Semi-Direct Monocular Visual-Inertial Odometry Using Point and Line Features for IoV

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation. Internet of Vehicles ( IoV ) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles. Global positioning System ( GPS ) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features ( SDMPL-VIO ) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost Inertial Measurement Unit ( IMU ) and monocular camera, using them as the sensor to acquire the surrounding environmental information. Visual-Inertial Odometry ( VIO ), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.

Identifying Key Characteristics of Business Rules That Affect Software Project Success

Today, businesses need to continuously adjust to a dynamic environment. Enterprises have to deal with global competition and technological advances, meet government regulations, and keep their expenses under control. Under these pressures, enterprises need to implement and improve software that supports and helps to evolve their business. However, as practice shows, software implementation projects are complex, and a considerable percentage of them do not meet business requirements. Therefore, a business needs to manage software implementation properly. Existing research shows that using business rules (BR) in software implementation projects helps to ensure its success. The purpose of our study is to advance the understanding of how BR affect software implementation success, namely, which key characteristics of BR are the most important. To achieve this goal, the top thousand enterprises in Slovenia, by added value, facing typical software implementation projects were surveyed. The obtained results show that BR that are specifically prepared for a particular project and easy to understand have a statistically significant positive effect on software implementation project success.

Research on Trajectory Planning in Thunderstorm Weather Based on Dynamic Window Algorithm during Approach Segment

Dynamic window algorithm (DWA) is a local path-planning algorithm, which can be used for obstacle avoidance through speed selection and obtain the optimal path, but the algorithm mainly plans the path for fixed obstacles. Based on DWA algorithm, this paper proposes an improved DWA algorithm based on space-time correlation, namely, space-time dynamic window approach. In SDWA algorithm, a DWA associated with obstacle position and time is proposed to achieve the purpose of path planning for moving obstacles. Then, by setting the coordinates of the initial moving obstacle and identifying safety distance, we can define the shape of the obstacle and the path planning of the approach segment in thunderstorm weather based on the SDWA model was realized. Finally, the superior performance of the model was verified by setting moving obstacles for path planning and selecting the aircraft approach segment in actual thunderstorm weather. The results showed that SDWA has good path-planning performance in a dynamic environment. Its path-planning results were very similar to an actual aircraft performing thunderstorm-avoidance maneuvers, but with more smooth and economical trajectory. The proposed SDWA model had great decision-making potential for approach segment planning in thunderstorm weather.

Strategic change management

The textbook is developed on the basis of competence-based and interdisciplinary approaches, contains theoretical foundations for the formation, change, development and improvement of organization management systems in a dynamic environment, as well as methodological aspects of the development and practical implementation of strategic changes. The publication examines the features of strategic changes, the technology of change management, reflects the models and principles of organizational changes, defines the prerequisites for the development of scenarios for the development of the organization, factors, conditions and mechanisms for the implementation of the change management strategy in the organization. The publication fully complies with the requirements of the federal state educational standards of higher education of the latest generation. It is intended for students studying in the areas of training 38.03.02 "Management", 38.03.03 "Personnel Management", 38.03.04 "State and municipal management". It will also be useful for students of MBA programs, advanced training courses and professional training of managerial personnel, senior students of economic specialties of universities, graduate students, teachers, practitioners and anyone interested in the problems of effective development of organizations based on the approach of organizational change.

An Effective Dynamic Path Planning Approach for Mobile Robots Based on Ant Colony Fusion Dynamic Windows

To further improve the path planning of the mobile robot in complex dynamic environments, this paper proposes an enhanced hybrid algorithm by considering the excellent search capability of the ant colony optimization (ACO) for global paths and the advantages of the dynamic window approach (DWA) for local obstacle avoidance. Firstly, we establish a new dynamic environment model based on the motion characteristics of the obstacles. Secondly, we improve the traditional ACO from the pheromone update and heuristic function and then design a strategy to solve the deadlock problem. Considering the actual path requirements of the robot, a new path smoothing method is present. Finally, the robot modeled by DWA obtains navigation information from the global path, and we enhance its trajectory tracking capability and dynamic obstacle avoidance capability by improving the evaluation function. The simulation and experimental results show that our algorithm improves the robot's navigation capability, search capability, and dynamic obstacle avoidance capability in unknown and complex dynamic environments.

Deep Reinforcement Learning-Based Workload Scheduling for Edge Computing

Edge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users. It offers an effective solution to help mobile devices with computation-intensive and delay-sensitive tasks. However, the edge of network presents a dynamic environment with large number of devices, high mobility of users, heterogeneous applications and intermittent traffic. In such environment, edge computing often suffers from unbalance resource allocation, which leads to task failure and affects system performance. To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and the failed task rate. Meanwhile, We adopt Deep-Q-Network(DQN) algorithms to solve the complexity and high dimension of workload scheduling problem. Simulation results show that our proposed approach achieves the best performance in aspects of service time, virtual machine(VM) utilization, and failed tasks rate compared with other approaches. Our DRL-based approach can provide an efficient solution to the workload scheduling problem in edge computing.

A Robust Visual-Aided Inertial Navigation Algorithm for Pedestrians

Real-time and robust state estimation for pedestrians is a challenging problem under the satellite denial environment. The zero-velocity-aided foot-mounted inertial navigation system, with the shortcomings of unobservable heading, error accumulation, and poorly adaptable parameters, is a conventional method to estimate the pose relative to a known origin. Visual and inertial fusion is a popular technology for state estimation over the past decades, but it cannot make full use of the movement characteristics of pedestrians. In this paper, we propose a novel visual-aided inertial navigation algorithm for pedestrians, which improves the robustness in the dynamic environment and for multi-motion pedestrians. The algorithm proposed combines the zero-velocity-aided INS with visual odometry to obtain more accurate pose estimation in various environments. And then, the parameters of INS have adjusted adaptively via taking errors between fusion estimation and INS outputs as observers in the factor graphs. We evaluate the performance of our system with real-world experiments. Results are compared with other algorithms to show that the absolute trajectory accuracy in the algorithm proposed has been greatly improved, especially in the dynamic scene and multi-motions trials.

The Potential for Lunar and Martian Regolith Simulants to Sustain Plant Growth: A Multidisciplinary Overview

Bioregenerative life support systems (BLSS) are conceived of and developed so as to provide food sources for crewed missions to the Moon or Mars. The in situ resource utilization (ISRU) approach aims to reduce terrestrial input into a BLSS by using native regoliths and recycled organic waste as primary resources. The combination of BLSS and ISRU may allow sustainable food production on Moon and Mars. This task poses several challenges, including the effects of partial gravity, the limited availability of oxygen and water, and the self-sustaining management of resources. Lunar and Martian regoliths are not available on Earth; therefore, space research studies are conducted on regolith simulants that replicate the physicochemical properties of extra-terrestrial regoliths (as assessed in situ by previous missions). This review provides an overview of the physicochemical properties and mineralogical composition of commercially available Lunar and Martian regolith simulants. Subsequently, it describes potential strategies and sustainable practices for creating regolith simulants akin to terrestrial soil, which is a highly dynamic environment where microbiota and humified organic matter interact with the mineral moiety. These strategies include the amendment of simulants with composted organic wastes, which can turn nutrient-poor and alkaline crushed rocks into efficient life-sustaining substrates equipped with enhanced physical, hydraulic, and chemical properties. In this regard, we provide a comprehensive analysis of recent scientific works focusing on the exploitation of regolith simulant-based substrates as plant growth media. The literature discussion helps identify the main critical aspects and future challenges related to sustainable space farming by the in situ use and enhancement of Lunar and Martian resources.