Robotized recognition of a wire harness utilizing tracing operation

In assembly lines, deformation of the objects makes many efforts on automating the corresponding processes difficult. Mating of a wire harness onto a car body is among the representative tasks. Academically, this kind of problems are known as automatic manipulation of deformable linear objects. Previous technical approaches for solving the problem is characterized by employment of multiple contactless sensors and complex sensor based algorithms. These attempts are verified to be feasible only for simplified environments. The dependence of the approaches on real-time measurement to the state of the deformable targets limits their application in practical factory environment. In this paper, it is proposed to utilize wire tracing operation in recognizing the wire harness for the purpose of automatic mating. This method is inspired from the behaviors of humans demonstrated when they deal with similar problems. The proposed method is implemented and verified in a robot system. The feasibility and effectiveness demonstrated in the preliminary experiments show the potential of the proposed method for substantially simplifying the wire recognition problems which is thought to be crucial for practices in factory environments.     

Tolerance dataset: mating process of plug-in cable connectors for wire harness assembly tasks

In this research, we studied the mating tolerance of various plug-in cable connectors and provide a mating tolerance dataset of 70 different connectors. This dataset will be highly advantageous to industries for wire harness assembly tasks using robots. Understanding the mating tolerance is crucial for automating the mating process because it is closely related to the control specifications of a robotic manipulator. Our system uses a 2-finger Robotiq adaptive gripper attached to a 6 degree-of-freedom industrial robot (ABB Robotics) to test the mating process of wire harness assembly tasks. In addition, we use 70 types of wire harness connectors with different numbers of pins widths, lengths, and thicknesses, and various shapes, to test the mating tolerance. The results indicate that the connector mating tolerance of our dataset is more generous than the repeatability of conventional industrial manipulators, and further demonstrate the suitability of the position control methods to wire harness assembly tasks.

     

Active Learning for Vision-Based Robot Grasping

Reliable vision-based grasping has proved elusive outside of controlled environments. One approach towards building more flexible and domain-independent robot grasping systems is to employ learning to adapt the robot's perceptual and motor system to the task. However, one pitfall in robot perceptual and motor learning is that the cost of gathering the learning set may be unacceptably high. Active learning algorithms address this shortcoming by intelligently selecting actions so as to decrease the number of examples necessary to achieve good performance and also avoid separate training and execution phases, leading to higher autonomy. We describe the IE-ID3 algorithm, which extends the Interval Estimation (IE) active learning approach from discrete to real-valued learning domains by combining IE with a classification tree learning algorithm (ID-3). We present a robot system which rapidly learns to select the grasp approach directions using IE-ID3 given simplified superquadric shape approximations of objects. Initial results on a small set of objects show that a robot with a laser scanner system can rapidly learn to pick up new objects, and simulation studies show the superiority of the active learning approach for a simulated grasping task using larger sets of objects. Extensions of the approach and future areas of research incorporating more sophisticated perceptual and action representation are discussed     

Robust implementation of generalized impedance control for robot manipulators

Compliant manipulation requires the robot to follow a motion trajectory and to exert a force profile while making compliant contact with a dynamic environment. For this purpose, a generalized impedance in the task space consisting of a second-order function relating motion errors and interaction force errors is introduced such that force tracking can be achieved. Using variable structure model reaching control, the generalized impedance is realized in the presence of parametric uncertainties. The proposed control method is applied to a multi-d.o.f. robot for an assembly task of inserting a printed circuit board into an edge connector socket. It is suggested that an assembly strategy which involves a sequence of planned target generalized impedances can enable the task to be executed in a desirable manner. The effectiveness of this approach is illustrated through experiments by comparing the results with those obtained using a model-based control implementation.     

Discovering and restoring changes in object positions using an autonomous robot with laser rangefinders

This paper describes an object rearrangement system for an autonomous mobile robot. The objective of the robot is to autonomously explore and learn about an environment, to detect changes in the environment on a later visit after object disturbances and finally, to move objects back to their original positions. In the implementation, it is assumed that the robot does not have any prior knowledge of the environment and the positions of the objects. The system exploits Simultaneous Localisation and Mapping (SLAM) and autonomous exploration techniques to achieve the task. These techniques allow the robot to perform localisation and mapping which is required to perform the object rearrangement task autonomously. The system includes an arrangement change detector, object tracking and map update that work with a Polar Scan Match (PSM) Extended Kalman Filter (EKF) SLAM system. In addition, a path planning technique for dragging and pushing an object is also presented in this paper. Experimental results of the integrated approach are shown to demonstrate that the proposed approach provides real-time autonomous object rearrangements by a mobile robot in an initially unknown real environment. Experiments also show the limits of the system by investigating failure modes.     

