Direct Virtual-hand Interface in Robot Assembly Programming

For a long time, robot assembly programming has been produced in two environments: on-line and off-line. On-line robot programming uses the actual robot for the experiments performing a given task; off-line robot programming develops a robot program in either an autonomous system with a high-level task planner and simulation or a 2D graphical user interface linked to other system components. This paper presents a whole hand interface for more easily performing robotic assembly tasks in the virtual tenvironment. The interface is composed of both static hand shapes (states) and continuous hand motions (modes). Hand shapes are recognized as discrete states that trigger the control signals and commands, and hand motions are mapped to the movements of a selected instance in real-time assembly. Hand postures are also used for specifying the alignment constraints and axis mapping of the hand-part coordinates. The basic virtual-hand functions are constructed through the states and modes developing the robotic assembly program. The assembling motion of the object is guided by the user immersed in the environment to a path such that no collisions will occur. The fine motion in controlling the contact and ending position/orientation is handled automatically by the system using prior knowledge of the parts and assembly reasoning. One assembly programming case using this interface is described in detail in the paper.     

An articulated limb motion planner for optimized movement

Task level animation of articulated figures, such as the human body, requires the ability to generate collision-free goal-directed motion of individual limbs in the presence of obstacles. This paper describes a new articulated limb motion planner for goal-directed point-to-point reaching motions. The produced motion avoids obstacles while optimizing an objective function. This two-phase algorithm uses heuristic guided Monte Carlo techniques to create a consistent underlying paradigm. The first phase consists of an existing potential field based random path planner which generates a population of candidate paths. This initial population is fed into the second phase, a genetic algorithm, which iteratively refines the population as it optimizes with respect to the objective function. The refinement process works on the principle of path coherency, the idea that a family of closely related collision-free paths lies in the vicinity of a given collision-free path. This paper focuses on seven different optimization functions. Optimized trajectories produced by the new motion planner are compared to those generated solely by the random path planner. The presented algorithm is flexible in that a wide range of objective functions can be optimized. Applications of the algorithm include task level animation, ergonomics and robotics.     

VRML环境下基于语义的产品装配设计技术研究

装配语义是零件间装配约束的抽象与概括，研究基于语义的产品装配设计可有效地提高装配设计的直观性与装配效率。文中给出了基于语义的产品装配设计系统的总体框架结构，将装配语义分为常用语义与特殊语义，并建立了装配语义的层次表达；通过装配语义与几何约束的映射，形成几何约束的代数表达；借助零部件间的运动自由度的求解，实现了装配语义的驱动，有助于将装配设计从几何层发展到语义层进行操作。     

基于机器视觉和力反馈的自动装配技术研究

针对工业生产中自动装配技术装配精度不高的问题,提出了一种基于机器视觉和六维力传感器的自动装配控制方法。使用两个单目摄像头对目标进行两次定位,通过改进的NCC匹配算法进行一次定位,通过基于边缘特征的模板匹配进行二次定位,利用六维力传感器获取装配过程中的力与力矩变化情况,并基于反馈的力与力矩提出了直线运动与螺旋线运动两种装配轨迹规划策略。在搭建的机器人平台上对两种策略进行了对比实验,实验结果表明,在轴孔间隙较大时直线运动装配效率较高,但当轴孔间隙小于0. 1 mm时,直线运动的装配效率和成功率均大幅下降,而螺旋线运动的装配时间主要与装配孔直径有关,对于不同轴孔间隙装配表现稳定,并能以较高成功率实现精度0. 05 mm的轴孔装配。     

Enhancing Randomized Motion Planners: Exploring with Haptic Hints

In this paper, we investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our work is motivated by our experience that automatic motion planners sometimes fail due to the difficulty of discovering critical configurations of the robot that are often naturally apparent to a human observer.Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine natural ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional C-space. We illustrate the utility of our methods on difficult problems involving complex 3D CAD Models.     

Computing collision-free motions for a team of robots using formation and non-holonomic constraints

This paper focuses on the collision-free motion of a team of robots moving in a 2D environment with formation and non-holonomic constraints. With the proposed approach one can simultaneously control the formation of the team and generate a safe path for each individual robot. The computed paths satisfy the non-holonomic constraints, avoid collisions, and minimize the task-completion time. The proposed approach, which combines techniques from mathematical programming and CAD, consists of two main steps: first, a global team path is computed and, second, individual motions are determined for each unit. The effectiveness of the proposed approach is demonstrated using several simulation experiments.     

