Browse Topic: Computer software and hardware
This document defines a set of standard application layer interfaces called JAUS Manipulator Services. JAUS Services provide the means for software entities in an unmanned system or system of unmanned systems to communicate and coordinate their activities. The Manipulator Services represent platform-independent capabilities commonly found across domains and types of unmanned systems. At present, twenty-five (25) services are defined in this document. These services are categorized as: Low Level Manipulator Control Services – The one service in this category allows for low-level command of the manipulator joint actuation efforts. This is an open-loop command that could be used in a simple tele-operation scenario. The service in this category is listed as follows: Primitive Manipulator Service Manipulator Sensor Services – These services, when queried, return instantaneous sensor data. Three services are defined that return respectively joint positions, joint velocities, and joint
The SAE Aerospace Information Report AIR5315 – Generic Open Architecture (GOA) defines “a framework to identify interface classes for applying open systems to the design of a specific hardware/software system.” [sae] JAUS Service (Interface) Definition Language defines an XML schema for the interface definition of services at the Class 4L, or Application Layer, and Class 3L, or System Services Layer, of the Generic Open Architecture stack (see Figure 1). The specification of JAUS services shall be defined according to the JAUS Service (Interface) Definition Language document.
Software Defined Vehicle (SDV) is gaining attraction in the automotive industry due to its wide range of benefits like remote software/feature upgrade, scalable functionality, Electronic Control Unit (ECU) commonization, remote diagnostics, increased safety, etc. To obtain all these benefits, ECUs need to be designed accordingly. ECU hardware must be designed to support a range of vehicles with a variety of loading, scalable features, power distribution, levels of processing, and networking architecture. Each domain has unique challenges to make the ECU economical and robust to operating conditions without compromising performance. This paper illustrates the critical hardware design challenges to accommodate a scalable SDV architecture. This paper focuses electrical interface design to support wide range of input/output port loads, scalable functionality, and robust diagnostics. Also, flexibility of microprocessor processing capability, ECU networking, and communication complexity are
Security flaws in automotive software have significant consequences. Modern automotive engineers must assess software not only for performance and reliability but also for safety and security. This paper presents a tool to verify software for safety and security. The tool was originally developed for the Department of Defense (DoD) to detect cybersecurity vulnerabilities in legacy safety-critical software with tight performance constraints and a small memory footprint. We show how the tool and techniques developed for verifying legacy safety-critical software can be applied to automotive and embedded software using real-world case studies. We also discuss how this tool can be extended for software comprehension.
In the automotive industry, there have been many efforts of late in using Machine Learning tools to aid crash virtual simulations and further decrease product development time and cost. As the simulation world grapples with how best to incorporate ML techniques, two main challenges are evident. There is the risk of giving flawed recommendations to the design engineer if the training data has some suspect data. In addition, the complexity of porting simulation data back and forth to a Machine Learning software can make the process cumbersome for the average CAE engineer to set up and execute a ML project. We would like to put forth a ML workflow/platform that a typical CAE engineer can use to create training data, train a PINN (Physics Informed Neural Network) ML model and use it to predict, optimize and even synthesize for any given crash problem. The key enabler is the use of an industry first data structure named mwplot that can store diverse types of training data - scalars, vectors
E-mobility is revolutionizing the automotive industry by improving energy-efficiency, lowering CO2 and non-exhaust emissions, innovating driving and propulsion technologies, redefining the hardware-software-ratio in the vehicle development, facilitating new business models, and transforming the market circumstances for electric vehicles (EVs) in passenger mobility and freight transportation. Ongoing R&D action is leading to an uptake of affordable and more energy-efficient EVs for the public at large through the development of innovative and user-centric solutions, optimized system concepts and components sizing, and increased passenger safety. Moreover, technological EV optimizations and investigations on thermal and energy management systems as well as the modularization of multiple EV functionalities result in driving range maximization, driving comfort improvement, and greater user-centricity. This paper presents the latest advancements of multiple EU-funded research projects under
A hierarchical control architecture is commonly employed in hybrid torque control, where the supervisor CPU oversees system-level objectives, while the slave CPU manages lower-level control tasks. Frequently, control authority must be transferred between the two to achieve optimal coordination and synchronization. When a closed-loop component is utilized, accurately determining its actual contribution to the controlled system can be challenging. This is because closed-loop components are often designed to compensate for unknown dynamics, component variations, and actuation uncertainties. This paper presents a novel approach to closed-loop component factor transfer and coordination between two CPUs operating at different hierarchical levels within a complex system. The proposed framework enables seamless control authority transition between the supervisor and slave CPUs, ensuring optimal system performance and robustness. To mitigate disturbances and uncertainties during the transition
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
In an era where technological advancements are rapid and constant, the U.S. Army will need a more agile and efficient approach to modernizing systems on succeeding generations of Army vehicles. Legacy platforms like Abrams, Stryker, and Bradley vehicles use multiple mission computers tied to individual sensors that often required the addition of “boxes” to accommodate new capabilities, which could take years to deploy and drove sustainment costs up due to vendor lock. In addition, this antiquated approach doesn't leverage data to converge effects across the formation in a multi-domain environment. Centralized, common computing as detailed in GCIA would help solve this problem, potentially linking all major subsystems and providing higher-speed processing to assess large datasets in real time with AI and ML algorithms. By using a common, open architecture computer, the Army will be able to rapidly integrate new capabilities inside one box, versus adding multiple boxes. This pivotal
Artificial intelligence (AI) and machine learning (ML) are being adopted and deployed across the global aerospace and defense industry in a wide variety of software and hardware-defined applications right now. Here are five startups developing new and novel AI and ML technologies for aerospace and defense applications. This list is not intended to be in a ranking order.
Nestled in a commercial park in Sunnyvale, California, sits the Mercedes-Benz research and development North America office. A spinning star sits in the front of the building. It is one of six locations across North America and joins research facilities in Asia and Europe. During a recent media roundtable, Mercedes-Benz CEO Ola Källenius told journalists that the original purpose for the facility 30 years ago was because it recognized that Silicon Valley was a unique place where top academia meets with venture capital and where smart people from around the world gather. “So the very first intent with the first few baby steps of coming to Silicon Valley was like, it's almost like you send out a group of people to do reconnaissance, create contact, be part of the conversation, and figure out what's going on,” Källenius said.
Items per page:
50
1 – 50 of 6293