Dynamically-Stable Motion Planning for Humanoid Robots

We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.     

Clock-turning gait synthesis for humanoid robots

Turning gait is a basic motion for humanoid robots. This paper presents a method for humanoid tuming, i.e. clock-turning. The objective of clock-turning is to change robot direction at a stationary spot. The clock-turning planning consists of four steps： ankle trajectory generation, hip trajectory generation, knee trajectory generation, and inverse kinematics calculation. Our proposed method is based on a typical humanoid structure with 12 DOFs （degrees of freedom）. The final output of clock-turning planning is 12 reference trajectories, which are used to control a humanoid robot with 12 DOFs. ZMP （zero moment point） is used as stability criterion for the planning. Simulation experiments are conducted to verify the effectiveness of our proposed clock-turuing method.     

拟人机器人TH-1手臂运动学

拟人机器人手臂的主要特点是它的运动功能，能够实现握手、行走时掌握平衡等动作．本文 主要针对自行设计的具有转摆结构的拟人机器人TH-1手臂机构进行了运动学分析，为其控 制提供数学基础．提出了坐标变换、三角变换等方法，巧妙求解出拟人机器人TH-1手臂逆 向运动学的解析表达式．建立了仿真软件平台，验证了运动学正逆向方程的有效性．     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

Collision-free and smooth joint motion planning for six-axis industrial robots by redundancy optimization

Planning collision-free and smooth joint motion is crucial in robotic applications, such as welding, milling, and laser cutting. Kinematic redundancy exists when a six-axis industrial robot performs five-dimensional tasks, and there are infinite joint configurations for a six-axis industrial robot to realize a cutter location data of the tool path. The robot joint motion can be optimized by taking advantage of the kinematic redundancy, and the collision-free joint motion with minimum joint movement is determined as the optimal. However, most existing redundancy optimization methods do not fully exploit the redundancy of the six-axis industrial robots when they conduct five-dimensional tasks. In this paper, we present an optimization method to solve the problem of inverse kinematics for a six-axis industrial robot to synthesize the joint motion that follows a given tool path, while achieving smoothness and collision-free manipulation. B-spline is applied for the joint configuration interpolation, and the sum of the squares of the first, second, and third derivatives of the B-spline curves are adopted as the smoothness indicators. Besides, the oriented bounding boxes are adopted to simplify the shape of the robot joints, robot links, spindle unit, and fixtures to facilitate collision detections. Dijkstra's shortest path technique and Differential Evolution algorithm are combined to find the optimal joint motion efficiently and avoid getting into a local optimal solution. The proposed algorithm is validated by simulations on two six-axis industrial robots conducting five-axis flank milling tasks respectively.     

Pivoting based manipulation by a humanoid robot

In this paper we address whole-body manipulation of bulky objects by a humanoid robot. We adopt a “pivoting” manipulation method that allows the humanoid to displace an object without lifting, but by the support of the ground contact. First, the small-time controllability of pivoting is demonstrated. On its basis, an algorithm for collision-free pivoting motion planning is established taking into account the naturalness of motion as nonholonomic constraints. Finally, we present a whole-body motion generation method by a humanoid robot, which is verified by experiments.     

Incremental kinesthetic teaching of motion primitives using the motion refinement tube

We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR??s humanoid upper-body robot Justin.     

Humanoid robot arm performance optimization using multi objective evolutionary algorithm

As humanoid robots are expected to operate in human environments they are expected to perform a wide range of tasks. Therefore, the robot arm motion must be generated based on the specific task. In this paper we propose an optimal arm motion generation satisfying multiple criteria. In our method, we evolved neural controllers that generate the humanoid robot arm motion satisfying three different criteria; minimum time, minimum distance and minimum acceleration. The robot hand is required to move from the initial to the final goal position. In order to compare the performance, single objective GA is also considered as an optimization tool. Selected neural controllers from the Pareto solution are implemented and their performance is evaluated. Experimental investigation shows that the evolved neural controllers performed well in the real hardware of the mobile humanoid robot platform.     

仿人机器人复杂动作设计中人体运动数据提取及分析方法

提出了仿人机器人复杂动作设计中人体运动数据提取及分析方法. 首先, 通过运动捕捉系统获取人体运动数据, 并采用运动重定向技术, 输出人--机简化模型的数据; 然后, 对运动数据进行分析和运动学解算, 给出基于人体运动数据的仿人机器人逆运动学求解方法, 得到仿人机器人模型的关节角数据; 再经过运动学约束和稳定性调节后, 生成能够应用于仿人机器人的运动轨迹. 最终, 通过在仿人机器人BHR-2上进行刀术实验验证了该方法的有效性.     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

