基于QRcode技术的家庭半未知环境语义地图构建

根据家庭环境中机器人特有的服务任务和人机交互要求,结合大物品上粘贴的基于QR code技术的自相似人工物标,构建半未知环境的以房间为单位的语义地图。首先基于谱聚类算法构建具有房间分割功能的拓扑地图。再利用QR code中存储的物品信息,建立物品信息库和物品归属关联图,最终获得包含物品信息描述、房间功能描述及物品-房间归属关系的语义地图。该地图为家庭环境中物品的搜寻、管理和机器人服务提供完备的、拟人化的信息。仿真实验表明,基于语义地图机器人能理解人的语义命令,生成合理的服务路径,实现功能驱动的导航。     

Efficient models for grasp planning with a multi-fingered hand

This paper presents a simple grasp planning method for a multi-fingered hand. Its purpose is to compute a context-independent and dense set or list of grasps, instead of just a small set of grasps regarded as optimal with respect to a given criterion. By context-independent, we mean that only the robot hand and the object to grasp are considered. The environment and the position of the robot base with respect to the object are considered in a further stage. Such a dense set can be computed offline and then used to let the robot quickly choose a grasp adapted to a specific situation. This can be useful for manipulation planning of pick-and-place tasks. Another application is human–robot interaction when the human and robot have to hand over objects to each other. If human and robot have to work together with a predefined set of objects, grasp lists can be employed to allow a fast interaction.The proposed method uses a dense sampling of the possible hand approaches based on a simple but efficient shape feature. As this leads to many finger inverse kinematics tests, hierarchical data structures are employed to reduce the computation times. The data structures allow a fast determination of the points where the fingers can realize a contact with the object surface. The grasps are ranked according to a grasp quality criterion so that the robot will first parse the list from best to worse quality grasps, until it finds a grasp that is valid for a particular situation.     

A human–robot interface for mobile manipulator

This paper presents a remote manipulation method for mobile manipulator through operator’s gesture. In particular, a track mobile robot is equipped with a 4-DOF robot arm to grasp objects. Operator uses one hand to control both the motion of mobile robot and the posture of robot arm via scheme of gesture polysemy method which is put forward in this paper. A sensor called leap motion (LM), which can obtain the position and posture data of hand, is employed in this system. Two filters were employed to estimate the position and posture of human hand so as to reduce the inherent noise of the sensor. Kalman filter was used to estimate the position, and particle filter was used to estimate the orientation. The advantage of the proposed method is that it is feasible to control a mobile manipulator through just one hand using a LM sensor. The effectiveness of the proposed human–robot interface was verified in laboratory with a series of experiments. And the results indicate that the proposed human–robot interface is able to track the movements of operator’s hand with high accuracy. It is found that the system can be employed by a non-professional operator for robot teleoperation.     

排爆机器人控制与无线通讯系统

排爆机器人控制可分为小车的行走控制和机械手的运动控制。小车的行走控制需要进行“路程规划”以实现小车避障和向目标物(可疑爆炸物)靠近,而机械手的运动控制需要进行“轨迹规划”以避开障碍物实现避碰,顺利抓取、搬运目标物。此机器人控制系统是开放式系统,实现了智能化和网络化。远程管理机房电脑可以显示现场机器人手爪、目标物、障碍物。     

Fast grasping of unknown objects through automatic determination of the required number of mobile robots

In this paper, we present a strategy for fast grasping of unknown objects by mobile robots through automatic determination of the number of robots. An object handling system consisting of a Gripper robot and a Lifter robot is designed. The Gripper robot moves around an unknown object to acquire partial shape information for determination of grasping points. The object is transported if it can be lifted by the Gripper robot. Otherwise, if all grasping trials fail, a Lifter robot is used. In order to maximize use of the Gripper robot’s payload, the detected grasping points that apply the largest force to the gripper are selected for the Gripper robot when the object is grasped by two mobile robots. The object is measured using odometry and scanned data acquired while the Gripper robot moves around the object. Then, the contact point for calculating the insert position for the Lifter robot can be acquired quickly. Finally, a strategy for fast grasping of known objects by considering the transition between stable states is used to realize grasping of unknown objects. The proposed approach is tested in experiments, which find that a wide variety of objects can be grasped quickly with one or two mobile robots.     

