A decentralized kinematic control architecture for collaborative and cooperative multi-arm systems

In this paper a two-layer decentralized framework for kinematic control of cooperative and collaborative multi-robot systems is developed. The motion of the system is specified at the workpiece level, by adopting a task-oriented formulation for cooperative tasks. The first layer computes the motion of the single arms in the system. In detail, the control unit of each robot computes the end-effector motion references in a decentralized fashion on the basis of the knowledge of the assigned cooperative task and the motion references computed by its neighbors. Then, in the second layer, each control unit computes the reference joint motion of the corresponding manipulator from the end-effector reference motion. The approach is, then, tested in simulation on a work-cell composed by several manipulators, and experimentally on a dual-arm kinematically redundant work-cell composed by industrial manipulators.     

Six-DOF Impedance Control of Dual-Arm Cooperative Manipulators

In this paper, the problem of impedance control of dual-arm cooperative manipulators is studied. A general impedance control scheme is adopted, which encompasses a centralized impedance control strategy, aimed at conferring a compliant behavior at the object level, and a decentralized impedance control, enforced at the end-effector level, aimed at avoiding large internal loading of the object. Remarkably, the mechanical impedance behavior is defined in terms of geometrically consistent stiffness. The overall control scheme is based on a two-loop arrangement, where a simple proportional integral derivative inner motion loop is adopted for each manipulator, while an outer loop, using force and moment measurements at the robots wrists, is aimed at imposing the desired impedance behaviors. The developed control scheme is experimentally tested on a dual-arm setup composed of two 6-DOF industrial manipulators carrying a common object. The experimental investigation concerns the four different controller configurations that can be achieved by activating/deactivating the single impedance controllers.     

工作流系统中基于任务的RBAC控制模型研究

WFM（工作流模型）和RBAC（基于角色的访问控制）模型都包含角色空间和任务空间两个组件,在二者的基础上提出工作流框架下基于任务的访问控制模型（TRBAC）。分析了工作流中的任务空间和RBAC模型中的权限空间,在此基础上研究了任务空间的状态。解决了OA（办分自动化）系统层次空间上多个角色参与的协同工作流状态的判断和转移状况的判断。     

On max-min fair congestion control for multicast ABR service in ATM

In the ATM Forum activities, considerable efforts have focused on the congestion control of point-to-point available bit rate (ABR) service. We present a novel approach that extends existing point-to-point (unicast) congestion control protocols to a point-to-multipoint (multicast) environment. In particular, we establish a unified framework to derive a multicast congestion control protocol for an ABR service from a given rate-based unicast protocol. We generalize a known necessary and sufficient condition on the max-min fairness of unicast rate allocation for a multicast service. Using this condition, we show that the resulting multicast protocol derived using our framework preserves the fairness characteristics of the underlying unicast protocol. The practical significance of our approach is illustrated by extending a standard congestion control mechanism for an ABR service to a multicast environment. The performance of the resulting multicast protocol is examined using benchmark network configurations suggested by the traffic management subworking group at the ATM Forum, and simulation results are presented to substantiate our claims     

动态环境下的多UCAV协同任务分配研究

战场环境的动态性和不确定性以及协同控制的复杂性,使得预先制定的无人作战飞机任务分配方案不能满足动态战场环境中的实时性要求。考虑到无人作战飞机的差异、目标的差异及战场态势对目标分配的影响,建立了多无人作战飞机协同目标分配问题的数学模型。针对多无人作战飞机协同控制中的动态任务分配问题,提出一种改进的混合重分配策略,从任务层次上解决了多无人作战飞机的动态任务分配问题,在此基础上提出了混合细菌觅食算法,并用该算法在动态环境中为无人作战飞机进行合理的任务分配,通过仿真实验和分析表明混合重分配策略和混合细菌觅食算法的有效性。     

A modelling language for cooperative plans in highly dynamic domains

Cooperative behaviour is one of the challenges most pronounced in the RoboCup Middle Size League. Especially the dynamic nature of the domain, which calls for swift adaptation by each robot and the team as a whole, is a distinctive property of the league. The ability to establish highly responsive teamwork while facing unreliable communication and sensory noise is a key to successful soccer teams. Moreover, modelling such responsive, cooperative behaviour is difficult. In this work, we specify a novel model for cooperative behaviour geared towards highly dynamic domains, focussing on the language syntax and semantics. In our approach, agents estimate each other’s decision and correct these estimations once they receive contradictory information. We provide a comprehensive approach for agent teamwork featuring intuitive modelling capabilities for multi-agent activities, abstractions over activities and agents, and a clear operational semantics. Moreover, we briefly present a graphical modeling tool for cooperative strategies, which is based directly on the theory laid out, together with a practical framework for executing said strategies. We show experimentally the responsiveness and coherence of the resulting team play.     