A biomimetic approach to inverse kinematics for a redundant robot arm

Redundant robots have received increased attention during the last decades, since they provide solutions to problems investigated for years in the robotic community, e.g. task-space tracking, obstacle avoidance etc. However, robot redundancy may arise problems of kinematic control, since robot joint motion is not uniquely determined. In this paper, a biomimetic approach is proposed for solving the problem of redundancy resolution. First, the kinematics of the human upper limb while performing random arm motion are investigated and modeled. The dependencies among the human joint angles are described using a Bayesian network. Then, an objective function, built using this model, is used in a closed-loop inverse kinematic algorithm for a redundant robot arm. Using this algorithm, the robot arm end-effector can be positioned in the three dimensional (3D) space using human-like joint configurations. Through real experiments using an anthropomorphic robot arm, it is proved that the proposed algorithm is computationally fast, while it results to human-like configurations compared to previously proposed inverse kinematics algorithms. The latter makes the proposed algorithm a strong candidate for applications where anthropomorphism is required, e.g. in humanoids or generally in cases where robotic arms interact with humans.     

在线考虑运动学限制的最小加速度的轨迹规划

提出了一种基于简化运动规划的机器人轨迹规划新方法,可用于多自由度的机器人操作臂系统。关键问题是找到最小加速度的轨迹规划,来优化操作臂的运动以减少抖动。此外,给出了轨迹规划的解存在的充分必要条件,并考虑了所有的关节位置、角速度、加速度、加加速度等运动学限制。而且这种方法能够在线应用,适合任意非零的关节初始状态和目标状态,以便使机器人能够在运动过程中进行实时路径修正。最后提出的方法应用于一个七自由度的仿人机器人手臂来验证方法的有效性。     

A New Geometric Subproblem to Extend Solvability of Inverse Kinematics Based on Screw Theory for 6R Robot Manipulators

Geometric inverse kinematics procedures that divide the whole problem into several subproblems with known solutions, and make use of screw motion operators have been developed in the past for 6R robot manipulators. These geometric procedures are widely used because the solutions of the subproblems are geometrically meaningful and numerically stable. Nonetheless, the existing subproblems limit the types of 6R robot structural configurations for which the inverse kinematics can be solved. This work presents the solution of a novel geometric subproblem that solves the joint angles of a general anthropomorphic arm. Using this new subproblem, an inverse kinematics procedure is derived which is applicable to a wider range of 6R robot manipulators. The inverse kinematics of a closed curve were carried out, in both simulations and experiments, to validate computational cost and realizability of the proposed approach. Multiple 6R robot manipulators with different structural configurations were used to validate the generality of the method. The results are compared with those of other methods in the screw theory framework. The obtained results show that our approach is the most general and the most efficient.

     

Stepping over obstacles with humanoid robots

The wide potential applications of humanoid robots require that the robots can walk in complex environments and overcome various obstacles. To this end, we address the problem of humanoid robots stepping over obstacles in this paper. We focus on two aspects, which are feasibility analysis and motion planning. The former determines whether a robot can step over a given obstacle, and the latter discusses how to step over, if feasible, by planning appropriate motions for the robot. We systematically examine both of these aspects. In the feasibility analysis, using an optimization technique, we cast the problem into global optimization models with nonlinear constraints, including collision-free and balance constraints. The solutions to the optimization models yield answers to the possibility of stepping over obstacles under some assumptions. The presented approach for feasibility provides not only a priori knowledge and a database to implement stepping over obstacles, but also a tool to evaluate and compare the mobility of humanoid robots. In motion planning, we present an algorithm to generate suitable trajectories of the feet and the waist of the robot using heuristic methodology, based on the results of the feasibility analysis. We decompose the body motion of the robot into two parts, corresponding to the lower body and upper body of the robot, to meet the collision-free and balance constraints. This novel planning method is adaptive to obstacle sizes, and is, hence, oriented to autonomous stepping over by humanoid robots guided by vision or other range finders. Its effectiveness is verified by simulations and experiments on our humanoid platform HRP-2.     

Position control of two-arm manipulators for coordinated point-to-point motion

A trajectory planning and motion control algorithm is; presented for the point-to-point (PTP) motion of two-arm manipulators cooperating on a task. The proposed method considers the multi-arm manipulator as a system when formulating its kinematic model and obtains a global solution to the system, as opposed to individual arm solutions. For PTP motion control between two arm configurations, a simple trajectory is first assumed by defining joint velocity profiles and maximum allowable task space errors between the two end effectors of the manipulator. The task space errors during the motion are then continuously monitored to take corrective action when necessary to prevent those errors from exceeding the given tolerance limits. The main objective of this method is to reduce the number of inverse kinematics solutions during the real-time control of the two-arm system. The algorithm is illustrated by a numerical example for an eight degree-of-freedom kinematically redundant planar two-arm system.     