Robotic execution of everyday tasks by means of external vision/force control

In this article, we present an integrated manipulation framework for a service robot, that allows to interact with articulated objects at home environments through the coupling of vision and force modalities. We consider a robot which is observing simultaneously his hand and the object to manipulate, by using an external camera (i.e. robot head). Task-oriented grasping algorithms (Proc of IEEE Int Conf on robotics and automation, pp 1794–1799, 2007) are used in order to plan a suitable grasp on the object according to the task to perform. A new vision/force coupling approach (Int Conf on advanced robotics, 2007), based on external control, is used in order to, first, guide the robot hand towards the grasp position and, second, perform the task taking into account external forces. The coupling between these two complementary sensor modalities provides the robot with robustness against uncertainties in models and positioning. A position-based visual servoing control law has been designed in order to continuously align the robot hand with respect to the object that is being manipulated, independently of camera position. This allows to freely move the camera while the task is being executed and makes this approach amenable to be integrated in current humanoid robots without the need of hand-eye calibration. Experimental results on a real robot interacting with different kind of doors are presented.     

Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

The purpose of this study is to develop partner robots that can obtain and accumulate human-friendly behaviors. To achieve this purpose, the entire architecture of the robot is designed, based on a concept of structured learning which emphasizes the importance of interactive learning of several modules through interaction with its environment. This paper deals with a trajectory planning method for generating hand-to-hand behaviors of a partner robot by using multiple fuzzy state-value functions, a self-organizing map, and an interactive genetic algorithm. A trajectory for the behavior is generated by an interactive genetic algorithm using human evaluation. In order to reduce human load, human evaluation is estimated by using the fuzzy state-value function. Furthermore, to cope with various situations, a self-organizing map is used for clustering a given task dependent on a human hand position. And then, a fuzzy state-value function is assigned to each output unit of the self-organizing map. The robot can easily obtain and accumulate human-friendly trajectories using a fuzzy state-value function and a knowledge database corresponding to the unit selected in the self-organizing map. Finally, multiple fuzzy state-value functions can estimate a human evaluation model for the hand-to-hand behaviors. Several experimental results show the effectiveness of the proposed method.     

基于机器视觉的非特定物体的智能抓取系统的研究

针对传统机械臂局限于按既定流程对固定位姿的特定物体进行机械化抓取,设计了一种基于机器视觉的非特定物体的智能抓取系统;系统通过特定的卷积神经网络对深度相机采集到的图像进行目标定位,并在图像上预测出一个该目标的可靠抓取位置,系统进一步将抓取位置信息反馈给机械臂,机械臂根据该信息完成对目标物体的抓取操作;系统基于机器人操作系统,硬件之间通过机器人操作系统的话题机制传递必要信息;最终经多次实验结果表明,通过改进的快速搜索随机树运动规划算法,桌面型机械臂能够根据神经网络模型反馈的的标记位置对不同位姿的非特定物体进行实时有效的抓取,在一定程度上提高了机械臂的自主能力,弥补了传统机械臂的不足.     

Object–object interaction affordance learning

This paper presents a novel object–object affordance learning approach that enables intelligent robots to learn the interactive functionalities of objects from human demonstrations in everyday environments. Instead of considering a single object, we model the interactive motions between paired objects in a human–object–object way. The innate interaction-affordance knowledge of the paired objects are learned from a labeled training dataset that contains a set of relative motions of the paired objects, human actions, and object labels. The learned knowledge is represented with a Bayesian Network, and the network can be used to improve the recognition reliability of both objects and human actions and to generate proper manipulation motion for a robot if a pair of objects is recognized. This paper also presents an image-based visual servoing approach that uses the learned motion features of the affordance in interaction as the control goals to control a robot to perform manipulation tasks.     

