Executing optimized throwing motion on robot arm with free joint

We address the throwing motion optimization for robot. In order to pursue the best throwing motion, we may need heuristics/intuition free methods. We propose a throwing method that is composed of rapid semi-optimal motion-planning and output zeroing method. So as to execute the optimized trajectories in real rigid body systems, we need some compensations for the noises around the optimized trajectories. We introduce a compensation method for the optimized throwing motions of a robot arm with a free joint. To validate the effectiveness of the proposed method, we conducted a throwing experiment using a two-link arm. As a result of the experiment, the robot arm threw a ball with 63.7 km/h, which was the best record through the past experiments of this arm.     

Selection and design of drive modules for robot joint based on dynamic load characteristics

This paper presents the selection and design process of joint motors and gears for robot systems. The velocity, acceleration, and torque profiles of each joint that satisfy a given task are calculated using dynamic and kinematic analysis, and the required motor torque to drive the joint is calculated with respect to the combination of module inertia and gear ratios. Considering energy efficiency, motor parameters, and switching characteristics of the inverter, the correlation between the module inertia and gear ratio for driving joints is investigated, and the final combination is determined by referring to commercial gear data sheets. From the determined motor inertia, various motor designs are possible. To compare the performance and determine the optimal model, performance metrics of drive motors, torque matching, coil temperature, and the motor constant, are proposed and the significance of these figures are discussed.

     

机械臂逆运动学模型控制新方法

根据机械臂关节轴线方向建立了连杆坐标系,采用Denavit-Hartenberg（D-H）法得到连杆坐标系变换矩阵,通过坐标系变换矩阵得到机械臂正运动学模型,由正运动模型得到机械臂逆运动学模型。该模型是一个非线性约束优化问题。为了求解模型,基于多种群和多变异策略,提出了多变异策略的多种群差分演化算法。多种群策略可以提升个体共享群体信息的能力,多变异模式策略可以提升个体间的差异性,数值试验表明新算法可以有效求解机械臂逆运动学模型。     

含铰链间隙的刚-柔机械臂动力学模型

根据Hertz接触定律和Coulomb摩擦定律,建立了含间隙平面旋转铰的力学模型;采用几何变形约束法和模态缩聚技术描述柔性机械臂的非线性变形;同时考虑两个旋转铰的间隙特性和柔性臂的弹性变形,最终采用Kane方程建立了含铰链间隙的刚-柔机械臂系统的动力学模型．     

Polynomial joint angle arm robot motion planning in complex geometrical obstacles

This paper addresses a point-to-point of an arm robot motion planning in complex geometrical obstacle. It will govern a two-layer optimization strategy utilizing sixth degree polynomial as joint angle path. At the beginning of the motion planning process, the path planning starts with the optimization objective to minimize the joint angle travelling distance under collision detection rules as constraint. After the best path has been met, the associated time will be searched with the optimization objective to minimize the total travelling time and the torque under the maximum velocity, the maximum acceleration, the maximum jerk, and the maximum torque constraints. The performance of a Genetic Algorithm (GA) and a Particle Swarm Optimization (PSO) will be investigated in searching the feasible sixth degree polynomial joint angle path and the total travelling time that gives the optimal trajectories under kinodynamic constraints. A 3-Degree-Of-Freedom (3-DOF) planar robot will be utilized to simulate the proposed scenario.     

Minimum-time control of a two-link robot arm

Minimum time paths for a two-link robot arm are determined so that the tip moves a specified distance, starting from rest and ending at rest. Two torquers are used, one at the shoulder and one at the elbow, each having bounded torque. The optimal locations of the two end points in the robot coordinate system are determined as part of the solution. The paths are found to be “bang-bang” in both controls. For distances that are small compared to the reach of the arm, the minimum time paths are asymmetric, changing to symmetric paths when the distance is just slightly less than the maximum reach of the arm. A new gradient algorithm for systems with multiple bounded controls was developed to solve the problem.     