机器人力装配操作的顺应综合与实验验证

本文提出并实现了一种机器人装配顺应综合方法．装配操作过程可以表示为一列离散 的几何接触状态的改变和转移．通过对不确定性下所有机器人形位进行仿真,得到每一接触 形式对应的静态接触力特征．由对基本接触元的分析,求解实现接触状态转移的机器人运动 方向,构成顺应的力 错误修复运动方向映射,为规划的装配运动实现提供顺应．并在PUMA 562机器人上完成了一类简单装配实验对本文的方法进行了验证．     

A Human Aware Mobile Robot Motion Planner

Robot navigation in the presence of humans raises new issues for motion planning and control when the humans must be taken explicitly into account. We claim that a human aware motion planner (HAMP) must not only provide safe robot paths, but also synthesize good, socially acceptable and legible paths. This paper focuses on a motion planner that takes explicitly into account its human partners by reasoning about their accessibility, their vision field and their preferences in terms of relative human-robot placement and motions in realistic environments. This planner is part of a human-aware motion and manipulation planning and control system that we aim to develop in order to achieve motion and manipulation tasks in the presence or in synergy with humans.     

Evolutionary path planning for robot assisted part handling in sheet metal bending

While the bending sequence planning has been intensively studied, design of the motion path of a sheet metal part in the bending operation tends to be ignored by researchers. Because during the bending operation, the space for maneuvering a sheet metal part is very small, collisions between the part and bending tools are likely to occur. When a robot is used to handle the part, the role of an automatic path-planning tool becomes more significant. In this study, an evolutionary path-planning approach for robot-assisted handling of sheet metal parts in bending is firstly proposed and implemented. The proposed approach globally searches the motion path space to identify feasible paths. Collision detection algorithms based on segment intersection are used to check if the generated paths are feasible or infeasible. This method can automatically design feasible handling operations for a robot. Simulation examples on a simple “V” shaped part and a part with multiple bents demonstrate that the approach is efficient and practical.     

Path Planning for Improved Visibility Using a Probabilistic Road Map

This paper focuses on the challenges of vision-based motion planning for industrial manipulators. Our approach is aimed at planning paths that are within the sensing and actuation limits of industrial hardware and software. Building on recent advances in path planning, our planner augments probabilistic road maps with vision-based constraints. The resulting planner finds collision-free paths that simultaneously avoid occlusions of an image target and keep the target within the field of view of the camera. The planner can be applied to eye-in-hand visual-target-tracking tasks for manipulators that use point-to-point commands with interpolated joint motion.      

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

Two-handed assembly with immersive task planning in virtual reality

Assembly modelling is the process of capturing entities and activity information related to assembling and assembly. Currently, most CAD systems have been developed to ease the design of individual components, but are limited in their support for assembly designs and planning capability, which are crucial for reducing the cost and processing time in complex design, constraint analysis and assembly task planning. This paper presents a framework of a two-handed virtual assembly (VA) planner for assembly tasks, which coordinates two hands jointly for feature-based manipulation, assembly analysis and constraint-based task planning. Feature-based manipulation highlights the important assembling features (e.g. dynamic reference frames, moving arrow, mating features) to guide users for the ease of assembly and in an efficient and fluid manner. The users can freely navigate and move the mating pair along the collision-free path. The free motion of two-handed input in assembly is further restricted to the allowable motion guided by the constraints recognised on-line. The allowable motion in assembly is planned by the logic steps derived from the analysis of constraints and their translation in the progress of assembly. No preprocessing or predefined assembly sequence is necessary since the planning is produced in real-time upon the two-handed interactions. Mating features and constraints in databases are automatically updated after each assembly to simplify the planning process. The two-handed task planner has been developed and experimented for several assembly examples including a drill (12-parts) and a robot (17-parts). The system can be generally applied for the interactive task planning of assembly-type applications.     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

Physiological and subjective evaluation of a human-robot object hand-over task

In the context of task sharing between a robot companion and its human partners, the notions of safe and compliant hardware are not enough. It is necessary to guarantee ergonomic robot motions. Therefore, we have developed Human Aware Manipulation Planner (Sisbot et al., 2010), a motion planner specifically designed for human–robot object transfer by explicitly taking into account the legibility, the safety and the physical comfort of robot motions. The main objective of this research was to define precise subjective metrics to assess our planner when a human interacts with a robot in an object hand-over task. A second objective was to obtain quantitative data to evaluate the effect of this interaction. Given the short duration, the “relative ease” of the object hand-over task and its qualitative component, classical behavioral measures based on accuracy or reaction time were unsuitable to compare our gestures. In this perspective, we selected three measurements based on the galvanic skin conductance response, the deltoid muscle activity and the ocular activity. To test our assumptions and validate our planner, an experimental set-up involving Jido, a mobile manipulator robot, and a seated human was proposed. For the purpose of the experiment, we have defined three motions that combine different levels of legibility, safety and physical comfort values. After each robot gesture the participants were asked to rate them on a three dimensional subjective scale. It has appeared that the subjective data were in favor of our reference motion. Eventually the three motions elicited different physiological and ocular responses that could be used to partially discriminate them.     