Industrial applications of fuzzy logic at General Electric

Fuzzy logic control (FLC) technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. We illustrate some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, steam turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variables in a rolling mill stand. We compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit tradeoff criteria used to manage multiple control strategies. Finally, we describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering     

Improving retrieval framework using information gain models

Content-based image retrieval systems are meant to retrieve the most similar images of a collection to a query image. One of the most well-known models widely applied for this task is the bag of visual words (BoVW) model. In this paper, we introduce a study of different information gain models used for the construction of a visual vocabulary. In the proposed framework, information gain models are used as a discriminative information to index image features and select the ones that have the highest information gain values. We introduce some extensions to further improve the performance of the proposed framework: mixing different vocabularies and extending the BoVW to bag of visual phrases. Exhaustive experiments show the interest of information gain models on our retrieval framework.     

Goal-Orientation, Goal-Setting, and Goal-Driven Behavior in Minimalist User Instructions

This paper opens with a summary of minimalist design strategies that aim to optimize user instructions. Next, it discusses three research efforts to further improve these strategies. The common focus in these efforts is the attention to people's goal-related management and control of attention, time, and effort. First, a comprehensive framework for designing procedures - the four components model - is described. The design principles for the goal component focus on supporting the user's goal orientation and goal setting. Second, two experiments are reported that studied the problem of when it is best to present conceptual information. When instructions employed a learning-by-doing approach, users clearly preferred a work-flow mode of presentation. This mode optimally exploits the user's momentary interest in conceptual information during goal-driven task execution. The third research effort concentrates on user affect in instructions. The main idea is that motivation and emotion play a key role in task appraisals and corresponding actions. After discussing theories and design approaches, an experiment is discussed in which instructions were optimized for affect. Good results for perceived relevance and self-confidence were found in all conditions. Presence of an affect-oriented co-user did not enhance these effects. The paper concludes that the contribution of the efforts extends beyond the minimalist framework from which they originated.     

Visual servoing on unknown objects

We study visual servoing in a framework of detection and grasping of unknown objects. Classically, visual servoing has been used for applications where the object to be servoed on is known to the robot prior to the task execution. In addition, most of the methods concentrate on aligning the robot hand with the object without grasping it. In our work, visual servoing techniques are used as building blocks in a system capable of detecting and grasping unknown objects in natural scenes. We show how different visual servoing techniques facilitate a complete grasping cycle.     

Semi-supervised feature selection with exploiting shared information among multiple tasks

Given several related tasks, multi-task feature selection determines the importance of features by mining the correlations between them. There have already many efforts been made on the supervised multi-task feature selection. However, in real-world applications, it’s noticeably time-consuming and unpractical to collect sufficient labeled training data for each task. In this paper, we propose a novel feature selection algorithm, which integrates the semi-supervised learning and multi-task learning into a joint framework. Both the labeled and unlabeled samples are sufficiently utilized for each task, and the shared information between different tasks is simultaneously explored to facilitate decision making. Since the proposed objective function is non-smooth and difficult to be solved, we also design an efficient iterative algorithm to optimize it. Experimental results on different applications demonstrate the effectiveness of our algorithm.     

Optimal Cooperative Spectrum Sensing Under Faded and Bandwidth Limited Control Channels

Most of the cooperative spectrum sensing related research assumes system models with perfect control channel (i.e. with unlimited channel bandwidth and no channel errors). However, the assumption is not realistic and can lead to incorrect conclusions regarding the performance analysis of the cooperative spectrum sensing detection capabilities. This paper proposes a novel cooperative spectrum sensing framework that mitigates the imperfect control channel features, like the limited control channel bandwidth and error proneness, and achieves the detection performances of cooperative spectrum sensing under ideal control channel. It utilizes node clustering and multi-antenna spatial multiplexing (i.e. beamforming) and provides a generic framework that can be exploited by any cooperative spectrum sensing and fusion technique. The performance analysis shows that the proposed framework alleviates the control channel bandwidth limitation and significantly decreases the control channel error rate. The performance evaluation results also show that the proposed framework achieves the upper bound detection performances, i.e. achieves the same detection performances as the conventional cooperative spectrum sensing with ideal control channel.     

