Neural network approach to trajectory synthesis for robotic manipulators

The paper deals with the collision free trajectory synthesis for industrial robotic manipulators. A new efficient method is proposed that is based on a neural network collision model. The developed iterative transformation procedure provides small computing times for the C-space synthesis and yields sufficiently precise configuration space map for the manipulators with many degrees of freedom. A topologically ordered neural network model is proposed to find the path in the configuration space. The stability of this model is proved using the Lyapunov function technique. To generate the collision model, a modification of the Radial Basis Function Network (RBFN) is used. The developed technique is illustrated by an application example of designing a robotic manufacturing cell for the automotive industry.     

Automatic work objects calibration via a global–local camera system

In a human–robot collaborative manufacturing application where a work object can be placed in an arbitrary position, there is a need to calibrate the actual position of the work object. This paper presents an approach for automatic work-object calibration in flexible robotic systems. The approach consists of two modules: a global positioning module based on fixed cameras mounted around robotic workspace, and a local positioning module based on the camera mounted on the robot arm. The aim of the global positioning is to detect the work object in the working area and roughly estimate its position, whereas the local positioning is to define an object frame according to the 3D position and orientation of the work object with higher accuracy. For object detection and localization, coded visual markers are utilized. For each object, several markers are used to increase the robustness and accuracy of the localization and calibration procedure. This approach can be used in robotic welding or assembly applications.     

A path conditioning method with trap avoidance

This work presents a sliding-mode method for robotic path conditioning. The proposal includes a trap avoidance algorithm in order to escape from trap situations, which are analogous to local minima in potential field-based approaches. The sliding-mode algorithm activates when the desired path is about to violate the robot workspace constraints, modifying it as much as necessary in order to fulfill all the constraints and reaching their limit surface at low speed. The proposed path conditioning algorithm can be used on-line, since it does not require a priori knowledge of the desired path, and improves the conventional conservative potential field-based approach in the sense that it fully exploits the robot workspace. The proposed approach can be easily added as an auxiliary supervisory loop to conventional robotic planning algorithms and its implementation is very easy in a few program lines of a microprocessor. The proposed path conditioning is compared through simulation with the conventional potential field-based approach in order to show the benefits of the method. Moreover, the effectiveness of the proposed trap avoidance algorithm is evaluated by simulation for various trap situations.     

An open-source multi-DOF articulated robotic educational platform for autonomous object manipulation

This research presents an autonomous robotic framework for academic, vocational and training purpose. The platform is centred on a 6 Degree Of Freedom (DOF) serial robotic arm. The kinematic and dynamic models of the robot have been derived to facilitate controller design. An on-board camera to scan the arm workspace permits autonomous applications development. The sensory system consists of position feedback from each joint of the robot and a force sensor mounted at the arm gripper. External devices can be interfaced with the platform through digital and analog I/O ports of the robot controller. To enhance the learning outcome for beginners, higher level commands have been provided. Advanced users can tailor the platform by exploiting the open-source custom-developed hardware and software architectures. The efficacy of the proposed platform has been demonstrated by implementing two experiments; autonomous sorting of objects and controller design. The proposed platform finds its potential to teach technical courses (like Robotics, Control, Electronics, Image-processing and Computer vision) and to implement and validate advanced algorithms for object manipulation and grasping, trajectory generation, path planning, etc. It can also be employed in an industrial environment to test various strategies prior to their execution on actual manipulators.     

CAD-based generation of collision-free paths for robotic manipulators

The use of a CAD system can improve the overall efficiency of automated factories. This paper describes the utilization of a CAD system devoted to the off-line generation of collision-free paths for robotic manipulators. This tool is first applied in the case of a robotic manipulator in a moderately cluttered environment, next it is utilized it for planning collision-free paths for two robotic manipulators sharing a common workspace. The implemented off-line programming system is written in C, in X/Windows environment. In this way portability is effectively achieved. Simulation results show the effectiveness of the proposed approach.     

Path planning for a manipulator with constraints on orientation

This paper discusses the work undertaken at the U.K. National Advanced Robotics Research Centre in the area of path planning. Experiments have been undertaken with the Best First Planner (BFP) and Random Path Planner (RPP) algorithms developed at Stanford University by Barraquand and Latombe. These look particularly suitable for solving a wide range of motion planning problems and for providing the basic mechanism for path planning in an advanced robotic architecture. From this basic mechanism higher level functional primitives have been developed to enable paths to be planned with a pre-defined orientation of the end effector at the goal location or maintaining an orientation throughout the whole path. These functions are achieved through placing additional constraints on the search through configuration space and modifying the workspace potential.     