Multirobot motion coordination in space and time

This paper describes a solution to the multirobot motion planning problem based on a decoupled analysis in the space domain and in the time domain. It investigates the practical use of the notion of motion plan quality and of the motion plan robustness measures for computing safe motions. The use of anytime algorithms allows one to evaluate the opportunity of looking for alternative solution paths by generating small variations of robot motions affecting both its geometrical path and its scheduled velocity. By using the concept of plan robustness, several alternative paths are generated and evaluated through various performance indices and impact factors, using heuristic rules. These indices allow one to know how much a variation affects a given plan. Finally, some recent experiments are outlined.     

A simple algorithm for intelligent manipulator collision-free motion

The collision-free planning of motion is a fundamental problem for artificial intelligence applications in robotics. The ability to compute a continuous safe path for a robot in a given environment will make possible the development of task-level robot planning systems so that the implementation details and the particular robot motion sequence will be ignored by the programmer.A new approach to planning collision-free motions for general real-life six degrees of freedom (d.o.f.) manipulators is presented. It is based on a simple object model previously developed. The complexity of the general collision detection problem is reduced, and realistic collision-free paths are efficiently found onCS planes. A heuristic evaluation function with a real physical sense is introduced, and computational cost is reduced to the strictly necessary by selecting the most adequate level of representation. A general algorithm is defined for 6 d.o.f. robots that yields good results for actual robot models with complex design structures with the aid of various heuristic techniques. The problem of adaptive motion is also considered.     

小型断路器柔性装配中视觉识别系统的设计与应用

现代制造业对小型断路器（MCB）生产过程的效率和精度要求都在不断提高,传统的人工装配效率低且装配质量参差不齐,而传统基于振动盘上料的自动装配技术限制了制造的柔性化水平。针对上述问题以及未来的市场需求,提出了一种基于机器视觉的小型断路器柔性装配系统,该系统搭建专用的视觉识别模块,通过VGG-16架构的深度学习分类器和特征模板匹配方法,对小型断路器零件的种类、位置坐标、当前姿态进行识别,并将识别结果发送给工业机器人控制器,指导工业机器人对不同型号产品的不同零件类型通过机器人夹爪的灵活切换来完成不同的装配任务。实验表明,该系统对零件种类识别准确率为99.8%,坐标偏差在±0.3mm以内,旋转角度偏差在±0.8°以内,达到了MCB装配的精度要求,符合柔性化制造的需求。     

Planning the motions of a mobile robot in a sensory uncertaintyfield

Failures in mobile robot navigation are often caused by errors in localizing the robot relative to its environment. This paper explores the idea that these errors can be considerably reduced by planning paths taking the robot through positions where pertinent features of the environment can be sensed. It introduces the notion of a “sensory uncertainty field” (SUF). For every possible robot configuration q, this field estimates the distribution of possible errors in the robot configuration that would be computed by a localization function matching the data given by the sensors against an environment model, if the robot was at q. A planner is proposed which uses a precomputed SUF to generate paths that minimize expected errors or any other criterion combining, say, path length and errors. This paper describes in detail the computation of a specific SUF for a mobile robot equipped with a classical line-striping camera/laser range sensor. It presents an implemented SUF-based motion planner for this robot and shows paths generated by this planner. Navigation experiments were conducted with mobile robots using paths generated by the SUF-based planner and other paths. The former paths were tracked with greater precision than the others. The final section of the paper discusses additional research issues related to SUF-based planning     

Fuzzy Logic Based Behavior Fusion for Navigation of an Intelligent Mobile Robot

This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments.Using behavior fusion by fuzzy logic,a mobile robot is able to directly execute its motion according to range information about environments,acquired by ultrasonic sensors,without the need for trajectory planning.Based on low-level behavior control,an efficient strategy for integrating high-level global planning for robot motion can be formulated,since,in most applications,some information on environments is prior knowledge.A global planner,therefore,only to generate some subgoal positions rather than exact geometric paths.Because such subgoals can be easily removed from or added into the plannes,this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments.Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.