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In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES. [ABSTRACT FROM AUTHOR]

A cable-driven segmented manipulator (CDSM) has superior dexterity for operations in confined space due to its light-slender body and redundant degree of freedoms (DOFs). However, its inverse kinematics resolving and configuration planning are very challenging due to the complex structure and strict constraints. In this article, we propose a two-layer geometric iteration (TLGI) method for inverse kinematics resolving and configuration-constrained Cartesian path planning. The computation efficiency is largely improved and singularities are avoided. First, the end-effector attitude is decomposed into a direction vector and a rotation angle. The former and the end-effector position are combined into state variables of the inner layer, and the latter is treated separately as the state variable of the outer layer. Then, the TLGI method enables to rapidly reach the desired 6-DOF pose by two-layer iterations, i.e., the inner and outer loop iteration. Second, during the inner loop iteration, the CDSM is modeled as an equivalent articulated arm whose end-effector position and direction is the same as that of CDSM, but its links length and joint angles depend on the current configuration of CDSM. Then, the efficient forward and backward reaching inverse kinematics (FABRIKs) method is extended to apply on CDSM so that it can fast reach the inner state variables. During the outer loop iteration, three different rotation cases, i.e., the rotating around the end, root, and both end and root, are designed to switch automatically to reach the outer state variable iteratively. Moreover, by parameterizing geometric constraints of the environment, a TLGI-based configuration-pose simultaneous planning method is also put forward to efficiently achieve additional configuration constraints for operations of CDSM in confined space. Finally, the proposed method is verified by both the simulations and experiments. [ABSTRACT FROM AUTHOR]

Superior dexterity and extreme flexibility are typical advantages for concentric cable-driven manipulators working in confined spaces. However, its inverse kinematics and configuration planning are very complicated. In this article, we propose a spatial biarc method for the above problem. The distinguishing feature of this method is that input parameters are two positions and two direction vectors in three-dimensional (3-D) space, and the output is a reasonable spatial biarc for controlling a concentric cable-driven manipulator in 3-D space. This method has the following three advantages. First, the positions and direction vectors of the base and inner distal tip are considered simultaneously. In addition, the length and ratio of the overlapped section and separated section can be adjusted by changing the length of the direction vectors. Furthermore, by judging the angular value of the direction vectors, one can predetermine whether the spatial configuration of the entire arm is C- or S-shaped. The proposed method realizes the parameterization of a concentric cable-driven manipulator, which makes it convenient to intuitively control the manipulator to achieve interference-free motion trajectory planning in confined spaces. Finally, trajectory tracking inspections are simulated and experimentally executed. It can be seen from results that the proposed spatial biarc method can provide reasonable solutions for concentric cable-driven manipulators. The method is especially favorable in terms of 3-D-pose-determination problem and trajectory-planning problem. It can also be applied to other manipulators with similar configurations. Without loss of generality, when the given points and direction vectors are coplanar, the proposed spatial biarc method can be transformed to a planar biarc method. [ABSTRACT FROM AUTHOR]

Integrated energy systems (IESs) represent a promising energy supply model within the energy internet. However, multi-energy flow coupling in the optimal configuration of IES results in a series of simplifications in the preliminary planning, affecting the cost, efficiency, and environmental performance of IES. A novel optimal planning method that considers the part-load characteristics and spatio-temporal synergistic effects of IES components is proposed to enable a rational design of the structure and size of IES. An extended energy hub model is introduced based on the “node of energy hub” concept by decomposing the IES into different types of energy equipment. Subsequently, a planning method is applied as a two-level optimization framework—the upper level is used to identify the type and size of the component, while the bottom level is used to optimize the operation strategy based on a typical day analysis method. The planning problem is solved using a two-stage evolutionary algorithm, combing the multiple-mutations adaptive genetic algorithm with an interior point optimization solver, to minimize the lifetime cost of the IES. Finally, the feasibility of the proposed planning method is demonstrated using a case study. The life cycle costs of the IES with and without consideration of the part-load characteristics of the components were $4.26 million and $4.15 million, respectively, in the case study. Moreover, ignoring the variation in component characteristics in the design stage resulted in an additional 11.57% expenditure due to an energy efficiency reduction under the off-design conditions.