Run-time management of systems with partially reconfigurable FPGAs

Partial reconfiguration (PR) of FPGAs can be used to dynamically extend and adapt the functionality of computing systems by swapping in and out HW tasks. To coordinate the on-demand task execution, we propose and implement a Run-Time System Manager (RTSM) for scheduling software (SW) tasks on available processor(s) and hardware (HW) tasks on any number of reconfigurable regions (RRs) of a partially reconfigurable FPGA. Fed with the initial partitioning of the application into tasks, the corresponding task graph, and the available task mappings, the RTSM controls system operation considering the status of each task and region (e.g. busy, idle, scheduled for reconfiguration/execution, etc). Our RTSM supports task reuse and configuration prefetching to minimize reconfigurations, task movement among regions to efficiently manage the FPGA area, and region reservation for future reconfiguration and execution. We validate the correctness and portability of our RTSM executing an image processing application on two Xilinx-based platforms: ZedBoard and XUPV5. We also perform a more extensive evaluation of its features using a simulation framework, and find that – despite the technology limitations – our approach can give promising results in terms of scheduling quality. Since our RTSM supports also the scheduling of parallel SW tasks, we use it to manage the execution of the entire parallel Edge Detection application on a desktop; we compare the application execution time with that using the OpenMP framework and find that with our RTSM execution is 2.4 times faster than the unoptimized OpenMP version. When processor affinity optimization is enabled for OpenMP, our RTMS and the OpenMP are on par, indicating that the scheduling efficiency of our RTSM is competitive to this state-of-the-art scheduler, while supporting in addition the management of HW tasks.     

Utility‐optimal cross‐layer resource allocation in distributed wireless cooperative networks

In this paper, we propose a distributed cross‐layer resource allocation algorithm for wireless cooperative networks based on a network utility maximization framework. The algorithm provides solutions to relay selections, flow pass probabilities, transmit rate, and power levels jointly with optimal congestion control and power control through balancing link and physical layers such that the network‐wide utility is optimized. Via dual decomposition and subgradient method, we solve the utility‐optimal resource allocation problem by subproblems in different layers of the protocol stack. Furthermore, by introducing a concept of pseudochannel gain, we model both the primal direct logical link and its corresponding cooperative transmission link as a single virtual direct logical link to simplify our network utility framework. Eventually, the algorithm determines its primal resource allocation levels by employing reverse‐engineering of the pseudochannel gain model. Numerical experiments show that the convergence of the proposed algorithm can be obtained and the performance of the optimized network can be improved significantly. Copyright © 2012 John Wiley & Sons, Ltd.     

Efficient reporting node selection-based MAC protocol for wireless sensor networks

Wireless sensor networks rely on the cooperative effort of the densely deployed sensor nodes to report the detected events. As a result, sensor observations are highly correlated in the space domain. Typically, multiple sensor nodes may report the same event. Consequently, redundant information may be transmitted by the different sensor nodes, leading thus to unnecessary energy wastage. In this paper, we investigate the relationship between the spatial correlation and the number of reporting nodes by developing a new analytical model based on the theoretical framework of the CC-MAC (correlation-based collaborative medium access control) protocol (Vuran and Akyildiz in IEEE/ACM Trans Netw 14(2): 316–32912006). We show that the reporting task can be delegated to a small subset of sensor nodes without transgressing the distortion constraint. Building on this result, a simple spatial correlation medium access control protocol is then proposed to achieve further energy conservation and faster reporting latency than CC-MAC.     