Learning Novel Objects for Extended Mobile Manipulation

We propose a method for learning novel objects from audio visual input. The proposed method is based on two techniques: out-of-vocabulary (OOV) word segmentation and foreground object detection in complex environments. A voice conversion technique is also involved in the proposed method so that the robot can pronounce the acquired OOV word intelligibly. We also implemented a robotic system that carries out interactive mobile manipulation tasks, which we call “extended mobile manipulation”, using the proposed method. In order to evaluate the robot as a whole, we conducted a task “Supermarket” adopted from the RoboCup@Home league as a standard task for real-world applications. The results reveal that our integrated system works well in real-world applications.     

基于工程语义约束的线束预装配自动规划方法

线束预装配是线束生产的重要环节,论文通过总结线束工艺设计中的工程 语义约束信息,建立装配关联图模型,提出了一种线束预装配自动规划方法。该方法通过主 件选择、最大子装配体提取、工程语义约束检测来实现对预装配序列自动规划,而无需与用 户进行交互。最后的实例表明该方法是有效可行的,能较好的解决大线束图纸中装配序列规 划问题。     

Online robot calibration based on vision measurement

Robot calibration is a useful diagnostic method to improve positioning accuracy in robot production and maintenance. Unlike traditional calibration methods that require expensive equipment and complex steps, a vision-based online robot calibration method that only requires several reference images is presented in this paper. The method requires a camera that is rigidly attached to the robot end effector (EE), and a calibration board must be settled around the robot where the camera can see it. An efficient automatic approach to detect the corners from the images of the calibration board is proposed. The poses of the robot can be estimated from the detected corners. The kinematic parameters can be conducted automatically based on the known poses of the robot. Unlike in the existing self-calibration methods, the great advantage of this online self-calibration method is that the entire process of robot calibration is automatic and without any manual intervention, enabling the robot calibration to be completed online when the robot is working. Therefore, the proposed approach is particularly suitable for unknown environments, such as deep sea or outer space. In these high-temperature and/or high-pressure environments, the shapes of the robot links are easy to change. Thus, the robot kinematic parameters are changed by allowing the robot to grab objects with different qualities to verify the performance of the online robot calibration. Experimental studies on a GOOGOL GRB3016 robot show that the proposed method has high accuracy, convenience, and high efficiency.     

Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. Experimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.     

Learning Dextrous Manipulation Skills for Multifingered Robot Hands Using the Evolution Strategy

We present a method for autonomous learning of dextrous manipulation skills with multifingered robot hands. We use heuristics derived from observations made on human hands to reduce the degrees of freedom of the task and make learning tractable. Our approach consists of learning and storing a few basic manipulation primitives for a few prototypical objects and then using an associative memory to obtain the required parameters for new objects and/or manipulations. The parameter space of the robot is searched using a modified version of the evolution strategy, which is robust to the noise normally present in real-world complex robotic tasks. Given the difficulty of modeling and simulating accurately the interactions of multiple fingers and an object, and to ensure that the learned skills are applicable in the real world, our system does not rely on simulation; all the experimentation is performed by a physical robot, in this case the 16-degree-of-freedom Utah/MIT hand. E xperimental results show that accurate dextrous manipulation skills can be learned by the robot in a short period of time. We also show the application of the learned primitives to perform an assembly task and how the primitives generalize to objects that are different from those used during the learning phase.     

Robot programming system for assembly: Conceptual graph-based approach

This work develops a method for robot program synthesis. Currently, the programming task is one of the major hurdles of robotic application. Progress towards automatic synthesis of robot programs will ease industrial robot application. The proposed system provides a means towards automated (guided by knowledge) conversion of a user's request, expressed in natural language, to the appropriate conceptual model of the required task. This model incorporates the information necessary for understanding, planning, and sensory-guided performance of the required robotic task.First, we state the problem of robotic assembly and recognize its hierarchic structure as the structure of a system that builds a predesigned assembly. Next, we present and analyze the requirements of the robotic assembly domain. This analysis enables us to draw conclusions concerning the most suitable methodology for the development of a support system for assembly program synthesis and interpretation. It is the conceptual graph-based approach. Then we present the algorithm of the proposed conceptual graph-based system and show how the system synthesizes robotic assembly operations, such as valid assembly sequences and sequences of special treatments for the assembled components (including the determination of the required resources). Finally, a case study illustrates the approach developed here on a large family of multi-axisymmetric components. We also present illustrative examples of working sessions with the current implementation of the system.     

