Browse Topic: Planning / scheduling
ABSTRACT The current reliability growth planning model used by the US Army, the Planning Model for Projection Methodology (PM2), is insufficient for the needs of the Army. This paper will detail the limitations of PM2 that cause Army programs to develop reliability growth plans that incorporate unrealistic assumptions and often demand that infeasible levels of reliability be achieved. In addition to this, another reliability growth planning model being developed to address some of these limitations, the Bayesian Continuous Planning Model (BCPM), will be discussed along with its own limitations. This paper will also cover a third reliability growth planning model that is being developed which incorporates the advantageous features of PM2 and BCPM but replaces the unrealistic assumptions with more realistic and customizable ones. The internal workings of this new TARDEC developed simulation-based model will be delved into with a focus on the advantages this model holds over PM2 and BCPM
ABSTRACT This GVSETS paper outlines the strategy for integrating Digital Engineering (DE) practices into the Detroit Arsenal (DTA) acquisition, engineering, and sustainment communities. A DTA DE Community of Practice (CoP) is being led by Program Executive Office (PEO) Ground Combat Systems (GCS), PEO Combat Support & Combat Service Support (CS&CSS), Combat Capabilities Development Command (DEVCOM) Ground Vehicles Systems Center (GVSC), and Tank-Automotive & Armaments Command (TACOM). In addition, Program Management Offices (PMOs) will document their DE implementation plans as part of all planning documents per Assistant Secretary of the Army for Acquisition, Logistics & Technology (ASA[ALT]) guidance [1]. In this paper, each of the DTA organizations will address the following: Ongoing DE Related Efforts; Upcoming / Planned Efforts / Opportunities; Lessons Learned; and Challenges / Issues / Help Needed. Additionally, each DTA organization explains its current and future states along
Abstract: The Team Cybernet vehicle for the 2007 DARPA Urban Challenge1 incorporated a route planning approach that uses sensed obstacles in the environment as the basis for potential turn placement prior to performing path search. The path search is confined to finding a set of straight-line tangents that connect circles of maximum curvature that are constructed adjacent to sensed obstacles. This approach is substantially different from traditional approaches in that the complexity of the search space is not based on the length of the path, but rather on the number of obstacles in the field. For sparse obstacle fields, this approach allows for very fast plan generation and results in paths that are guaranteed by construction to not violate steering constraints
ABSTRACT Off-road autonomous navigation poses a challenging problem, as the surrounding terrain is usually unknown, the support surface the vehicle must traverse cannot be considered flat, and environmental features (such as vegetation and water) make it difficult to estimate the support surface elevation. This paper will focus on Robotic Research’s suite of off-road autonomous planning and obstacle avoidance tools. Specifically, this paper will provide an overview of our terrain detection system, which utilizes advanced LADAR processing techniques to provide an estimate of the surface. Additionally, it will describe the kino-dynamic off-road planner which can, in real-time, calculate the optimal route, taking into account the support surface, obstacles sensed in the environment, and more. Finally, the paper will explore how these technologies have been applied to a wide variety of different robotic applications
ABSTRACT Systems Engineering (SE) would always benefit from the inclusion of the Six-Sigma perspective in both the planning and execution of project systems. This applies to not only System Engineers but also to Systems Extended Team Members, all who must provide cumulated knowledge along with competency to the project. It is difficult to obtain a high level of competency among single members of the team to be highly successful. Strength in one area is very often an underlying factor of weakness in another area. Determining and integrating sigma characteristics from the development cycle into all remaining phases of the product project, especially at critical component interfaces, with a resultant sigma value given to those connections that develop a sigma-risk factor for each function and process pathway within the operational configuration. This sigma-risk factor concept is the key in uniting knowledge with experience
ABSTRACT In the Tank Automotive Research Development and Engineering Center (TARDEC) Phase II SBIR “Autonomy and Visualization Enhancement for Situational Awareness” (AVESA) program, Robotic Research, LLC has developed a camera-based intelligence and reconnaissance tool to address the needs of warfighters on the battlefield. The RR-Flashback system developed under this program provides a hardware and software solution that captures, time tags, and geo-references panoramic imagery, along with a spatiotemporal imagery database for use in mission planning, intelligence analysis, and detecting changes in the environment