Using FORTH to control a robot arm

The characteristics of the Cyber 3 robot arm are outlined, with a brief history of programming languages used to control it. The advantages of FORTH over its rivals are given. A program sequence for a robot to reorient an object is given.     

Modelling and estimating the pose of a human arm

Pose estimation of 3-D objects based on monocular computer vision is an ill-posed problem. To ease matters a model-based approach can be applied. Such an approach usually relies on iterating when matching the model and the image data. In this paper we estimate the 3-D pose of a human arm from a monocular image. To avoid the inherent problems when iterating, we apply an exhaustive matching strategy. To make this plausible, we reduce the size of the solution space through a very compact model representation of the arm and prune the solution space. The model is developed through a detailed investigation of the functionality and structure of the arm and the shoulder complex. The model consists of just two parameters and is based on the screw-axis representation together with image measurements. The pruning is achieved through kinematic constraints and it turns out that the solution space of the compact model can be pruned , on average. Altogether, the compact representation and the constraints reduce the solution space significantly and, therefore, allow for an exhaustive matching procedure. The approach is tested in a model-based silhouette framework, and tests show promising results.Published online: 8 August 2003Correspondence to: Thomas B. Moeslund     

Vibration control of a two-link flexible robot arm

Analysis and experimentation is described for a two-link apparatus in which both members are very flexible. Attention is focused on endpoint position control for point-to-point movements, assuming a fixed reference frame for the base, with two rotary joints. Each link is instrumented with acceleration sensing and is driven by a separate motor equipped with velocity and position sensing. The control perspective adopted is to implement a two-stage control strategy in which the vibration control problem for fine-motion endpoint positioning is considered separate from the gross-motion, large-angle slew problem. In the first stage the control law shapes the actuator inputsfor the large-angle movement in such a way that minimal energy is injected into the flexible modes, while in the second phase an endpoint acceleration feedback scheme is employed in independent joint controlfor vibration suppression at the link endpoints.     

Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives

When describing robot motion with dynamic movement primitives (DMPs), goal (trajectory endpoint), shape and temporal scaling parameters are used. In reinforcement learning with DMPs, usually goals and temporal scaling parameters are predefined and only the weights for shaping a DMP are learned. Many tasks, however, exist where the best goal position is not a priori known, requiring to learn it. Thus, here we specifically address the question of how to simultaneously combine goal and shape parameter learning. This is a difficult problem because learning of both parameters could easily interfere in a destructive way. We apply value function approximation techniques for goal learning and direct policy search methods for shape learning. Specifically, we use “policy improvement with path integrals” and “natural actor critic” for the policy search. We solve a learning-to-pour-liquid task in simulations as well as using a Pa10 robot arm. Results for learning from scratch, learning initialized by human demonstration, as well as for modifying the tool for the learned DMPs are presented. We observe that the combination of goal and shape learning is stable and robust within large parameter regimes. Learning converges quickly even in the presence of disturbances, which makes this combined method suitable for robotic applications.     

Dynamic analysis and determination of the joint characteristics of parallel-drive robot manipulators

This article presents a theoretical and experimental study on structural dynamic response and determination of the joint characteristics of a five degree-of-freedom industrial robot manipulator with a parallel-drive mechanism. The joints were modeled as a linear spring in parallel with a viscous damper while the link members were assumed to be rigid in this study. The dynamic equations of motion of the robot manipulator were derived using the principle of virtual work. Based on these equations, the complex structural characteristics of the manipulator were simplified by carefully arranging the manipulator in proper arm configurations to avoid coupling effects among joints. Hence, the joint stiffness and damping ratio of each joint were determined experimentally. Meanwhile, the dynamic responses of the robot manipulator were also investigated. Good correlation between computer simulations and experimental results was achieved. From the experimental study, an additional troublesome flexural mode of about 10 Hz that tends to dominate the whole dynamic response and influence the positioning accuracy of the manipulator was found due to the weakness of the structural member at the base rotation joint, which was not modeled in the dynamic equations. The results of this study will be useful in providing a basis for improving the design of mechanical components and the articulating members of industrial robot manipulators.     