Indoor scene recognition by a mobile robot through adaptive object detection

Mobile robotics has achieved notable progress, however, to increase the complexity of the tasks that mobile robots can perform in natural environments, we need to provide them with a greater semantic understanding of their surrounding. In particular, identifying indoor scenes, such as an Office or a Kitchen, is a highly valuable perceptual ability for an indoor mobile robot, and in this paper we propose a new technique to achieve this goal. As a distinguishing feature, we use common objects, such as Doors or furniture, as a key intermediate representation to recognize indoor scenes. We frame our method as a generative probabilistic hierarchical model, where we use object category classifiers to associate low-level visual features to objects, and contextual relations to associate objects to scenes. The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model. In contrast to alternative computer vision based methods, we boost performance by exploiting the embedded and dynamic nature of a mobile robot. In particular, we increase detection accuracy and efficiency by using a 3D range sensor that allows us to implement a focus of attention mechanism based on geometric and structural information. Furthermore, we use concepts from information theory to propose an adaptive scheme that limits computational load by selectively guiding the search for informative objects. The operation of this scheme is facilitated by the dynamic nature of a mobile robot that is constantly changing its field of view. We test our approach using real data captured by a mobile robot navigating in Office and home environments. Our results indicate that the proposed approach outperforms several state-of-the-art techniques for scene recognition.     

复杂机电产品中的柔性线缆装配过程仿真技术

针对复杂机电产品中柔性线缆结构复杂且在装配操作中发生变形而导致的装配过程仿真难的问题,系统地提出了柔性线缆装配过程仿真的解决方案及其关键技术的实现方法.首先提出一种基于元素包含关系和无向图的线缆数字化表达模型,实现了复杂线缆拓扑、几何和属性信息的统一表达,为装配过程仿真奠定了基础;然后提出一种改进的层次链装配过程模型,统一处理针对不同对象的装配任务和装配操作等,实现了装配过程中线缆和结构件交叉装配任务和装配操作的表达和存储;最后建立一种改进的线缆质点-弹簧模型,通过添加长度变化约束并设定典型装配操作中的约束条件,实现了线缆在装配仿真中柔性形变的实时仿真.在开发的原型系统中实现了柔性线缆的装配过程仿真,并通过实例验证了文中方法的可行性.     

Calibration of an smt robot assembly cell

A new calibration method for an assembly robot cell is described. The proposed method is a combination of a model-free, numerical, relative robot calibration procedure and a procedure for the robot periphery calibration. Two important simplifications based on the study of an assembly process are introduced into the calibration strategy. A robot is calibrated in a task (Cartesian) space. The robot workspace and the number of calibrated degrees of freedom (dof) in the task space are reduced in accordance with the difficulty measure of the task. An automatic measurement system for measuring the relative robot accuracy was developed. An original principle of transforming the robot endpoint approach distance into the one-dimensional position displacement error is introduced. The accuracy errors of each particular calibrated dof in the task space is measured separately. The error tables are used in a direct robot calibration procedure that is based on the linear interpolation of the discrete position-error functions. An iterative inverse calibration algorithm used in a particular robot cell is described. An efficient sensor-based system for an additional simultaneous robot periphery calibration is presented. The implementation of the proposed calibration methodology in the pick-and-place robot cell for Surface Mount Technology (SMT) is presented. © 1994 John Wiley & Sons, Inc.     

A Neural Network Approach to Dynamic Task Assignment of Multirobots

In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.     

Relational Model for Robotic Semantic Navigation in Indoor Environments

The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks.     

Methodology of Concept Control Synthesis to Avoid Unmoving and Moving Obstacles

The dynamic path generation problem of robots in environments with other unmoving and moving objects is considered. Generally, the problem is known in the literature as find path or robot motion planning. In this paper, we apply the behavioral cloning approach to design the robot controller. In behavioral cloning, the system learns from control traces of a human operator. The task for the given problem is to find a controller in the form of an explicit mathematical expression. Thus, machine learning programs to induce the operator's trajectories as a set of symbolic constraints are used. Then, mathematical induction to generalize the obtained equations in order to apply them in situ with an infinite number of obstacles is also used. A method to evaluate cloning success is proposed. The typical kind of noise is included.     

Multisensor-Based Human Detection and Tracking for Mobile Service Robots

One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.     

Detection of robustly collision-free trajectories in unpredictable environments in real-time

One of the ultimate goals in robotics is to make robots of high degrees of freedom (DOF) work autonomously in real world environments. However, real world environments are unpredictable, i.e., how the objects move are usually not known beforehand. Thus, whether a robot trajectory is collision-free or not has to be checked on-line based on sensing as the robot moves. Moreover, in order to guarantee safe motion, the motion uncertainty of the robot has to be taken into account. This paper introduces a general approach to detect if a high-DOF robot trajectory is continuously collision-free even in the presence of robot motion uncertainty in an unpredictable environment in real time. Our method is based on the novel concept of dynamic envelope, which takes advantage of progressive sensing over time without predicting motions of objects in an environment or assuming specific object motion patterns. The introduced approach can be used by general real-time motion planners to check if a candidate robot trajectory is continuously and robustly collision-free (i.e., in spite of uncertainty in the robot motion).