Destination Positions Research Articles

Energy-Efficiency Joint Trajectory and Resource Allocation Optimization in Cognitive UAV Systems

In this article, an effective energy-efficiency optimization problem is investigated in the cognitive unmanned aerial vehicle (UAV) communication system, where the moving following UAV (FUAV) transmits collected data to the leading UAV (LUAV) by reusing the spectrum of the ground primary user. For this scenario, a novel joint UAV trajectory and resource allocation optimization algorithm is proposed. We aim to maximize the energy efficiency of the cognitive UAV communication systems under interference, shortest step size, collision prevention, and speed constraints. However, this optimization problem is difficult to be solved, as it is nonconvex and involves strong relations among many variables. To address this issue, we first decompose the original optimization problem into four subproblems: 1) spectrum sensing duration subproblem; 2) spectrum sensing threshold subproblem; 3) transmitted power subproblem; and 4) FUAV trajectory optimization subproblem. For spectrum sensing duration subproblem and spectrum sensing threshold subproblem, their optimal solutions can be efficiently solved via a golden section search method. For the transmitted power subproblem, the closed-form expression of the optimal transmitted power is deduced. For the nonconvex FUAV trajectory optimization subproblem, we consider the shortest step size design under known starting and destination positions and then obtain an approximate solution of FUAV trajectory via the successive convex optimization (SCO) technique. Finally, an alternate iterative framework with fast convergence is designed to solve the original optimization problem. The simulation results show that the proposed algorithm has 18 times improvement in energy efficiency compared with the straight flight scheme with lower energy consumption.

Agritourism sector of Kazakhstan: attracting foreign direct investment

The investment policy of each country is aimed to increase investment in potential areas of economy. Tourism in the world market, including in agricultural sector, has become an attractive industry for investors who are planning long-term income. The goal is to analyze how effective it is to attract foreign direct investment in agro-tourism complex of Kazakhstan. To achieve this goal, methods of organizing interviews with specialists and a survey of existing representatives of tourism business were used. The results were obtained by integrating qualitative and quantitative analysis in the course of survey with targeted audience selection. Results – the positive impact of investments of foreign countries in agritourism was revealed, since developing countries are most interested and need to invest in tourism activities. The initiative is justified by the fact that targeted bank deposits have both direct and indirect impact on other market segments, creating new jobs, developing services, and improving infrastructure. Conclusions – the estimated position of entrepreneurs in rural tourism revealed the low potential of the republic for external sources of financing, since there are a number of factors that reduce investment opportunities. Taking into account the negatively affecting inhibitors, recommendations have been developed to increase investment activity based on the analysis of secondary data with the study of foreign experience and primary materials obtained during the collection of information and interviews. For comparison, the example of Uzbekistan was used, with similar economic, cultural and historical characteristics. In addition, business tourism of this destination positions the tourist product, having resources similar to those of the Republic of Kazakhstan on the world stage.

Cultivating a Leadership Pipeline: Using a Real Options Lens to Understand Executives’ Strategic Staffing Decisions

This paper adapts real options theory to explain how executives create and maintain real options portfolios within leadership pipelines. Hypotheses flowing from our theorizing predict that executives often make seemingly risky staffing decisions for leaders who occupy stepping-stone positions. Focusing on their option (future potential) rather than project (current productivity) value, executives laterally transfer leaders in stepping-stone positions frequently, despite it resulting in lower short-term job performance, but often promote these leaders at lower levels of performance and sooner. Once leaders are promoted to destination positions where they may stay indefinitely, executives tend to transfer high-performing leaders more often but not when they are still improving the effectiveness of their current unit. We present evidence suggesting that executives make these decisions to improve other units and maintain a flexible system, possibly recapturing previous investments in developing those leaders. We provide empirical support for our hypotheses using eight years of data in a large retail organization (n = 25,004) where executives overseeing thousands of units made internal mobility decisions. These findings refine real options theory, show that it explains these phenomena better than existing theories, and provide important and immediately usable practical implications for executives. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1608 .