Global regulation of a planar robot arm striking a surface

Considers the problem of modeling and controlling the impact of a two-degree-of-freedom planar robot arm against an infinitely rigid and massive surface. For this case study, the basic equations describing the motion of the robot arm are derived for noncontact and contact conditions. A control scheme is proposed on the basis of a reduced-order observer that is able to asymptotically estimate the impact-induced forces and to allow their asymptotic compensation when the robot arm is in contact with the surface. The resulting control system ensures the global asymptotic regulation of the position of the arm with an assigned impact-induced force. The case study is completed by a simulation test     

Adaptive control of a robot arm using driver programs

Most successful state-of-the-art robotic manipulators have the characteristic of producing precise, fast, smooth and reproducible movements. Their drawback is that they tend to have a limited repertoire which can only be extended by costly inverse kinematics calculations or direct teach-in sessions. The goal of our project is to develop a flexible open world robot, which adapts to its task and environment progressively and in an iterative manner. It begins carefully and slowly, with small jagged movements, but, after repetition, reaches an acceptable level of smoothness and execution speed. It can be placed in new surroundings with new task definitions, requiring only a further practice time before becoming expert. Similarities from other sub-tasks are recognized and may be transferred to the new domains. The basic constituents of the adaptive robot are simple iterative inverse kinematics and driver programs. In this paper the idea of driver programs for robot arm control is introduced and their properties investigated.     

Design and construction of a microcomputer-controlled light-weight robot arm

This paper describes current research on a computer-controlled light-weight mechanical arm. This mechanical arm is a self-contained, autonomous system capable of executing high-level commands from a supervisory computer. The links used in the experimental model are made of aluminium, while the actuators of the joints are permanent magnet type dc motors driven by servo-amplifiers via Pulse Width Modulation. To avoid the use of tachometers and thus reduce the link masses, a method for velocity estimation based on a position history was developed which can be implemented in digital circuitry. The design of the full-servo-loop is described, as well as the interface between the arm and an IBM-XT type microcomputer. Experimental results show that the link masses can be reduced by replacing the joint tachometers with a velocity estimator and very fast response can be achieved using light-weight structures without compromising rigidity.     

The SOF-PID controller for the control of a MIMO robot arm

The application of a self-organizing fuzzy proportional-integral-derivative (SOF-PID) controller to a multiple-input-multiple-output (MIMO) nonlinear revolute-joint robot arm is studied in this paper. The SOF controller is a learning supervisory controller, making small changes to the values of the PID gains while the system is in operation. In effect, the SOF controller replaces an experienced human operator, which otherwise would have readjusted the PID gains during the system operation. The three PID gains are tuned using classical tuning techniques prior to the application of the SOF controller at the supervisory level. Two trajectories of step input and path tracking were applied to the SOF-PID controller at the setpoint. For comparison purposes, the same experiments were repeated by using the self-organizing fuzzy controller (SOFC) and the PID controller subject to the same information supplied at the setpoint. For the step input, the SOF-PID controller produced a aster rise time, a smaller steady state error, and an insignificant overshoot than the SOFC and the PID controller. For the path tracking experiments, better results were obtained.     

Cooperative neural field for the path planning of a robot arm

This paper deals with the problem of finding a good trajectory, from an initial position to a prescribed target point, for the end effector of a robot arm moving on a two-dimensional work field and avoiding obstacles lying on the work field. Two algorithms based on cooperative neural fields are proposed: the former is suited for the case where the location of obstacles is known, the latter doesn't require any a priori knowledge and is based on a very crude collision detector.     

Application of a neural network to the generation of a robot arm trajectory

We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Coverage trajectory planning for a bush trimming robot arm

A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.