Controlling the Execution of a Visual Servoing Task

This paper presents the application of the Visual Servoing approach to a mobile robot which must execute coordinate motions in a known indoor environment. In this work, we are interested in the execution and control of basic motions like Go to an object by using the mobile robot H^il are2Bis. We use a diagonal matrix for the gain to improve the visual servoing behaviour and the potential field formalism to avoid obstacles. Namely, the robot is controlled according to the position of some features in an image. Such a path will be executed by a nonholonomic mobile robot, which has only two degrees of freedom (two wheels), and three configuration parameters (X Y ); a camera is mounted on the robot close to the end effector of an arm, controlled to add at least a new degree of freedom (_pl).     

Real-Time Collision-Free Path Planning for Robots in Configuration Space

Collision-free path planning for an industrial robot in configuration space requires mapping obstacles from robot‘s workspace into its configuration space.In this paper,an approach to real-time collision-free path planning for robots in configuration space is presented.Obstacle mapping is carried out by fundamental obstacles defined in the workspace and their images in the configuration space.In order to avoid dealing with unimportant parts of the configuration space that do not affect searching a collision-free path between starting and goal configurations,we construct a free subspace by slice configuration obstacles.In this free subspace,the collision-free path is determined by the A^* algorithm.Finally,graphical simulations show the effectiveness of the proposed approach.     

Distributed real-time control of a spatial robot juggler

The Yale spatial juggler and an emerging set of working principles for the design and implementation of embedded real-time distributed controllers are described. The robot uses a distributed network of transputers to process stereo camera data and control the torque of a three-degree-of-freedom arm to juggle a ball. The juggling algorithm is a direct extension of a novel class of nonlinear feedback controllers, called mirror laws. The algorithm takes the form of a mathematical expression that specifies robot position as a function of the ball's position and velocity. The programming approach, called geometric programming, substitutes event-driven dynamical processes and geometrical transformations for a more syntactically oriented if-then-else approach     

Programming for modular reconfigurable robots

Composed of multiple modular robotic units, self-reconfigurable modular robots are metamorphic systems that can autonomously rearrange the modules and form different configurations depending on dynamic environments and tasks. The goal of self-reconfiguration is to determine how to change connectivity of modules to transform the robot from the current configuration to the goal configuration subject to restrictions of physical implementation. The existing reconfiguration algorithms use different methods, such as divide-and-conquer, graph matching, and the like, to reduce the reconfiguration cost. However, an optimal solution with a minimal number of reconfiguration steps has not been found yet. The optimal reconfiguration planning problem consists in finding the least number of reconfiguration steps transforming the robot from one configuration to another. This is an NP-complete problem. In this paper, we describe an approach to solve this problem. The approach is based on constructing logical models of the problem under study.     

Programming mechanical simulations

This paper examines the control of complex physical objects in simulation. We introduce a programming paradigm that allows a simulation to be treated as a multi-level constraint solver. The control programmer is given the ability to specify constraints on the controlled response of mechanisms and to conditionally change these constraints dependent on the state of system. The approach facilitates the development of model-based, event-driven control programs. The usefulness of the paradigm is demonstrated through the simulation of a hopping robot.     

Performance modeling and analysis of message-oriented event-driven systems

Message-oriented event-driven systems are becoming increasingly ubiquitous in many industry domains including telecommunications, transportation and supply chain management. Applications in these areas typically have stringent requirements for performance and scalability. To guarantee adequate quality-of-service, systems must be subjected to a rigorous performance and scalability analysis before they are put into production. In this paper, we present a comprehensive modeling methodology for message-oriented event-driven systems in the context of a case study of a representative application in the supply chain management domain. The methodology, which is based on queueing Petri nets, provides a basis for performance analysis and capacity planning. We study a deployment of the SPECjms2007 standard benchmark on a leading commercial middleware platform. A detailed system model is built in a step-by-step fashion and then used to predict the system performance under various workload and configuration scenarios. After the case study, we present a set of generic performance modeling patterns that can be used as building blocks when modeling message-oriented event-driven systems. The results demonstrate the effectiveness, practicality and accuracy of the proposed modeling and prediction approach.     