Joint routing and scheduling optimization in arbitrary ad hoc networks: Comparison of cooperative and hop-by-hop forwarding

Cooperation schemes form a key aspect of infrastructure-less wireless networks that allow nodes that cannot directly communicate to exchange information through the help of intermediate nodes. The most widely adopted approach is based on hop-by-hop forwarding at the network layer along a path to destination. Cooperative relaying brings cooperation to the physical layer in order to fully exploit wireless resources. The concept exploits channel diversity by using multiple radio units to transmit the same message. The underlying fundamentals of cooperative relaying have been quite well-studied from a transmission efficiency point of view, in particular with a single pair of source and destination. Results of its performance gain in a multi-hop networking context with multiple sources and destinations are, however, less available. In this paper, we provide an optimization approach to assess the performance gain of cooperative relaying vis-a-vis conventional multi-hop forwarding under arbitrary network topology. The approach joint optimizes packet routing and transmission scheduling, and generalizes classical optimization schemes for non-cooperative networks. We provide numerical results demonstrating that the gain of cooperative relaying in networking scenarios is in general rather small and decreases when network connectivity and the number of traffic flows increase, due to interference and resource reuse limitations. In addition to quantifying the performance gain, our approach leads to a new framework for optimizing routing and scheduling in cooperative networks under a generalized Spacial Time Division Multiple Access (STDMA) scheme.     

Optimizing medical data quality based on multiagent web service framework

One of the most important issues in e-healthcare information systems is to optimize the medical data quality extracted from distributed and heterogeneous environments, which can extremely improve diagnostic and treatment decision making. This paper proposes a multiagent web service framework based on service-oriented architecture for the optimization of medical data quality in the e-healthcare information system. Based on the design of the multiagent web service framework, an evolutionary algorithm (EA) for the dynamic optimization of the medical data quality is proposed. The framework consists of two main components; first, an EA will be used to dynamically optimize the composition of medical processes into optimal task sequence according to specific quality attributes. Second, a multiagent framework will be proposed to discover, monitor, and report any inconstancy between the optimized task sequence and the actual medical records. To demonstrate the proposed framework, experimental results for a breast cancer case study are provided. Furthermore, to show the unique performance of our algorithm, a comparison with other works in the literature review will be presented.     

Force-Feedback Augmentation Modes in the Laparoscopic Minimally Invasive Telesurgical System

The proportional-integral (PI) gain-scheduling full-force-feedback control (three translational and one grasping degrees of freedom) on the dual-arm master/slave telesurgical manipulator system has been implemented. Gain scheduling algorithm provides the operator with maximum-allowable force perception, while retaining the stability of system manipulation with different environments. It is a way to cope with the instability caused by the motor saturation as well. Local force-feedback configuration naturally compensates for the inherent inertia of the master manipulator in addition to precise display of the interaction force. It has been observed that, during the operation, the surgeon often needs to concentrate on force in some directions than others. Also, the organs involved in the abdominal laparoscopic surgery tend to yield very small initial reaction force, which might be difficult to perceive. With the consideration of the performed task and tissue mechanical properties, the reaction force in those directions will be amplified while the others will be sent normally. Seven force-feedback augmentation modes have been prepared. A switching algorithm has been developed to automatically change the augmentation mode appropriately according to the position and force signals. The surgeon can, thus, focus more on the operation task.     

多机协同作战任务决策方法多智能体结构框架

多机协同攻击能够充分利用各个飞机的作战资源和空间占位,是未来空战的主要模式。为了描述多架飞机协同作战过程中的信息组织形式和传递流程,基于多智能体的行为特征和构造方式,从飞机编队和飞机平台两个层面建立了多智能体结构框架。由编队中的各架飞机为智能体组成第一层多智能体,由每架飞机内部的各个功能模块为智能体组成第二层多智能体结构。以编队层多智能体和长机内部的决策功能智能体为例,介绍了多智能体数学模型的设计细节。依据所建立的多智能体结构框架和数学模型,以编队层为例,描述了多机协同作战任务决策方法的信息流程。研究表明,以飞机编队和飞机平台两个层面多智能体结构框架描述多机协同作战过程是恰当的。     

A non‐cooperative game model for reliability‐based task scheduling in cloud computing

Cloud computing is a newly emerging distributed system. Task scheduling is the core research of cloud computing which studies how to allocate the tasks among the physical nodes, so that the tasks can get a balanced allocation or each task's execution cost decreases to the minimum, or the overall system performance is optimal. Unlike task scheduling based on time or cost before, aiming at the special reliability requirements in cloud computing, we propose a non‐cooperative game model for reliability‐based task scheduling approach. This model takes the steady‐state availability that computing nodes provide as the target, takes the task slicing strategy of the schedulers as the game strategy, then finds the Nash equilibrium solution. We also design a task scheduling algorithm based on this model. It can be seen from the experiments that our task scheduling algorithm is better than the so‐called balanced scheduling algorithm.