Te Tang

An exoskeleton system for hand rehabilitation driven by shape memory alloy

An exoskeleton system to assist hand rehabilitation exercise autonomously was introduced in this paper. A geared four-bar linkage mechanism was proposed as the main structure of the exoskeleton, and was verified by kinematic modeling, static analysis as well as finite element analysis. To determine the size of the exoskeleton, the optimization method was adopted, so as to improve the kinematic performance of the system. A differential rotational actuator based on shape memory alloy (SMA) was proposed, which reduced the systems weight and hence improved its portability. The 3D printer was used to produce the exoskeleton structure. Finally, through a series of experiments, feasibility and reliability of the hand exoskeleton system were demonstrated.

Teach industrial robots peg-hole-insertion by human demonstration

Programming robotic assembly tasks usually requires delicate force tuning. In contrast, human may accomplish assembly tasks with much less time and fewer trials. It will be a great benefit if robots can learn the human inherent skill of force control and apply it autonomously. Recent works on Learning from Demonstration (LfD) have shown the possibility to teach robots by human demonstration. The basic idea is to collect the force and corrective velocity that human applies during assembly, and then use them to regress a proper gain for the robot admittance controller. However, many of the LfD methods are tested on collaborative robots with compliant joints and relatively large assembly clearance. For industrial robots, the non-backdrivable mechanism and strict tolerance requirement make the assembly tasks more challenging. This paper modifies the original LfD to be suitable for industrial robots. A new demonstration tool is designed to acquire the human demonstration data. The force control gains are learned by Gaussian Mixture Regression (GMR) and the closed-loop stability is analysed. A series of peg-hole-insertion experiments with H7h7 tolerance on a FANUC manipulator validate the performance of the proposed learning method.

Robotic manipulation of deformable objects by tangent space mapping and non-rigid registration

Recent works of non-rigid registration have shown promising applications on tasks of deformable manipulation. Those approaches use thin plate spline-robust point matching (TPS-RPM) algorithm to regress a transformation function, which could generate a corresponding manipulation trajectory given a new pose/shape of the object. However, this method regards the object as a bunch of discrete and independent points. Structural information, such as shape and length, is lost during the transformation. This limitation makes the objects final shape to differ from training to test, and can sometimes cause damage to the object because of excessive stretching. To deal with these problems, this paper introduces a tangent space mapping (TSM) algorithm, which maps the deformable object in the tangent space instead of the Cartesian space to maintain structural information. The new algorithm is shown to be robust to the changes in the objects pose/shape, and the objects final shape is similar to that of training. It is also guaranteed not to overstretch the object during manipulation. A series of rope manipulation tests are performed to validate the effectiveness of the proposed algorithm.

A Learning-Based Framework for Robot Peg-Hole-Insertion

Peg-hole-insertion is a common operation in industry production, but autonomous execution by robots has been a big challenge for many years. Current robot programming for this kind of contact problem requires tremendous effort, which needs delicate trajectory and force tuning. However, human may accomplish this task with much less time and fewer trials. It will be a great benefit if robots can learn the human skill and apply it autonomously. This paper introduces a framework for teaching robot peg-hole-insertion from human demonstration. A Dimension Reduction and Recovery method is proposed to simplify control policy learning. The Gaussian Mixture Regression is utilized to imitate human skill and a Dual Stage Force Control strategy is designed for autonomous execution by robots. The effectiveness of the teaching framework is demonstrated by a series of experiments.Copyright

Human guidance programming on a 6-DoF robot with collision avoidance

In the application of physical human-robot interaction (pHRI), the collaboration between human and robot can significantly improve the production efficiency through combination of the humans flexible intelligence and the robots consistent performance. In this application, however, it is an important concern to ensure the safety of the human and the robot. In the human guidance programming scenario, the operator plans a collision-free path for the robot end-effector, but the robot body might collide with an obstacle while being guided by the operator. In this paper, a novel on-line velocity based collision avoidance algorithm is developed to solve the problem in this particular scenario. The proposed algorithm gives an explicit solution to deal with both collision avoidance and human guidance command at the same time, which provides the operator a better and safer lead through programming experience. The real-time experiment is performed on FANUC LR Mate 200 iD/7L in three different obstacle scenarios.