GripSee: A Gesture-Controlled Robot for Object Perception and Manipulation

We have designed a research platform for a perceptually guided robot, which also serves as a demonstrator for a coming generation of service robots. In order to operate semi-autonomously, these require a capacity for learning about their environment and tasks, and will have to interact directly with their human operators. Thus, they must be supplied with skills in the fields of human-computer interaction, vision, and manipulation. GripSee is able to autonomously grasp and manipulate objects on a table in front of it. The choice of object, the grip to be used, and the desired final position are indicated by an operator using hand gestures. Grasping is performed similar to human behavior: the object is first fixated, then its form, size, orientation, and position are determined, a grip is planned, and finally the object is grasped, moved to a new position, and released. As a final example for useful autonomous behavior we show how the calibration of the robot's image-to-world coordinate transform can be learned from experience, thus making detailed and unstable calibration of this important subsystem superfluous. The integration concepts developed at our institute have led to a flexible library of robot skills that can be easily recombined for a variety of useful behaviors.     

Distributed environment representation and object localization system in intelligent space

A kind of new environment representation and object localization scheme is proposed in the paper aiming to accomplish the task of object operation more efficiently in intelligent space.First,a distributed environment representation method is put forward to reduce storage burden and improve the system’s stability.The layered topological maps are separately stored in different landmarks attached to the key positions of intelligent space,so that the robot can search the landmarks on which the map information can be read from the QR code,and then the environment map can be built autonomously.Map building is an important prerequisite for object search.An object search scheme based on RFID and vision technology is proposed.The RFID tags are attached to the target objects and reference objects in the indoor environment. A fixed RFID system is built to monitor the rough position(room and local area)of target and a mobile RFID system is constructed to detect the targets which are not in the covering range of the fixed system.The existing area of target is determined by the time sequence of reference tags and target tags,and the accurate position is obtained by onboard vision system at a short distance.The experiments demonstrate that the distributed environment representation proposed in the paper can fully meet the requirements of object localization,and the positioning scheme has high search efficiency,high localization accuracy and precision,and a strong anti-interference ability in the complex indoor environment.     

Integrating a flexible anthropomorphic, robot hand into the control, system of a humanoid robot

This article presents the approaches taken to integrate a novel anthropomorphic robot hand into a humanoid robot. The requisites enabling such a robot hand to use everyday objects in an environment built for humans are presented. Starting from a design that resembles the human hand regarding size and movability of the mechatronical system, a low-level control system is shown providing reliable and stable controllers for single joint angles and torques, entire fingers and several coordinated fingers. Further on, the high-level control system connecting the low-level control system with the rest of the humanoid robot is presented. It provides grasp skills to the superior robot control system, coordinates movements of hand and arm and determines grasp patterns, depending on the object to grasp and the task to execute. Finally some preliminary results of the system, which is currently tested in simulations, will be presented.     

Detection and prevention of slip using sensors with different properties embedded in elastic artificial skin on the basis of previous experience

In dexterous robotic manipulation, it is essential to control the force exerted by the robot hands while grasping. This paper describes a method by which robot hands can be controlled on the basis of previous experience of slippage of objects held by the hand. We developed an anthropomorphic human scale robot hand equipped with an elastic skin in which two types of sensor are randomly embedded. One of these sensors is a piezoelectric polyvinylidenefluoride (PVDF) film which can be used for the detection of pressure changes. The other is a strain gauge which can measure static pressure. In our system, PVDF films are used to detect slipping, and strain gauges to measure stresses which are caused by normal and shear forces. The stress measured by the strain gauges is used as input data to a neural network which controls the actuators of the robot. Once slip is detected, the neural network is updated to prevent it. We show that this system can control the grasp force of the robot hand and adapt it to the weight of the object. By using this method, it was shown that robots can hold objects safely.     

Dynamic perception after visually guided grasping by a human-like autonomous robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.     