How are social origin, destination and mobility linked to physical, mental, and self-rated health? Evidence from the United States

For decades, scholars have been exploring persistent inequalities in health by studying the roles of origin and destination socioeconomic positions (SEP), and the importance of social mobility trajectories from childhood to adult life in individuals’ wellbeing. However, this literature does not produce consistent and systematic findings on the relative importance of origin and destination SEP and independent social mobility effects. One of the main reasons for this is a set of methodological choices and decisions which researchers make. Arguably, one of the most critical aspects of research design is the operationalisation of SEP and the selection of health outcomes, usually without accounting for initial values of the health indicators employed. Using a nationally representative longitudinal dataset (Add Health) for the United States and diagonal reference models, in the present study, we examine how the choice of SEP in terms of educational, occupational, and income attainment, and the choice of health measures in terms of obesity, depressive symptoms, and self-rated health, influence findings on the origin and destination effects, as well as the health implications of social mobility. We also address the health selection problem by explicitly accounting for adolescents’ health in terms of each health outcome considered. Our results indicate that both choosing SEP and health measures, and accounting for social mobility and adolescent health have a noticeable impact on the relative importance of social origin and destination positions for health outcomes. We do not find evidence that social mobility has an independent effect on health, or that individuals’ previous health status moderates this association.

A Survey on OD Data Visualization

<p indent=0mm>OD data is a type of data that provides origin and destination positions, and sparsely describes the movement trajectory of objects. Being the research hotspots in data visualization and geospatial analysis, visual analysis of OD data contributes to discovering group movement patterns, exploring spatial anomalies and hidden relationships, and in-depth analysis of various statistics attributes. It is started from the pair-point structure of OD data in geospatial space, and classical OD data visualization methods and principles are elaborated, such as OD matrix, OD map, OD flow, etc. Then from the perspective of spatial-temporal feature exploration, spatial and spatial-temporal joint dimensions, the visual analysis of OD data is performed, while its application scenarios and enhanced significance of feature expression, perception, extraction and presentation are researched. To solve the problem of visual clutter in large-scale OD data visualization, the visual simplification methods such as filtering, aggregation, and sampling are sorted out in the research methods of visual analysis of OD data. Finally, the applications and specific cases of OD data visual analysis technology are investigated in different domains, the challenging problems in the research of OD data visual analysis are summarized, and the trend of its future development is prospected.

Indoor Unmanned Aerial Vehicle Navigation System Using LED Panels and QR Codes

In this study, we propose an unmanned aerial vehicle (UAV) navigation system using LED panels and QR codes as markers in an indoor environment. An LED panel can display various patterns; hence, we use it as a command presentation device for UAVs, and a QR code can embed various pieces of information, which is used as a sign to estimate the location of the UAV on the way of the flight path. In this paper, we present a navigation method from departure to destination positions in which an obstacle lies between them. In addition, we investigate the effectiveness of our proposed method using an actual UAV.

Anisotropic Q-learning and waiting estimation based real-time routing for automated guided vehicles at container terminals

Finding short and convenient routes for vehicles is an important issue on efficient operations of Automated Guided Vehicle (AGV) systems at container terminals. This paper proposes an anisotropic Q-learning method for AGVs to find the shortest-time routes in the guide-path network of cross-lane type according to real-time vehicle states, which includes current and destination positions, heading direction and the number of vehicles at anisotropic four-direction neighboring locations. The vehicle waiting time of AGV systems is discussed and its estimation is suggested to improve the policy to select actions in the Q-learning method. An improved anisotropic Q-learning routing algorithm is developed with the vehicle-waiting-time-estimation based selecting-action policy. The parameter settings and performance of the proposed methods are analyzed based on simulations. The numerical experiments show that the improved anisotropic Q-learning method can provide stable and dynamic solutions for AGV routing, and achieve 9.5% improvement in optimization efficiency compared to the Jeon Learning Method (Jeon et al. in Logist Res 3(1):19–27, 2011).

Path Planning for Reconfigurable hTetro Robot Combining Heat Conduction-Based and Discrete Optimization

Self-reconfigurable robots present advanced solutions for various automation applications in domains, e.g., planetary exploration, rescue missions, cleaning, and maintenance. These robots have the ability to change their morphology according to given requirements or adapt to new circumstances, which, for example, can overcome constraints while navigating within a working environment. However, the autonomous navigation of self-reconfigurable robots is more complex than that of robots with fixed shape because of the intrinsic complexity of robot motions, especially in complicated obstacle environments. To address this challenge, we present a novel path planning method for reconfigurable robots in this study. The technique is inspired by the similarity between a robot motion path and a heat conduction path at the steady-state. In the heat transfer analysis domain, feasible moving locations are modeled as materials with high conductivity, while obstacles are considered thermal insulators, and the initial and destination positions are assigned as heat sink and heat source, respectively. The temperature profile and gradient calculated by finite element analysis are used to indicate the possible moving directions from the heat sink to the heat source. Based on the temperature gradient ascent, a step-wise conductivity reaching algorithm is developed to optimize robot paths using customized multi-objective functions that take the costs of morphology changes, path smoothness, and safety into account. The proposed path planning method is successfully applied to the hinged-tetro self-reconfigurable robot and demonstrated on several virtual environments and a real-world testbed environment.

