He is an Associate Editor of IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics.     

Robust sliding-mode control applied to a 5-link biped robot

In this paper the application of robust control to a 5-link biped robotic model is investigated through the sliding mode approach, and compared to pure computed torque control. The biped consists of five links, namely the torso and two links in each leg. These links are connected via four (two hip and two knee) rotating joints which are considered to be friction-free and driven by independent d.c. motors. The locomotion of the biped is assumed to be constrained on the sagittal plane. The paper provides a full derivation of the biped dynamic model (single-leg support phase, biped-in-the-air phase) and an outline of the computed torque and sliding mode control algorithms. The simulation results were derived with two sets of parameters (one of which corresponds to a human-sized biped) and several degrees of parametric uncertainty (from 10% to 200%). In all cases the results obtained through the sliding mode control were much better than those obtained with the computed torque control. This superiority was shown to become stronger as the degree of uncertainty and the size of the biped increases.     

基于脉冲推力的半被动双足机器人无模型神经网络控制

研究了半被动双足机器人的平面稳定行走控制问题。以最简行走模型为动力学模型,采用沿支撑腿方向的脚后跟脉冲推力作为行走动力源。考虑到系统模型的非线性特征,将基于三角函数扩展的函数链接型人工神经网络控制算法引入到机器人系统中,以产生系统所需的脉冲推力。并采用基于数据驱动的无模型同步扰动随机逼近算法对神经网络的权值进行更新。利用庞加莱映射方法分析了半被动双足机器人行走的稳定条件。在理论分析的基础上,对该算法进行了仿真研究。仿真结果表明：文中所提算法在收敛快速性上要优于迭代学习控制算法,可以实现双足机器人平面上的稳定周期行走。且雅可比矩阵的特征值均位于单位圆内,满足系统的稳定条件。     

Experiments with nontraditional hybrid control technique of biped locomotion robots

By using the previously established zero-moment point theory and the semi-inverse approach [1–4] for solving the artificial gait synthesis based on the prescribed dynamics to part of the active mechanism, in this new approach to dynamic control of legged locomotion robots, the conventional control synthesis, based on complete dynamic robot model, is abandoned. The paper describes the simulation experiments of biped control with a hybrid approach that combines the traditional model-based and fuzzy logic-based control techniques. The combined method is developed by extending a model-based decentralized control scheme by fuzzy logic-based tuners for modifying parameters of joint servo controllers. The simulation experiments performed on a simplified two-legged mechanism demonstrate the suitability of fuzzy logic-based methods for improving the performance of the robot control system.     

An imaginary robot model and double-PD control for redundant robotic systems

The theory and applications of an imaginary robot model with a double-PD control law for redundant robotic systems are presented. The imaginary robot model is based on a special Riemannian metric decomposition for general nonlinear dynamic systems. This model offers an effective way for reducing nonlinear feedback formulation while preserving the linearized system equation. The developed procedure is also applicable to redundant robots. A three-dimensional redundant robot main-frame having three revolute joints plus a prismatic joint is used in the paper to illustrate the design procedure based on the imaginary robot model with the double-PD control scheme. The entire dynamic control algorithm is also verified by a simulation study on the four-joint three-dimensional robot arm.     

空间机器人控制系统硬件仿真平台的研究

建立了空间机器人控制系统的硬件仿真平台。研究了空间机器人基于手眼视觉的控制问题,建立了系统关键部件的模拟设备。仿真平台由中央控制器、关节模拟器、手眼模拟器、动力学/运动学仿真计算机和三维动画显示计算机组成。基于该平台,对空间机器人控制特性和仿真过程中的延时环节进行了研究。系统自主捕获仿真试验结果表明,所采用的运动控制算法能够稳定收敛于目标,仿真平台能够较好地完成对实际机器人系统控制过程的模拟测试及系统控制算法的验证。     

Neuro-fuzzy gait synthesis with reinforcement learning for a biped walking robot

 A reinforcement learning-based neuro-fuzzy gait synthesizer, which is based on the GARIC (Generalized Approximate Reasoning for Intelligent Control) architecture, is proposed for the problem of biped dynamic balance. We modify the GARIC architecture to enable it to generate the trunk trajectory in both sagittal and frontal plane. The proposed gait synthesizer is trained by reinforcement learning that uses a multi-valued scalar signal to evaluate the degrees of failure or success for the biped locomotion by means of the ZMP (Zero Moment Point). It can form the initial dynamic balancing gait from linguistic rules, which are obtained from human intuitive balancing knowledge and biomechanics studies, and accumulate dynamic balancing knowledge through reinforcement learning, and thus constantly improve its gait during walking. The feasibility of the proposed method is verified through a 5-link biped robot simulation.     

自由漂浮空间机器人轨迹跟踪的模型预测控制

针对传统控制方法难以解决自由漂浮空间机器人(free-floating space robot, FFSR)轨迹跟踪过程中的各类约束的问题,采用模型预测控制对自由漂浮空间机器人的轨迹跟踪问题进行了研究.在自由漂浮空间机器人拉格朗日动力学模型的基础上,建立了系统伪线性化的扩展状态空间模型;在给定系统的性能指标和各类约束的情况下,基于拉盖尔模型设计相应的离散模型预测控制器,并证明控制器的稳定性,控制器中引入任务空间滑模变量实现了对末端期望位置和期望速度的同时跟踪;以平面二杆自由漂浮空间机器人为例,对无约束末端轨迹跟踪和有约束末端轨迹跟踪两种情况进行对比仿真验证.仿真结果表明,该模型预测控制器不仅可以实现对末端期望轨迹的有效跟踪,还能满足各类约束.     

机器人相机空间智能编程方法研究*

针对传统的示教编程方式存在操作复杂,效率低,危险性高等不足,严重限制了工业机器人的推广应用。基于自然的人机交互示教方式,提出了一种基于计算机视觉的相机空间工业机器人智能虚拟编程方法,本方法不需要实际操作示教盒和机器人本体,仅采用辅助示教工具在视觉相机空间示教就实现了工业机器人的虚拟编程。主要研究了实现该方案的关键技术即基于相机空间映射模型的视觉定位技术以及基于K-means聚类算法实现的相机空间映射关系自学习技术。最后,基于自主开发的机器人平台,开展基于相机空间的虚拟智能编程实验,验证了本文提出的相机空间工业机器人智能编程方法的可行性及正确性。     

考虑空间系绳释放特性的空间绳系机器人协调耦合控制

针对空间绳系机器人的轨迹跟踪控制问题,提出考虑系绳释放特性的跟踪轨迹协调控制方法.该方法考虑系绳释放的动力学特性,在最优轨迹规划过程中将系绳释放速度作为一个规划量,将系绳释放机构的转矩作为一个控制输入,结合操作机器人上推力器控制输入设计协调耦合位姿控制器.该方法的优点是控制输入易于施加,可工程实现系绳协调控制.仿真结果表明,操作机器人能精确跟踪最优轨迹,末端误差为±0.1 m,且能有效跟踪期望姿态,精度在±0.2°.