Towards cognitive grasping: modeling of unknown objects and its corresponding grasp types

This paper describes an intuitive approach for a cognitive grasp of a robot. The cognitive grasp means the chain of processes that make a robot to learn and execute a grasping method for unknown objects like a human. In the learning step, a robot looks around a target object to estimate the 3D shape and understands the grasp type for the object through a human demonstration. In the execution step, the robot correlates an unknown object to one of known grasp types by comparing the shape similarity of the target object based on previously learned models. For this cognitive grasp, we mainly deal with two functionalities such as reconstructing an unknown 3D object and classifying the object by grasp types. In the experiment, we evaluate the performance of object classification according to the grasp types for 20 objects via human demonstration.     

Absolute localization for a mobile robot using place cells

This paper describes a method for absolute localization and environment recognition for an autonomous, sonar-equipped robot. The addition of an auto-associative memory to previously developed non-neural map making software results in a system that is capable of recognizing its environment and its position within the environment using remembered features and room geometry. In the prior system the robot used sonar to construct a metric map of an environment, but the map information had to be reconstructed each time the robot returned to an environment. We evaluated the system with a task that requires memory of the position of a goal that is not directly detectable by sonar.     

激光—相机系统语义栅格建图和路径规划

目的 SLAM（simultaneous localization and mapping）是移动机器人在未知环境进行探索、感知和导航的关键技术。激光SLAM测量精确，便于机器人导航和路径规划，但缺乏语义信息。而视觉SLAM的图像能提供丰富的语义信息，特征区分度更高，但其构建的地图不能直接用于路径规划和导航。为了实现移动机器人构建语义地图并在地图上进行路径规划，本文提出一种语义栅格建图方法。方法 建立可同步获取激光和语义数据的激光-相机系统，将采集的激光分割数据与目标检测算法获得的物体包围盒进行匹配，得到各物体对应的语义激光分割数据。将连续多帧语义激光分割数据同步融入占据栅格地图。对具有不同语义类别的栅格进行聚类，得到标注物体类别和轮廓的语义栅格地图。此外，针对语义栅格地图发布导航任务，利用路径搜索算法进行路径规划，并对其进行改进。结果 在实验室走廊和办公室分别进行了语义栅格建图的实验，并与原始栅格地图进行了比较。在语义栅格地图的基础上进行了路径规划，并采用了语义赋权算法对易移动物体的路径进行对比。结论 多种环境下的实验表明本文方法能获得与真实环境一致性较高、标注环境中物体类别和轮廓的语义栅格地图，且实验硬件结构简单、成本低、性能良好，适用于智能化机器人的导航和路径规划。     

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.     

Navigation system for an autonomous robot using an ocellus camera in an indoor environment

Autonomous and mobile robots are being expected to provide various services in human living environments. However, many problems remain to be solved in the development of autonomous robots that can work like humans. When a robot moves, it is important that it be able to have self-localization abilities and recognize obstacles. For a human, the present location can be correctly checked through a comparison between memorized information assuming, it is correct, and the present situation. In addition, the distance to an object and the perception of its size can be estimated by a sense of distance based on memory or experience. Therefore, the environment for robotic activity assumed in this study was a finite-space such as a family room, an office, or a hospital room. Because an accurate estimation of position is important to the success of a robot, we have developed a navigation system with self-localization ability which uses only a CCD camera that can detect whether the robot is moving accurately in a room or corridor. This article describes how this system has been implemented and tested with our developed robot.     

A Model of Proximity Control for Information-Presenting Robots

In this paper, we report a model that allows a robot to appropriately control its position as it presents information to a user. This capability is indispensable, since in the future, many robots will function in daily situations such as shopkeepers presenting products to customers or museum guides presenting information to visitors. Psychology research suggests that people adjust their positions to establish a joint view toward a target object. Similarly, when a robot presents an object, it should stand at an appropriate position that considers the positions of both the listener and the object to optimize the listener's field of view and establish a joint view. We observed human–human interaction situations, where people presented objects, and developed a model for an information-presenting robot to appropriately adjust its position. Our model consists of four constraints to establish O-space: 1) proximity to listener; 2) proximity to object; 3) listener's field of view; and 4) presenter's field of view. We also experimentally evaluate the effectiveness of our model.