基于体液免疫的机器人路径规划免疫算法

依据生物免疫中体液免疫原理,提出一种新的移动机器人路径规划免疫算法。针对机器人在已知环境下的运行特性,对其设计路径光滑性指标及依据神经网络设计碰撞行为评价指标,将路径规划问题转化为非约束优化,进而结合体液免疫应答机制,获得路径规划新方法。该算法的特点是具有并行及快速搜索的能力,对机器人初始路径无任何约束。对各种难易不同的初始和目标位置,均能保证获无碰最优路径。仿真事例获知此算法的有效性。     

Robot-based computer-integrated manufacturing as applied in manufacturing automation

In this paper, a study of robot-based computer-integrated manufacturing (CIM) automation is presented. In the study, we investigated issues regarding robotic workspace utilization, accuracy, repeatability and statistical considerations of fixture design that are relevant to the CIM system integration. Taguchi Methods are used to conduct parameter design to improve the accuracy of the Cartesian positions of the end-effector within the robot workspace, subject to spatial considerations of the manufacturing process and required boundary conditions. A SCARA robot with known tolerances of link length and joint angles is used to illustrate the methodology. We find the region within the workspace of the robot which gives rise to better accuracy using the S/N ratios of the Taguchi Methods. The results are compared with theoretical development and show very good agreement. In addition, the design of fixture is investigated with both analytical and statistical approaches. Reliability is defined to facilitate the fixture design. The robot-based CIM system encompasses equipment including a SCARA robot, a spatial robot, a CNC milling machine, a conveyor belt and programmable logic controllers (PLC). The system follows a typical factory floor set-up to demonstrate the ability of robotic applications in manufacturing automation. The CIM system utilizes a graphics user interface (GUI) within a message-based, object-oriented programming environment and is also packaged with interactive multimedia to facilitate the dissemination of research results.     

A neural network approach to complete coverage path planning.

Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.     

Dynamics of Nanostructured Origami

The nanostructured origami approach to 3-D nano manufacturing is a novel way to gain functionality from the third dimension in nanotechnology applications. After first patterning devices onto a structural membrane using traditional 2-D tools, the segments can be folded along predefined creases in order to realize a final shape in 3-D. In order to manufacture increasingly complex devices, knowledge of the origami's dynamics is imperative. This paper describes a method to model the dynamics of two types of origamis using methods that were originally developed for use in robotic manipulation tasks. The stability of the devices in the folded state is also discussed.     

Uncalibrated Eye-to-Hand Visual Servoing Using Inverse Fuzzy Models

A new uncalibrated eye-to-hand visual servoing based on inverse fuzzy modeling is proposed in this paper. In classical visual servoing, the Jacobian plays a decisive role in the convergence of the controller, as its analytical model depends on the selected image features. This Jacobian must also be inverted online. Fuzzy modeling is applied to obtain an inverse model of the mapping between image feature variations and joint velocities. This approach is independent from the robot's kinematic model or camera calibration and also avoids the necessity of inverting the Jacobian online. An inverse model is identified for the robot workspace, using measurement data of a robotic manipulator. This inverse model is directly used as a controller. The inverse fuzzy control scheme is applied to a robotic manipulator performing visual servoing for random positioning in the robot workspace. The obtained experimental results show the effectiveness of the proposed control scheme. The fuzzy controller can position the robotic manipulator at any point in the workspace with better accuracy than the classic visual servoing approach.     

Coordinated path tracking of two vision-guided tractors for heavy-duty robotic vehicles

Heavy-duty robotic vehicles are increasingly required to transport and position large-quantity and large-scale objects in the manufacturing process. The load-carrying capacity of vehicles can be enhanced by configuring multiple automated guided tractors and coordinating their motions. A vision-guided tractor is developed by using an on-board camera in order to improve the guidance accuracy. Each tractor can recognize the guide paths, measure its path deviations and control the speeds of driving motors independently. A coordinated path tracking technique is proposed for two vision-guided tractors of a robotic vehicle, in order to make them move along a guide path accurately and smoothly. The finite area of the vision field, the actuation capacity of driving motors and the motion conflict of two tractors are considered as control constraints of path tracking. Six path deviation states and their relevant approaching trajectories are classified based on deviation properties and control constraints. The general law of mutual conversion of six approaching trajectories is analyzed. Other approaching trajectories should be converted into the tangent-arc trajectory that can eliminate two path deviations synchronously. Expected tracking distance is a coupled parameter that influences control efficiency, safety margin of trajectory conversion and coordination degree of two tractors. A fuzzy logic regulator is used in the leader-follower control strategy to adjust this parameter, by taking two path deviations and the difference of attitude angles of two tractors into account. Numerical simulation and prototype experiment show that two vision-guided tractors can move along the straight and curvilinear guide paths with high tracking accuracy, control efficiency and motion coordination, which enhanced the load-carrying capacity of robotic vehicles significantly.     

An Information Roadmap Method for Robotic Sensor Path Planning

A new probabilistic roadmap method is presented for planning the path of a robotic sensor deployed in order to classify multiple fixed targets located in an obstacle-populated workspace. Existing roadmap methods have been successful at planning a robot path for the purpose of moving from an initial to a final configuration in a workspace by a minimum distance. But they are not directly applicable to robots whose primary objective is to gather target information with an on-board sensor. In this paper, a novel information roadmap method is developed in which obstacles, targets, sensor’s platform and field-of-view are represented as closed and bounded subsets of an Euclidean workspace. The information roadmap is sampled from a normalized information theoretic function that favors samples with a high expected value of information in configuration space. The method is applied to a landmine classification problem to plan the path of a robotic ground-penetrating radar, based on prior remote measurements and other geospatial data. Experiments show that paths obtained from the information roadmap exhibit a classification efficiency several times higher than that of existing search strategies. Also, the information roadmap can be used to deploy non-overpass capable robots that must avoid targets as well as obstacles.     