Autonomous alignment of peg and hole by force/torque measurement for robotic assembly

In the past years, many methods have been developed for robotic peg-hole-insertion to automate the assembly process. However, many of them are based on the assumption that the peg and hole are well aligned before insertion starts. In practice, if there is a large pose(position/orientation) misalignment, the peg and hole may suffer from a three-point contact condition where the traditional assembly methods cannot work. To deal with this problem, this paper proposes an autonomous alignment method by force/torque measurement before insertion phase. A three-point contact model is built up and the pose misalignment between the peg and hole is estimated by force and geometric analysis. With the estimated values, the robot can autonomously correct the misalignment before applying traditional assembly methods to perform insertions. A series of experiments on a FANUC industrial robot and a H7h7 tolerance peg-hole testbed validate the effectiveness of the proposed method. Experimental results show that the robot is able to perform peg-hole-insertion from three-point contact conditions with 96% success rate.

Remote Lead Through Teaching by Human Demonstration Device

Industrial robots are playing increasingly important roles in production lines. The traditional pendant programming method, however, is unintuitive and time-consuming. Its complicated operation also sets a high requirement on users. To simplify the robot programming process, many new methods have been proposed, such as lead through teaching, teleoperation, and human direct demonstration. Each of these methods, however, suffers from its own drawbacks. To overcome the drawbacks, a novel robot programming method, remote lead through teaching (RLTT), is introduced in this paper. In RLTT, the operator uses a device to train the robot remotely, allowing the demonstrators to use the mature lead through teaching techniques in a safe environment. In order to implement RLTT, the human demonstration device (HDD) is also designed to transfer the demonstration information from the human to the robot.Copyright

Optimized central pattern generator network for NAO humanoid walking control

In this paper, an optimized central pattern generator (CPG) network is proposed for humanoid walking control. The CPG controller targets three joints (hip, knee and ankle) of each leg including 4 degrees of freedom (DOFs). The connections for CPG units of related joints are simplified and optimized hierarchically. The total number of CPG parameters is greatly decreased in this way. Moreover, the genetic algorithm (GA) is adopted to acquire the optimal parameters in batch and the complexity of the algorithm is decreased greatly. Finally, the proposed CPG controller is applied to the straight and circular walking on a commercial humanoid robot NAO, both in simulations and practices.

Robot learning from human demonstration with remote lead hrough teaching

Industrial robots are playing increasingly important roles in factories. Many production applications require both position and force control; however, tuning the positionforce controller is nontrivial. To simplify this process, the learning from demonstration (LfD) is proposed to transfer the human skills directly into robot applications. However, the current teaching methods, such as direct demonstration, lead through teaching, and teleoperation, all have their own drawbacks. Hence, Remote Lead Through Teaching (RLTT) is proposed to robot learn some tasks from human knowledge and skill. To implement the human skill model, the demonstration data is firstly synchronized by dynamic time warping (DTW), then decomposed into several actions by a support vector machine (SVM) based classifier. Lastly, the learning controller is trained by the Gaussian mixture regression (GMR). The experimental validation is realized on FANUC LR Mate 200ÍD/7L in a H7/h7 peg-hole insertion task and a surface grinding task.

Zero time delay input shaping for smooth settling of industrial robots

Precise motion control is desired in a variety of industrial robot applications. In order to achieve precise and rapid rest-to-rest motion, the overshoot and the residual vibration should be minimized. In this paper, a modified input shaping approach is developed to address these problems. The time delay introduced by conventional input shaping technique is fully compensated in the proposed approach. Experiment result on an industrial robot has shown the effectiveness of the proposed approach.