An efficient local approach for the path generation of robot manipulators

In this article an efficient local approach for the path generation of robot manipulators is presented. The approach is based on formulating a simple nonlinear programming problem. This problem is considered as a minimization of energy with given robot kinematics and subject to the robot requirements and a singularities avoidance constraint. From this formulation a closed form solution is derived which has the properties that allows to pursue both singularities and obstacle avoidance simultaneously; and that it can incorporate global information. These properties enable the accomplishment of the important task that while a specified trajectory in the operational space can be closely followed, also a desired joint configuration can be attained accurately at a given time. Although the proposed approach is primarily developed for redundant manipulators, its application to nonredundant manipulators is examplified by considering a particular commercial manipulator.     

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach

This paper proposes an intelligent task planning and action selection mechanism for a mobile robot in a robot soccer system through a fuzzy neural network approach. The proposed fuzzy neural network system is developed through the two dimensional fuzzification of the soccer field. A five layer fuzzy neural network system is trained through error back propagation learning algorithm to impart a strategy based action selection. The action selection depends on the field configuration, and the emergence of a particular field configuration results from the game dynamics. Strategy of the robot changes when the configuration of the objects in the field changes. The proposed fuzzy neural network structure is flexible to accommodate all possible filed configurations. Simulation results indicate that the proposed approach is simple and has the capability in coordinating the multi-agent system through selection of sensible actions.     

A Constraint-Based Robotic Soccer Team

It is a challenging task for a team of multiple fast-moving robots to cooperate with each other and to compete with another team in a dynamic, real-time environment. For a robot team to play soccer successfully, various technologies have to be incorporated including robotic architecture, multi-agent collaboration and real-time reasoning. A robot is an integrated system, with a controller embedded in its plant. A robotic system is the coupling of a robot to its environment. Robotic systems are, in general, hybrid dynamic systems, consisting of continuous, discrete and event-driven components. Constraint Nets (CN) provide a semantic model for modeling hybrid dynamic systems. Controllers are embedded constraint solvers that solve constraints in real-time. A controller for our robot soccer team, UBC Dynamo98, has been modeled in CN, and implemented in Java, using the Java Beans architecture. A coach program using an evolutionary algorithm has also been designed and implemented to adjust the weights of the constraints and other parameters in the controller. The results demonstrate that the formal CN approach is a practical tool for designing and implementing controllers for robots in multi-agent real-time environments. They also demonstrate the effectiveness of applying the evolutionary algorithm to the CN-modeled controllers.     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

Roadmap-based motion planning in dynamic environments

In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.     

利用事件和期限驱动对机器人延时的优化

随着科技的进步,机器人领域得到了飞速发展,为了更好地让机器人适应复杂工作环境,需要进一步提高机器人的感知性能。大多数机器人采用视觉作为感知手段。但由于图像中包含大量数据以及处理这些数据需要花费大量时间,导致了机器人有显著的延时,从而导致机器人性能的下降。因此,为了解决这一问题,提出了一种基于期限驱动和事件驱动控制方法,该方法的核心是把基于模型的控制设计方法的思想应用到基于视觉的自定位算法的机器人运动控制中。同时考虑了一种简单的基于随机样本一致性的定位算法的延时情况。实验结果证明,提出的期限驱动和事件驱动控制设计明显优于传统的周期控制。     

Path planning for a quadruped robot: an artificial field approach

In this paper, we consider the problem of planning a feasible path for a quadruped walking robot in an environment of obstacles. In conventional path-planning problems, the main focus is merely collision avoidance with obstacles since a wheeled robot is involved. However, in the case of a legged robot, both collision avoidance and crossing over obstacles must be taken into account in the process of path planning. Furthermore, the constraints of the gait should be considered to guarantee the feasibility of a planned path. To resolve this complicated problem in a systematic way, a new concept of an artificial thermal field is proposed. Specifically, with the assumption that a robot walks with a periodic crab gait, a robot and obstacles in a three-dimensional (3D) space are projected on a 2D plane. Next, the 2D obstacles are transformed into the configuration space of a quadruped robot. A feasible path is finally sought in an artificial thermal field which is constructed numerically on the discretized configuration space. To verify the efficacy of the proposed approach, three notable simulation results are provided.     