Integrated energy system (IES) has the advantages of improving energy efficiency, optimizing energy structure and reducing pollutant emissions, so it has become one of the most effective measures to solve the energy and environmental problems. To solve the planning problem of regional integrated energy system (RIES) without a given system structure, a zoning planning method (ZPM) based on energy hub (EH) model and the concept of directed acyclic graph is presented. That is, by analyzing the energy coupling relationship of EH, the EH zoning model based on equipment type is obtained, and the RIES system structure planning model is proposed. This method not only solves the problem that the planning results are influenced by the unfixed number of system structure planning model layers, but also can jointly plan the system structure and equipment configuration of RIES and improve computational efficiency. An illustrative example of RIES planning is given to make comparative analysis of the planning schemes obtained by hierarchical planning method (HPM), three-tier planning method (TPM) based on the equipment type and ZPM. The results show that ZPM can avoid the influence of the unfixed number of system structure planning model layers on the planning results, and can obtain more economical planning schemes and improve the solution speed of the planning model.

Snake-like robots can enable workers to avoid difficult-to-reach, dangerous, and hazardous environments while enhancing their capabilities. The technologies developed for a snake-like robot can be transferred to applications such as robotic exploration, minimally invasive surgical robotics, and robotic manipulation in manufacturing industries. In this paper we consider high-load tasks, such as drilling through the studs inside a wall, using a snake-like robot. The key technical innovation in this work is to design a search-based planning algorithm for high degree of freedom articulated systems that explicitly takes into account contact with surfaces in the environment in order to garner mechanical support for performing load-intensive tasks. In case of a snake-like robot, contacts with the studs and other structural members within walls need to be exploited to its advantage for bracing against walls for support in order to climb up or perform load-intensive operations such as drilling. We present a contact-augmented graph construction, which is the main technical tool for finding stable load-bearing configurations. We also develop motion controllers for moving the robot into the planned configuration and progressing the robot during the drilling process. We validate the algorithms through simulation and introduce a preliminary experimental setup. [ABSTRACT FROM AUTHOR]

The planar tape-spring hyper-redundant manipulator presented in this paper is mainly constructed from tape springs, fixed-drive components, and mobile-drive components. It not only has high robustness and excellent transformability, but also high packaging efficiency. However, when the manipulator extends to a long range in motion experiments, some segments of the tape springs buckle. To address this drawback, a kinematic model of the planar tape-spring hyper-redundant manipulator is established, and, a configuration planning method based on a virtual spring model is proposed to solve the inverse kinematics problem. To enhance stability, the column stability is then incorporated into the configuration planning model. This approach relies on only configuration planning to prevent buckling. An alternative approach of adding auxiliary rods into the manipulator is also proposed. With this method, extra intermediate supports have been added to the manipulator. The effective column length of some segments is shortened, which effectively increases the critical buckling load of those segments of the tape spring. Finally, a prototype was subjected to motion and stability experiments to validate the presented approaches and analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal. Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window. © 2014 Elsevier B.V. All rights reserved.

In this paper, we present RED-BL (Relocate Energy Demand to Better Locations), a framework to minimize the electricity cost for operating data center networks over consecutive intervals of fixed duration. Within each interval, RED-BL provides a mapping of workload to a set of geographically distributed data centers. To this end, RED-BL uses the geographical and temporal variations in electricity prices as exhibited by electrical energy markets. In addition, we incorporate the transition costs associated with a change in workload mapping from one interval to the next, over a planning window comprising multiple such intervals. This results in a sequence of workload mappings that is optimal over the entire planning window, even though the workload mapping in a given interval may not be locally optimal. Our evaluation of RED-BL uses electricity prices from the US markets and workload traces from live Internet applications with millions of users. We find that RED-BL can reduce the electric bill by as much as 45% compared to the case when the workload is uniformly distributed. When compared to existing workload relocation solutions, for a wide range of data center deployment sizes, RED-BL achieves electricity cost savings that are 8.28% higher, on average. This seemingly modest reduction can save millions of dollars for the operators. The cost of this saving is an inexpensive computation at the start of each planning window. © 2014 Elsevier B.V. All rights reserved.

