Payload Management Research Articles

UAV payload virtualization based on the unified driving and capability abstraction

As UAV and swarms applications continue to deepen and become increasingly widespread, UAV payloads have characteristics such as diverse types, heterogeneous forms, and complex interfaces. At the same time, it is required that the system can quickly access and efficiently manage payloads. In order to decouple the close relationship between the payloads and the system platforms, and expand the compatibility with heterogeneous payloads, a novel payload device virtualization method is proposed. Firstly, a layered driver model is proposed to decouple the device hardware and device driver, standardize the payload access process, and the driver model is implemented by using the software and hardware. Secondly, a payload abstraction based on "capability" is proposed to decouple load application and device. Finally, specific examples for the verification of the virtualization method are implemented. The present UAV payload virtualization method can not only achieve software and hardware decoupling, but also enhance the compatibility and scalability of system, improving the efficiency of unified payload management.

IMUC: Edge–End–Cloud Integrated Multi-Unmanned System Payload Management and Computing Platform

Multi-unmanned systems are primarily composed of unmanned vehicles, drones, and multi-legged robots, among other unmanned robotic devices. By integrating and coordinating the operation of these robotic devices, it is possible to achieve collaborative multitasking and autonomous operations in various environments. In the field of surveying and mapping, the traditional single-type unmanned device data collection mode is no longer sufficient to meet the data acquisition tasks in complex spatial scenarios (such as low-altitude, surface, indoor, underground, etc.). Faced with the data collection requirements in complex spaces, employing different types of robots for collaborative operations is an important means to improve operational efficiency. Additionally, the limited computational and storage capabilities of unmanned systems themselves pose significant challenges to multi-unmanned systems. Therefore, this paper designs an edge–end–cloud integrated multi-unmanned system payload management and computing platform (IMUC) that combines edge, end, and cloud computing. By utilizing the immense computational power and storage resources of the cloud, the platform enables cloud-based online task management and data acquisition visualization for multi-unmanned systems. The platform addresses the high complexity of task execution in various scenarios by considering factors such as space, time, and task completion. It performs data collection tasks at the end terminal, optimizes processing at the edge, and finally transmits the data to the cloud for visualization. The platform seamlessly integrates edge computing, terminal devices, and cloud resources, achieving efficient resource utilization and distributed execution of computing tasks. Test results demonstrate that the platform can successfully complete the entire process of payload management and computation for multi-unmanned systems in complex scenarios. The platform exhibits low response time and produces normal routing results, greatly enhancing operational efficiency in the field. These test results validate the practicality and reliability of the platform, providing a new approach for efficient operations of multi-unmanned systems in surveying and mapping requirements, combining cloud computing with the construction of smart cities.

Forest transport payload management in the delivery of efficient forest supply chains

Operation payload management investigated the impact of two weighing technologies (truck and loader installed scales), operated by hired drivers and owner-operators on two route types; gazetted (classified for increased GVW limits) and non- gazetted (standard GVW limits for public roads). On non-gazetted roads the two approaches proved equally effective but when used together appeared to be less effective, while the owner operators appeared to have better management of the payload compared to hired drivers. On the gazetted roads all vehicles were under loaded by roughly the extra payload amount offered by the gazetted routes, which highlights the importance of communication and overall management to ensure payload potential is realised.

Design of On-orbit Autonomous Control for Payload Management Software Based on Data Sheet

目前有效载荷在轨工作呈现多样化的趋势，为更好地适应这种多样化的任务，设计了一种在轨工作自主控制方案。该方案对多个型号的软件需求进行归纳整理和统筹分析，对在轨工作自主控制进行模块和功能划分，解决了多个载荷管理软件在轨工作控制需求差异化带来的多样化、定制化问题。通过引入可配置设计思路，建立数据表对载荷工作流程进行配置，解决了对特定载荷在轨工作流程的适配性问题，可实现多载荷多任务的在轨工作自主控制功能。该方法已在多个型号卫星的载荷管理器上使用，满足载荷单元工作流程多样化的要求。软件的重复使用不仅大大提高了软件设计和测试阶段的时间，缩短软件研制周期，还可以降低软件开发成本，并且提高软件整个研制阶段的可靠性。

Convolutional Neural Networks for Flexible Payload Management in VHTS Systems

Very high throughput satellite (VHTS) systems are expected to have a large increase in traffic demand in the near future. However, this increase will not be uniform throughout the service area due to the nonuniform user distribution, and the changing traffic demand during the day. This problem is addressed using flexible payload architectures, enabling the allocation of the payload resources in a flexible manner to meet traffic demand of each beam, leading to dynamic resource management (DRM) approaches. However, DRM adds significant complexity to the VHTS systems, which is why in this article, we are analyzing the use of convolutional neural networks (CNNs) to manage the resources available in flexible payload architectures for DRM. The VHTS system model is first outlined, for introducing the DRM problem statement and the CNN-based solution. A comparison between different payload architectures is performed in terms of DRM response, and the CNN algorithm performance is compared by three other algorithms, previously suggested in the literature to demonstrate the effectiveness of the suggested approach and to examine all the challenges involved.