An algorithm for safe navigation of mobile robots by a sensor network in dynamic cluttered industrial environments

Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free navigation in dynamic environments. In this paper, we propose a sensor network based navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based navigation system with a novel artificial potential field (APF) based navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.     

无人飞艇的基于计算机视觉导航和预设航线跟踪控制

小型无人飞艇在反恐防暴、灾难监控、大气监测、广告、航拍、交通监控、应急通信中继平台等很多方面都有广阔的应用前景.这些应用都要求飞艇具有自动跟踪预设航线的能力.因此，本文提出了基于计算机视觉导航和优化模糊控制的策略来实现预设航线的自动跟踪.文中首先详细介绍了基于自然标志的视觉导航原理，无人飞艇机载视觉系统通过跟踪这些特征点——易在图像处理中识别的自然标志，例如城市中的高层建筑物，再结合数字地图或GIS，获取它们的位置和几何特征信息，利用针孔模型成像的几何关系解算出飞艇的位置和方向；接着根据无人飞艇的动力学特性，设计了以视觉导航获取的飞艇位置和方向信息作为输入的模糊飞控系统，并用GA算法优化了模糊控制器的成员关系函数.最后进行了验证和分析.     

Information-Driven Sensor Path Planning by Approximate Cell Decomposition

A methodology is developed for planning the sensing strategy of a robotic sensor deployed for the purpose of classifying multiple fixed targets located in an obstacle-populated workspace. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements using a bounded field of view (FOV). This paper develops a novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace. The method constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree from which an optimal sensing strategy can be computed. The effectiveness of the optimal sensing strategies obtained by this methodology is demonstrated through a mine-hunting application. Numerical experiments show that these strategies outperform shortest path, complete coverage, random, and grid search strategies, and are applicable to nonoverpass capable robots that must avoid targets as well as obstacles.     

Optimization-based path planning framework for industrial manufacturing processes with complex continuous paths

The complexity of robotic path planning problems in industrial manufacturing increases significantly with the current trends of product individualization and flexible production systems. In many industrial processes, a robotic tool has to follow a desired manufacturing path most accurately, while certain deviations, also called process tolerances and process windows, are allowed. In this work, a path planning framework is proposed, which systematically incorporates all process degrees of freedom (DoF), tolerances and redundant DoF of the considered manufacturing process as well as collision avoidance. Based on the specified process DoF and tolerances, the objective function and the hard and soft constraints of the underlying optimization problem can be easily parametrized to find the optimal joint-space path. By providing the analytical gradients of the objective function and the constraints and utilizing modern multi-core CPUs, the computation performance can be significantly improved. The proposed path planning framework is demonstrated for an experimental drawing process and a simulated spraying process. The planner is able to solve complex planning tasks of continuous manufacturing paths by systematically exploiting the process DoF and tolerances while allowing to move through singular configurations, which leads to solutions that cannot be found by state-of-the-art concepts.     

Workspace Generation for Multifingered Manipulation

In this paper, we propose a novel numerical approach and algorithm to compute and visualize the workspace of a multifingered hand manipulating an object. Based on feasibility analysis of grasps, the proposed approach uses an optimization technique to first compute discretely the position boundary of the grasped object and then calculate the rotation ranges of the object at specified positions within the boundary. In other words, workspace generation with the approach is fulfilled by obtaining reachable boundaries of the grasped object in the sense of both position and orientation, and the discrete boundary points are computed by a series of optimization models. Unlike in workspace generation of other robotic systems where only geometric and kinematic parameters of the robots are considered, all factors including geometric, kinematic and force-related factors that affect the workspace of a hand–object system can be taken into account in our approach to generate the workspace of multifingered manipulation. Since various constraints can be integrated into the optimization models, our method is general and complete, with adaptability to various grasps and manipulations. Workspace generation with the approach in both planar and spatial cases are illustrated with examples. The approach provides an effective and general solution to the long-term open and challenging problem of workspace generation of multifingered manipulation. Part of the work has been published in the Proceedings of IEEE/RSJ IROS2008 and IEEE/ASME AIM2008.     

ROBOT PATH PLANNING BASED ON MODIFIED GREY RELATIONAL ANALYSIS

In this paper, a simple searching approach integrated the nonlinear programming problem and the modified grey relational analysis is proposed to find the near-optimal and collision-free path for a robot, from an initial position to a goal position, in which the robot can be acted in the known or unknown workspace with multiple circular obstacles. The proposed find-path procedure consists of at least one trial. Each trial includes three main stages, i.e., a forward search stage, a backward search stage, and an inference stage. After all trials are completed, a decision-making stage is introduced to determine the near-optimal and collision-free path which is guaranteed to reach the goal. Besides, the presented method is applicable for the on-line applications and, furthermore, can solve the local minimal problems. Simulation results for circular obstacles demonstrate the performances of the proposed approach and its potential as an on-line path planner.