基于MATLAB的模块化机器人手臂运动学算法验证及运动仿真

针对由模块化关节构成的六自由度串联机器人手臂, 采用DH法对手臂的操作空间进行了描述, 得到了正运动学模型; 采用欧拉角表示手臂姿态, 得到了包含六个参数的用于表示手臂位姿的完备广义坐标, 并对欧拉角的几何关系进行了分析。针对SolidWorks虽然实体建模简洁方便但计算并非其强项的缺点, 编写相应接口程序, 将建立的手臂三维实体模型保留几何约束关系简化后导入MATLAB软件。基于MATLAB编写正逆运动学算法验证程序以及连杆驱动程序, 实现了手臂的仿真运动。通过仿真, 不仅更进一步验证了手臂正逆运动学解算的正确性, 而且非常直观地看出手臂末端在空间中运行的路径以及各关节的动作情况。机器人手臂正逆运动学算法正确性的验证及运动仿真为手臂的精确定位及其路径规划提供了必要的保证。     

基于Octomap的仿人机器人局部环境与能力图模型算法

基于3D点云数据的机器人三维空间能力图模型算法存在体素网格搜索计算量大的问题，由于OcTree在三维空间细分时的层次化优势，提出一种基于Octomap的局部环境与能力图模型算法。首先，根据NAO机器人的关节组成、正向运动学、逆向运动学和刚体坐标变换，对NAO仿人机器人构建全身二叉树状运动学模型；其次在此基础上使用前向运动学在笛卡儿空间计算离散的三维可达点云，并将其作为机器人终端效应器的基础工作空间；然后重点描述将点云空间表示转化为Octomap空间节点表示的方法，尤其是空间节点的概率更新方法；最后提出根据节点几何关系进行空间节点更新顺序选择的优化方法，从而高效地实现了仿人机器人能力图的空间优化表示。实验结果表明，相对于之前的原始Octomap更新方法，优化后的算法能降低近30%空间节点数，提高计算效率。     

Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints

It is a common belief that service robots shall move in a human-like manner to enable natural and convenient interaction with a human user or collaborator. In particular, this applies to anthropomorphic 7-DOF redundant robot manipulators that have a shoulder-elbow-wrist configuration. On the kinematic level, human-like movement then can be realized by means of selecting a redundancy resolution for the inverse kinematics (IK), which realizes human-like movement through respective nullspace preferences. In this paper, key positions are introduced and defined as Cartesian positions of the manipulator’s elbow and wrist joints. The key positions are used as constraints on the inverse kinematics in addition to orientation constraints at the end-effector, such that the inverse kinematics can be calculated through an efficient analytical scheme and realizes human-like configurations. To obtain suitable key positions, a correspondence method named wrist-elbow-in-line is derived to map key positions of human demonstrations to the real robot for obtaining a valid analytical inverse kinematics solution. A human demonstration tracking experiment is conducted to evaluate the end-effector accuracy and human-likeness of the generated motion for a 7-DOF Kuka-LWR arm. The results are compared to a similar correspondance method that emphasizes only the wrist postion and show that the subtle differences between the two different correspondence methods may lead to significant performance differences. Furthermore, the wrist-elbow-in-line method is validated as more stable in practical application and extended for obstacle avoidance.     

Backward Ladder Climbing Locomotion of Humanoid Robot with Gain Overriding Method on Position Control

We studied ladder climbing locomotion with the humanoid robot, DRC‐HUBO, under the constraints suggested by DARPA. Considering the hardware constraints of the robot platform, we planned for the robot to climb backward with four limbs moving separately. Task‐priority whole‐body inverse kinematics was used to generate and track the motion while maintaining COM inside the support polygon. As ladder climbing is a multicontact motion that generates interaction and internal forces, we resolved these issues using a gain overriding method applied to the position control of the motor controllers. This paper also provides various vision methods and posture modification strategies for the restricted conditions of the challenge. We ultimately verified our work in the DRC trials by getting a full score on the ladder task.     

表征空间中的机器人分层运动规划

由于对机器人的任务要求日趋复杂和多变,如何使机器人具备灵活的配置和运动规划能力,以适应复杂任务的需求,成为了目前运动规划领域所研究的核心问题.传统的基于任务空间和配置空间的建模方法虽然在机器人运动规划领域得到了非常广泛的应用,但在用于解决复杂规划任务时无法对不可行任务进行进一步地处理.本文在表征空间模型的基础上,提出了一种分层的运动规划算法,一方面借助于表征空间维度的扩展,使对运动规划任务的描述更为灵活;另一方面通过任务层与运动层的循环交互,使生成的路径满足更高层次和更丰富的任务要求.在仿人机器人和多机器人系统上的应用结果表明了本文所提算法的有效性.