Development of Payload Management System for HTV Small Re-entry Capsule

Development of Payload Management System for HTV Small Re-entry Capsule

A comprehensive investigation of loading variance influence on fuel consumption and gas emissions in mine haulage operation

The data collected from haul truck payload management systems at various surface mines show that the payload variance is significant and must be considered in analysing the mine productivity, diesel energy consumption, greenhouse gas emissions and associated costs. The aim of this study is to determine the energy and cost saving opportunities for truck haulage operations associated with the payload variance in surface mines. The results indicate that there is a non-linear relationship between the payload variance and the fuel consumption, greenhouse gas emissions and associated costs. A correlation model, which is independent of haul road conditions, has been developed between the payload variance and the cost saving using the data from an Australian surface coal mine. The results of analysis for this particular mine show that a significant saving of fuel and greenhouse gas emissions costs is possible if the standard deviation of payload is reduced from the maximum to minimum value.

A discrete-event model to simulate the effect of truck bunching due to payload variance on cycle time, hauled mine materials and fuel consumption

Data collected from truck payload management systems at various surface mines shows that the payload variance is significant and must be considered in analysing the mine productivity, energy consumption, greenhouse gas emissions and associated cost. Payload variance causes significant differences in gross vehicle weights. Heavily loaded trucks travel slower up ramps than lightly loaded trucks. Faster trucks are slowed by the presence of slower trucks, resulting in ‘bunching’, production losses and increasing fuel consumptions. This paper simulates the truck bunching phenomena in large surface mines to improve truck and shovel systems’ efficiency and minimise fuel consumption. The study concentrated on completing a practical simulation model based on a discrete event method which is most commonly used in this field of research in other industries. The simulation model has been validated by a dataset collected from a large surface mine in Arizona state, USA. The results have shown that there is a good agreement between the actual and estimated values of investigated parameters.

Supervising and Controlling Unmanned Systems: A Multi-Phase Study with Subject Matter Experts.

Proliferation in the use of Unmanned Aerial Systems (UASs) in civil and military operations has presented a multitude of human factors challenges; from how to bridge the gap between demand and availability of trained operators, to how to organize and present data in meaningful ways. Utilizing the Design Research Methodology (DRM), a series of closely related studies with subject matter experts (SMEs) demonstrate how the focus of research gradually shifted from “how many systems can a single operator control” to “how to distribute missions among operators and systems in an efficient way”. The first set of studies aimed to explore the modal number, i.e., how many systems can a single operator supervise and control. It was found that an experienced operator can supervise up to 15 UASs efficiently using moderate levels of automation, and control (mission and payload management) up to three systems. Once this limit was reached, a single operator's performance was compared to a team controlling the same number of systems. In general, teams led to better performances. Hence, shifting design efforts toward developing tools that support teamwork environments of multiple operators with multiple UASs (MOMU). In MOMU settings, when the tasks are similar or when areas of interest overlap, one operator seems to have an advantage over a team who needs to collaborate and coordinate. However, in all other cases, a team was advantageous over a single operator. Other findings and implications, as well as future directions for research are discussed.

Model-Driven Design of Real-Time Software for an Experimental Satellite

Model-driven engineering has gained widespread interest as a means to raise the abstraction level in software development, thus lowering cost and increasing efficiency. In this paper, a case study on using this approach to design the real-time software for UPMSat-2, an experimental micro-satellite, is described. The functionality of the software includes attitude determination and control, on-board data handling, platform monitoring and control, and payload management. A mix of modelling and software engineering tools have been used, enabling automatic code generation of most of the application software. The lessons learned from such an approach are analysed in the paper, and the use of model-driven engineering in real-time control systems is discussed.

Novel Multi-Sonar Controller and Other Advanced Features Developed and Tested on Latis, a Smart, Remotely Operated Vehicle

This paper describes in detail a novel multi-sonar controller developed at the University of Limerick and tested on the new smart, remotely operated vehicle (ROV) Latis at sea in March 2009. The multi-sonar controller allows the simultaneous operation of similar-frequency imaging and mapping sonar by interrogating the sonar returns of the multi-beam sonar in real time and adaptively triggering the pings of each sonar in a terrain-adaptive manner to avoid cross-talk and interference in sonar returns. The technique has benefits in hydrographic mapping and in payload sonar management on unmanned underwater vehicles. Detailed results on the testing of the sonar controller system are reported. The paper also describes the smart ROV Latis, designed and built at the University of Limerick, on which the sonar systems have been tested. With its advanced control systems and high-bandwidth networking capabilities, the ROV Latis is an ideal platform for experimentation of new techniques such as the multi-sonar controller, which can be ported to other ROVs and autonomous underwater vehicle platforms once proven.