With a light-slender and superior dexterous body, the cable-driven segmented redundant manipulators (CSRMs) are excellent candidates for operations in narrow confined space. However, their application is still largely limited due to their weak stiffness, low load capacity, and complex modeling and planning. In this article, we design a new type of CSRM with high stiffness and load capacity using hybrid active and passive cables, and propose a simplified kinematics modeling and configuration planning method. The manipulator is composed of three active-linkage segments and one active tool end-effector. Each active segment, driven by three active driving cables, has two degrees of freedom (DOFs). By utilizing the passive linkage cables, the segment can be fully constrained with high stiffness, and move with equal joint angles in nearly continuous shape. Based on such structural features, the kinematics equations about the active segment-joint variables (i.e., the equalpitch and yaw angles of the whole segment) are derived. The direct, inverse kinematics and differential kinematics are greatly simplified. To find suitable solutions under environment constraints, extended virtual joint (EVJ) is introduced for quick regional solutions searching and equivalent kinematic constraint analysis. Furthermore, a configuration planning method based on the simplified kinematics and equivalent EVJs is proposed for single obstacle avoidance and continuous narrow space crossing tasks. Finally, a prototype system is developed and typical experiments are performed. The results show the high linkage accuracy, improved stiffness, and load capacity of the developed CSRM and verify the adaptability of the proposed configuration planning method. [ABSTRACT FROM AUTHOR]

Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach. [ABSTRACT FROM AUTHOR]

The dynamic non-destructive grasp of thin-skinned fruits using flexible robotic hands, which requires obtaining three-dimension(3D) spatial structure information along with adaptive planning and motion control toward the target object, is a challenging topic in agricultural intelligence. To tackle the issue of 3D detection, we utilize RGB images and LiDAR point clouds for feature extraction and construct a multi-modal depth fusion convolution neural network (MDF-CNN) to obtain the classification information and perform image segmentation. Incorporating the advantages of a variable palm structure, we establish an evaluation mechanism of the optimal grasping stability (EM-OGS) using a hybrid method of the best configuration and force closure to build a new comprehensive performance optimal configuration planning (CPO-CP) method that is based on the multiple grasping performance indexes. We also create three cross-related nonlinear prediction models P-MGF, P-ODAP, and P-OBA along with a forward-looking non-destructive grasp control algorithm (FL-NGCA) using minimum grasping force to grasp thin-skinned fruits. The control algorithm carries out online, self-directed learning in the actual grasping process of the flexible hand and constantly optimizes the accuracy of the prediction model. Experimental results show that our proposed approach greatly improves the flexible hand comprehensive performance of grasping, outperforming state-of-the-art methods for non-destructive grasping of delicate fruits in most areas. • A multi-modal depth fusion convolution neural network is constructed to obtain the information of classification and instance segmentation. • An evaluation mechanism of the optimal grasping stability based on the hybrid method of the best configuration and force closure is established to form the comprehensive performance optimal configuration planning method. • A forward-looking non-destructive grasping control algorithm based on minimum grasping force is proposed to achieve dynamic non-destructive grasping of thin-skinned fruits. [ABSTRACT FROM AUTHOR]

This paper considers a computer configuration design problem. The computer system is modeled by a closed queuing network. The system throughput is the objective function to be maximized and the speed of the devices axe the decision variables, A rich class of non-linear cost functions is considered. It is shown that any local optimum of the optimization problem is also a global optimum. it is also shown that the cost constraint is active and that the method of Lagrange multipliers van be used to solve the problem efficiently. [ABSTRACT FROM AUTHOR]

• A novel expansion planning model for urban integrated energy system is proposed. • The transportation network is incorporated into the model, in which fast and slow charging modes are considered. • A layered-based framework for the optimization model of the urban integrated energy system is proposed. • A two-layer optimization algorithm is designed to solve the proposed model. • Simulation results based on a real case in China indicate the superiority of the proposed approach. In the modern metropolitan regions, the coupling among distribution networks (DNs), gas networks (GNs), and transportation networks (TNs) is increasingly intensified, which gives rise to the urgent demand for efficient modeling and solving methods. In this context, a mathematical model for the configuration planning of integrated energy systems is proposed in this paper. With the aim of minimizing total planning costs, the model jointly considers the investment and operation strategies for distribution lines, distributed wind power generators (DWGs), combined heat and power units (CHPs), power to gas units (P2Gs), gas furnaces (GFs), fast charging facilities (FCFs) and slow charging facilities (SCFs). Since the proposed model comes down to a non-convex and nonlinear optimization problem, a two-layer algorithm is designed to solve it. In the outer layer, a genetic algorithm (GA) is performed to obtain a set of individuals representing the investment decisions on distribution lines, DWGs, and FCFs, which will be delivered to the inner layer model. With fixed decision variables, the inner model optimizes the operating costs, charging profit coefficients, and strategies for DWGs and energy converters in a coordinated manner. A real urban area in China is employed in the numerical experiment, which corroborates the effectiveness of the proposed model and solution algorithm. [ABSTRACT FROM AUTHOR]