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。     

Robot teaching system based on hand-robot contact state detection and motion intention recognition

This paper presents a robot teaching system based on hand-robot contact state detection and human motion intent recognition. The system can detect the contact state of the hand-robot joint and extracts motion intention information from the human surface electromyography (sEMG) signals to control the robot's motion. First, a hand-robot contact state detection method is proposed based on the fusion of the virtual robot environment with the physical environment. With the use of a target detection algorithm, the position of the human hand in the color image of the physical environment can be identified and its pixel coordinates can be calculated. Meanwhile, the synthetic images of the virtual robot environment are combined with those of the physical robot scene to determine whether the human hand is in contact with the robot. Besides, a human motion intention recognition model based on deep learning is designed to recognize human motion intention with the input of sEMG signals. Moreover, a robot motion mode selection module is built to control the robot for single-axis motion, linear motion, or repositioning motion by combining the hand-robot contact state and human motion intention. The experimental results indicate that the proposed system can perform online robot teaching for the three motion modes.     

Dynamic path planning for a planar articulated robot arm moving amidst unknown obstacles

This work is concerned with planning collision-free paths for a robot arm moving in an environment filled with unknown obstacles, where any point of the robot body is subject to collision. To compensate for the uncertainty, the system is provided with sensory feedback information about its immediate surroundings. In such a setting, which presents significant practical and theoretical interest, human intuition is of little help, and designing algorithms with proven convergence thus becomes an important task. We show that, given the target position, local feedback information is sufficient to guarantee reaching a global objective (the target position) and present a nonheuristic algorithm which generates reasonable—if, in general, not optimal—collision-free paths. In this approach, the path is being planned continuously (dynamically), based on the arm's current position and on the sensory feedback. Here, a case of a planar arm with two revolute joints is studied. No constraints on the shape of the robot links or the obstacles are imposed. The general idea is to reduce the problem of motion planning to an analysis of simple closed curves on the surface of an appropriate two-dimensional manifold.     

Experience-based imitation using RNNPB

Robot imitation is a useful and promising alternative to robot programming. Robot imitation involves two crucial issues. The first is how a robot can imitate a human whose physical structure and properties differ greatly from its own. The second is how the robot can generate various motions from finite programmable patterns (generalization). This paper describes a novel approach to robot imitation based on its own physical experiences. We considered the target task of moving an object on a table. For imitation, we focused on an active sensing process in which the robot acquires the relation between the object's motion and its own arm motion. For generalization, we applied the RNNPB (recurrent neural network with parametric bias) model to enable recognition/generation of imitation motions. The robot associates the arm motion which reproduces the observed object's motion presented by a human operator. Experimental results proved the generalization capability of our method, which enables the robot to imitate not only motion it has experienced, but also unknown motion through nonlinear combination of the experienced motions.     

Markerless human–robot interface for dual robot manipulators using Kinect sensor

Remote teleoperation of robot manipulators is often necessary in unstructured, dynamic, and dangerous environments. However, the existing mechanical and other contacting interfaces require unnatural, or hinder natural, human motions. At present, the contacting interfaces used in teleoperation for multiple robot manipulators often require multiple operators. Previous vision-based approaches have only been used in the remote teleoperation for one robot manipulator as well as require the special quantity of illumination and visual angle that limit the field of application. This paper presents a noncontacting Kinect-based method that allows a human operator to communicate his motions to the dual robot manipulators by performing double hand–arm movements that would naturally carry out an object manipulation task. This paper also proposes an innovative algorithm of over damping to solve the problem of error extracting and dithering due to the noncontact measure. By making full use of the human hand–arm motion, the operator would feel immersive. This human–robot interface allows the flexible implementation of the object manipulation task done in collaboration by dual robots through the double hand–arm motion by one operator.     

On responsiveness, safety, and completeness in real-time motion planning

Replanning is a powerful mechanism for controlling robot motion under hard constraints and unpredictable disturbances, but it involves an inherent tradeoff between the planner’s power (e.g., a planning horizon or time cutoff) and its responsiveness to disturbances. This paper presents an adaptive time-stepping architecture for real-time planning with several advantageous properties. By dynamically adapting to the amount of time needed for a sample-based motion planner to make progress toward the goal, the technique is robust to the typically high variance exhibited by replanning queries. The technique is proven to be safe and asymptotically complete in a deterministic environment and a static objective. For unpredictably moving obstacles, the technique can be applied to keep the robot safe more reliably than reactive obstacle avoidance or fixed time-step replanning. It can also be applied in a contingency planning algorithm that achieves simultaneous safety-seeking and goal-seeking motion. These techniques generate responsive and safe motion in both simulated and real robots across a range of difficulties, including applications to bounded-acceleration pursuit-evasion, indoor navigation among moving obstacles, and aggressive collision-free teleoperation of an industrial robot arm.     