Image-Based Visual Servoing for Nonholonomic Mobile Robots Using Epipolar Geometry

We present an image-based visual servoing strategy for driving a nonholonomic mobile robot equipped with a pinhole camera toward a desired configuration. The proposed approach, which exploits the epipolar geometry defined by the current and desired camera views, does not need any knowledge of the 3-D scene geometry. The control scheme is divided into two steps. In the first, using an approximate input-output linearizing feedback, the epipoles are zeroed so as to align the robot with the goal. Feature points are then used in the second translational step to reach the desired configuration. Asymptotic convergence to the desired configuration is proven, both in the calibrated and partially calibrated case. Simulation and experimental results show the effectiveness of the proposed control scheme     

Network-based reconfiguration routes for a self-reconfigurable robot

This paper presents a network-based analysis approach for the reconfiguration problem of a self-reconfigurable robot. The self-reconfigurable modular robot named ＂AMOEBA-I＂ has nine kinds of non-isomorphic configurations that consist of a configuration network. Each configuration of the robot is defined to be a node in the weighted and directed configuration network. The transformation from one configuration to another is represented by a directed path with nonnegative weight. Graph theory is applied in the reconfiguration analysis, where reconfiguration route, reconfigurable matrix and route matrix are defined according to the topological information of these configurations. Algorithms in graph theory have been used in enumerating the available reconfiguration routes and deciding the best reconfiguration route. Numerical analysis and experimental simulation results prove the validity of the approach proposed in this paper. And it is potentially suitable for other self-reconfigurable robots＇ configuration control and reconfiguration planning.     

Obstacle Modelling Oriented to Safe Motion Planning and Control for Planar Rigid Robot Manipulators

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly challenging when obstacles are present in the manipulator workspace. In this paper a n-joint planar robot manipulator is considered and it is assumed that obstacles located in its workspace can be approximated in a conservative way with circles. The goal is to represent the obstacles in the robot configuration space. The representation allows to obtain an efficient and accurate trajectory planning and tracking. A simple but effective path planning strategy is proposed in the paper. Since path planning depends on tracking accuracy, in this paper an adequate tracking accuracy is guaranteed by means of a suitably designed Second Order Sliding Mode Controller (SOSMC). The proposed approach guarantees a collision-free motion of the manipulator in its workspace in spite of the presence of obstacles, as confirmed by experimental results.     

A path planning algorithm for industrial robots

Instead of using the tedious process of robot teaching, an off-line path planning algorithm has been developed for industrial robots to improve their accuracy and efficiency. Collision avoidance is the primary concept to achieve such goal. By use of the distance maps, the inspection of obstacle collision is completed and transformed to the configuration space in terms of the robot joint angles. On this configuration map, the relation between the obstacles and the robot arms is obvious. By checking the interference conditions, the collision points are indicated with marks and collected into the database. The path planning is obtained based on the assigned marked number of the passable region via wave expansion method. Depth-first search method is another approach to obtain minimum sequences to pass through. The proposed algorithm is experimented on a 6-DOF industrial robot. From the simulation results, not only the algorithm can achieve the goal of collision avoidance, but also save the manipulation steps.     

Simultaneous Parameter Calibration,Localization, and Mapping

Abstract

The calibration parameters of a mobile robot play a substantial role in navigation tasks. Often these parameters are subject to variations that depend either on changes in the environment or on the load of the robot. In this paper, we propose an approach to simultaneously estimate a map of the environment, the position of the on-board sensors of the robot, and its kinematic parameters. Our method requires no prior knowledge about the environment and relies only on a rough initial guess of the parameters of the platform. The proposed approach estimates the parameters online and it is able to adapt to non-stationary changes of the configuration. We tested our approach in simulated environments and on a wide range of real-world data using different types of robotic platforms.     

A Differentially Flat Open-Chain Space Robot with Arbitrarily Oriented Joint Axes and Two Momentum Wheels at the Base

The motion of a free-floating space robot is characterized by the principle of conservation of angular momentum. It is well known that these angular momentum equations are nonholonomic, i.e., are nonintegrable rate equations. If the base of the free-floating robot is partially actuated, it is difficult to determine joint trajectories that will result in point-to-point motion of the entire robot system in its configuration space. However, if the drift-less system associated with the angular momentum conservation equations is differentially flat, point-to-point maneuvers of the free-floating robot in its configuration space can be constructed by properly choosing trajectories in the differentially flat space. The primary advantages of this approach is that it avoids the use of nonlinear programming (NLP) to solve the nonintegrable rate equations, which at best can provide only approximate solutions. A currently open research problem is how to design a differentially flat space robot with under-actuated base. The contributions of this technical note are as follows: i) study systematically the structure of the nonholonomic rate constraint equations of a free-floating open-chain space robot with two momentum wheels at the base and arbitrarily oriented joint axes; ii) identify a set of sufficient conditions on the inertia distribution under which the system exhibits differential flatness; iii) exploit these design conditions for point-to-point trajectory planning and control of the space robot.