Intelligent Multirobot Navigation and Arrival-Time Control Using a Scalable PSO-Optimized Hierarchical Controller.

We present a hierarchical fuzzy logic system for precision coordination of multiple mobile agents such that they achieve simultaneous arrival at their destination positions in a cluttered urban environment. We assume that each agent is equipped with a 2D scanning Lidar to make movement decisions based on local distance and bearing information. Two solution approaches are considered and compared. Both of them are structured around a hierarchical arrangement of control modules to enable synchronization of the agents' arrival times while avoiding collision with obstacles. The proposed control module controls both moving speeds and directions of the robots to achieve the simultaneous target-reaching task. The control system consists of two levels: the lower-level individual navigation control for obstacle avoidance and the higher-level coordination control to ensure the same time of arrival for all robots at their target. The first approach is based on cascading fuzzy logic controllers, and the second approach considers the use of a Long Short-Term Memory recurrent neural network module alongside fuzzy logic controllers. The parameters of all the controllers are optimized using the particle swarm optimization algorithm. To increase the scalability of the proposed control modules, an interpolation method is introduced to determine the velocity scaling factors and the searching directions of the robots. A physics-based simulator, Webots, is used as a training and testing environment for the two learning models to facilitate the deployment of codes to hardware, which will be conducted in the next phase of our research.

Control Position of Mobile Robot Based on Odometry Method and PID Controller

A mobile robot had been made based on kinematics drive differential concept. This robot used odometry method in order to reach a position and determine its current position. Proportional Integral Derivative (PID) control has been applied for improving response of robot while it reach a detremined position. Two rotary encoder sensors were attached at right and left side of robot motors for measuring distance. While motor rotating, rotary encoder sensors counted a number of pulses conversed into left and right wheel mileage. Both of them were used to get robot position at certain time (x, y, and heading angle). Robot passed some tracks and reached two positions in each track as destination positions. These positions are the position which had final heading 30° and 90°on first track, 45° and 90° on second track, 60° and 90° on third track. PID control was examined by put mobile robot in first position 0,0,0°. Some PID parameters have been applied to examine the performance and response of robot. The increment of derivative parameter reduced the steady state error and increased the stability of system.

Laser simulator

A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoi diagram with fast marching and PointBug algorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.

Impact of Social Relation and Group Size in Multicast Ad Hoc Networks

This paper investigates the multicast capacity of static wireless social networks. We adopt the two-layer network model, which includes the social layer and the networking layer. In the social layer, the social group size of each source node is modeled as power-law distribution. Moreover, the rank-based model is utilized to describe the relation between source and destinations in the networking layer. Based on the two-layer network model, the probability density function (PDF) of the destination positions is analyzed and verified by numerical simulation, which is different from the traditional ad hoc networks. According to the PDF, the bound of the network capacity is derived, and we propose a Euclidean minimum-spanning-tree-based transmission scheme, which is proved to achieve the order of capacity bound for most cases. Finally, the capacity of social networks is compared to the traditional multicast ad hoc networks, which indicates that the capacity scaling performs better in social networks than traditional ones. To our best knowledge, this is the first work of analyzing the impact on the capacity of social relation and group size in multicast ad hoc networks for the rank-based model.

Why Do Women Still Not Make It to the Top? Dominant Organizational Ideologies and Biases by Promotion Committees Limit Opportunities to Destination Positions

Prior studies have made important strides in understanding the drivers of gender bias facing women at the top. Yet, relatively little is known about the intra-organizational power dynamics of how and why these patterns still persist despite a plethora of initiatives to redress the phenomenon over the last several decades. This paper develops an intra-organizational power perspective on the dynamics of promotion bias to destination positions. We propose that social dominance emerges as social categorization based on a candidate’s visible and invisible markers leads to distorted perceptions and stereotyping which, when combined with group favoritism and conformity pressures within committee practices, engender the perceived degree of ideological asymmetry between the candidate and the organization. It is the magnitude of the perceived degree of ideological asymmetry that drives promotion bias. This bias has potent effects on the institutionalization of power over time. Our perspective ultimately offers new insights into the role of dominant organizational ideology and dynamics of biases that continue to limit promotion opportunities of women to destination positions.

The Design and Implementation of a GPS-based Realtime Unmanned Driving System

This study is to design and implement an unmanned driving system that runs to a certain destination of the user in connection with a smart phone and Arduino. Once the departure and destination positions of the unmanned vehicle are set by means of a smart phone, it runs based on GPS information. This system is designed in a way that the monitoring and controlling are also possible by means of a smart phone. An Arduino-based unmanned vehicle is also designed and implemented