基于似人特性的机器人拟人臂自主抓取动作规划

针对拟型服务机器人在家庭环境中的自主抓取任务,提出了一种强调运动姿态似人特性的机器人手臂动作的运动规划方法.该方法基于人体工程学中的快速上肢评估准则评价机器人运动姿态的似人特性,并在此基础上以机器人传速速率最优为目标规划机器人持物动作的姿态构型.最后以Motoman SDA10D拟人双臂机器人为例,具体介绍了该方法的应用和规划的结果,规划结果证明了该方法的可行性和有效性.     

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.     

Exploration of unknown object by active touch of robot hand

This paper proposes a method of exploring the local shape of an unknown object using the force and torque information obtained from active touch. In the first, we present a method to estimate an unknown curvature, using rolling and sliding motion with a force/torque sensor attached to the fingertip of the hand. Then, the normal curvature equation from 2D curvatures is obtained. Finally we present a reconstruction algorithm of local geometry by using a normal curvature equation, which is composed of principal curvatures and principal directions. The method is tested by using a hand-arm system consisting of an industrial robot arm and an anthropomorphic robot hand with 6-axis force/torque sensor. The feasibility of the proposed method is experimentally validated for objects with simple geometries such as cylinder, spheres etc.     

Haptic rendering: introductory concepts

Haptic rendering allows users to "feel" virtual objects in a simulated environment. We survey current haptic systems and discuss some basic haptic-rendering algorithms. In the past decade we've seen an enormous increase in interest in the science of haptics. Haptics broadly refers to touch interactions (physical contact) that occur for the purpose of perception or manipulation of objects. These interactions can be between a human hand and a real object; a robot end-effector and a real object; a human hand and a simulated object (via haptic interface devices); or a variety of combinations of human and machine interactions with real, remote, or virtual objects. Rendering refers to the process by which desired sensory stimuli are imposed on the user to convey information about a virtual haptic object.     

A study on effect of biarticular muscles in an antagonistically actuated robot arm through numerical simulations

Control of articulated robots by biarticular actuation has recently attracted great attention in the research field of robotics. Although many of studies concerned with this issue deal with legged robots or robot arms kinetically interacting with environment such as a floor or an object, motion control of an articulated robot arm with no kinetic interaction is also an interesting topic of biarticular actuation. In the motion control, a major issue is how it is possible for biarticular actuation to contribute to improvement of control; however, showing a clear finding for this issue seems to be considerably difficult. This paper considers a study for exploring that issue. Biarticular actuation usually constitutes a redundant actuation system; therefore, control of a robot arm to a desired posture can be achieved by many combinations of actuator forces. Based on this feature, this paper considers three typical combinations of actuator forces. Point-to-point control of the robot is performed for each of the combinations in simulation, and control performances of the combinations are compared with each other. In addition, the performances are compared with that of monoarticular actuation. In those comparisons, two of the three combinations show similar control performances, which suggests possibility of major contribution of biarticular actuators to motion control of a robot arm. On the other hand, control performance of the other combination is similar to that of monoarticular actuation, rather than those of other two combinations.     

A biomimetic reach and grasp approach for mechanical hands

Reach and grasp are the two key functions of human prehension. The Central Nervous System controls these two functions in a separate but interdependent way. The choice between different solutions to reach and grasp an object–provided by multiple and redundant degrees of freedom (dof)–depends both on the properties and on the use (affordance) of the object to be manipulated. This same control paradigm, i.e. subdivision of prehension into reach and grasp as well as the corresponding multimodal (sensory/motor) information fusion schemes, can also be applied to a mechanical hand carried by a robotic arm. The robotic arm will then be responsible for positioning the hand with respect to the object, and the hand will then grasp and manipulate the object. In this article, we present a biomimetic sensory–motor control scheme in the aim of providing an object-dependent and intelligent reach and grasp ability to such systems. The proposed model is based on a multi-network architecture which incorporates multiple Matching Units trained by a statistical learning algorithm (LWPR). Matching Units perform a multimodal signal integration by correlating sensory and motor information analogous to that observed in cerebral neuronal networks. The simulated network of multiple Matching Units provided estimations of object-dependent 5-finger grasp configurations with endpoint positional errors in the order of a few millimeters. For validation, these estimations were then applied to the control of movement kinematics on an experimental robot composed of a 6 dof robot arm carrying a 16 dof mechanical 4-finger hand. Precision of the kinematics control was such that successful reach, grasp and lift was obtained in all the tests.     

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.     

Design of robotic behavior that imitates animal consciousness

In order to satisfy need for enhanced user affinity for robots, we are attempting to give robots a “consciousness” such as that identified in humans and animals. We developed software to control a robot’s actions including emotion by introducing the evaluation function of action choice into the hierarchical structure model. This connected the robot’s consciousness with the robot’s action. We named the process Consciousness-based Architecture (CBA). However, it is difficult to change the consciousness of the robot only using this CBA model. In order to induce and change autonomously consciousness and action for the robot, some motivation is required. Therefore, a motivation model has been developed to induce and change autonomously, and is combined with CBA. To inspect CBA including the motivation model, a robot arm (Conbe-I) has been developed with a small Web camera built into the fingers. CBA was installed on this Conbe-I, and the autonomous actions performed to catch an object were inspected. A motivation model of the robot was devised to describe interests for the aim thing of the robot and the desire of the robot. To build this motivation model, we studied the action of dopamine, which added activity to the robot, in conjunction with the incentive to do an action. In this paper described about the expression of the emotion by a robot incorporated this motivation model in Conbe-I. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A robot safety experiment varying robot speed and contrast with human decision cost

An industrial robot safety experiment was performed to find out how quickly subjects responded to an unexpected robot motion at different speeds of the robot arm, and how frequently they failed to detect a motion that should have been detected. Robotics technicians risk being fatally injured if a robot should trap them against a fixed object. The value of the experimentation lies in its ability to show that this risk can be reduced by a design change. If the robot is moving at a slow speed, during programming and troubleshooting tasks, then the worker has sufficient time to actuate an emergency stop device before the robot can reach the person. The dependent variable in the experiment was the overrun distance (beyond an expected stopping point) that a robot arm travelled before a person actuated a stop pushbutton. Results of this experiment demonstrated that the speed of the robot arm and the implied decision cost for hitting an emergency stop button had a significant effect on human reaction time. At a fairly high level of ambient lighting (560 lux), fixed-level changes in the luminance contrast between the robot arm and its background did not significantly affect human reaction time.     

可重构机器人越障运动的规划

提出一种新型的可重构机器人,该机器人在受一个驱动力矩的作用时,在不同约束下表现的输出形式不同. 利用机构这一特点,实现了机器人移动中的自主越障运动．主要针对机器人越障运动中越障高度受导轮和手臂影响的问题,提出一种借助环境同时改变手臂姿态的越障规划方法,有效地提高了越障的高度．通过试验样机验证了规划方法的有效性．     

An efficient motion generation method for redundant humanoid robot arms based on motion continuity

This paper proposes a novel method of motion generation for redundant humanoid robot arms, which can efficiently generate continuous collision-free arm motion for the preplanned hand trajectory. The proposed method generates the whole arm motion first and then computes the actuators’ motion, which is different from IK (inverse kinematics)-based motion generation methods. Based on the geometric constraints of the preplanned trajectory and the geometric structure of humanoid robot arms, the wrist trajectory and elbow trajectory can be got first without solving inverse kinematics and forward kinematics. Meanwhile, the constraints restrict all feasible arm configurations to an elbow-circle and reduce the arm configuration space to a two-dimension space. By combining the configuration space and collision distribution of arm motion, collision-free arm configurations can be identified and be used to generate collision-free arm motion, which can avoid unnecessary forward and inverse kinematics. The experiments show that the proposed method can generate continuous and collision-free arm motion for preplanned hand trajectories.     

人与机器人共存中的位姿估计与碰撞检测

提出了一种人与机器人共存中的位姿估计与碰撞检测方法。首先,利用光学3维动作捕捉系统获取标记点位姿信息,建立人体手臂的运动学模型。其次,针对工作空间中障碍物遮挡导致部分标记点位姿信息丢失的问题,将角度传感器获取的肘关节角度作为人体手臂运动学模型的输入,获取人体手臂末端位姿信息。再次,构建人体手臂和协作机器人的胶囊体模型,计算各胶囊体之间的最短距离,从而判断人机的相对位姿关系并实现碰撞检测。最后,通过10个人在不同人机共存场景下对人机位姿估计与碰撞检测方法进行评价。实验结果表明,本方法估计的人体手臂末端位置误差在20mm以内,人机最短距离的最大误差为14.53mm,能够实现人机碰撞检测。     

Towards autonomous robotic servicing: Using an integrated hand-arm-eye system for manipulating unknown objects

Executing complex robotic tasks including dexterous grasping and manipulation requires a combination of dexterous robots, intelligent sensors and adequate object information processing. In this paper, vision has been integrated into a highly redundant robotic system consisting of a tiltable camera and a three-fingered dexterous gripper both mounted on a puma-type robot arm. In order to condense the image data of the robot working space acquired from the mobile camera, contour image processing is used for offline grasp and motion planning as well as for online supervision of manipulation tasks. The performance of the desired robot and object motions is controlled by a visual feedback system coordinating motions of hand, arm and eye according to the specific requirements of the respective situation. Experiences and results based on several experiments in the field of service robotics show the possibilities and limits of integrating vision and tactile sensors into a dexterous hand-arm-eye system being able to assist humans in industrial or servicing environments.