ABSTRACT The Product Director for Contingency Base Infrastructure (PD CBI) is chartered to bring a system-of-systems approach to contingency basing. PD CBI has four major lines of effort to accomplish the mission. This paper briefly touches on the Strategic Recommendations, Analytical Support, and Stakeholder Collaboration and Integration lines of effort and focuses on the Contingency Basing Interface to the Warfighter line of effort. The paper outlines the Model-Based Systems Engineering (MBSE) approach employed by the CBI team, detailing the application of a common set of tools to address each part of the problem. The paper also addresses the use of existing models and simulations, modifying them for use with base infrastructure materiel, and developing new tools as needed, to conduct analyses treating a contingency base as a system of systems (similar to a ground vehicle system). The results of the analyses will provide the Army with materiel investment recommendations for decision
Autonomous vehicle navigation requires signal processing of the vehicle’s sensors to provide meaningful information to the planners such that challenging artifacts like shadows, rare events, obstructive vegetation, etc. are identified properly, avoiding ill-informed navigation. Using a single algorithm such as semantic segmentation of camera images is often not enough to identify those challenging features but can be overcome by processing more than one type of sensor and fusing their results. In this work, semantic segmentation of camera image and LiDAR point cloud signals is performed using Echo State Networks to overcome the challenge of shadows identified as obstructions in off-road terrains. The coordination of algorithms processing multiple sensor signals is shown to avoid unnecessary road obstructions caused by high-contrast shadows for more informed navigational planning
Bringing a construction project from planning on the page to execution in the real world is replete with challenges. Whether a company is building a sprawling solar farm or laying lines on the road, precision is paramount. Misfires of just a few inches can have massive implications, and that often leads to a plodding layout process. But, in partnership with Point One, Civ Robotics is ensuring that precise construction layouts won’t be at odds with efficiency
Humans are generally good at whole-body manipulation, but robots struggle with such tasks. Now, MIT researchers have found a way to simplify this process, known as contact-rich manipulation planning. They use an AI technique called smoothing, which summarizes many contact events into a smaller number of decisions, to enable even a simple algorithm to quickly identify an effective manipulation plan for the robot
For decades, there has been a tug-of-war between many suppliers and their vehicle-manufacturer customers with respect to future planning volumes. The stakes are significant. Using volumes that are too high drives an extreme capital commitment and risk suppliers to stranded capital and missed opportunities to employ resources elsewhere. Using volumes that are too low means the OEM may miss potential sales and the supplier would be stressed with extreme overtime to keep up. It is a never-ending balance. OEMs often use internally built ‘Capacity Planning Volumes’ (CPVs) to ensure they capacitize to both their annual and peak volume expectations. These volumes are used as the divisor to understand per-part costs and how tooling, machines, infrastructure and other capitalized items are amortized over the life of the program. Suppliers often utilize third-party views such as the S&P Global Mobility Light Vehicle Production Forecasts to gain an impartial perspective of market dynamics, as
Aiming at the problem of weak communication, strong interference, cross-domain, and large-scale environment, it is difficult to achieve efficient decision-making and planning in the collaborative operation of intelligent groups. Based on the SOM algorithm, this paper proposes a dual-selection allocation and distributed vectorized trajectory planning. Form a collaborative planning algorithm that can be updated with high frequency and a rational decision-making mechanism. Provide technical support for collaborative search and detection of intelligent groups. At the same time, based on the principle of minimum consistency, this paper proposes a clock synchronization model under spatial coordination and conducts simulation experiments to verify it. The result proves the efficiency and practicability of the collaborative intelligent decision-making plan proposed in this paper
Directed energy deposition is of interest to the aerospace and defense industries for the production of novel and complex geometries, as well as repair applications. However, variability during the build process can result in deviations in final component geometry, structure, and mechanical properties, which adds to the complexity of process planning and slows down adoption of this technology
Due to its wide range of applications, multiagent UAV trajectory planning has been extensively studied. For reliable real-world deployment, it is essential that a trajectory planner be robust to both communication delays and dynamic environments; however, achieving robustness to both communication delays and dynamic environments has not been addressed in the literature. Multiagent trajectory planners can be centralized (one machine plans every agent’s trajectory) or decentralized (each agent plans its own trajectory). Decentralized planners are more scalable and robust to failures of the centralized machine. Despite these advantages, a decentralized scheme requires communication between the agents, and communication delays could potentially introduce failure in the trajectory deconfliction between the agents. It is also worth noting that there are two layers of decentralization— decentralized planning and decentralized communication architecture. Even if the planning algorithm is
The body stiffness plays a key role in vehicle performance, such as noise and vibration, ride and handling, durability and so on. In particular, a body D-pillar ring structure is the most sensitive affecting the body stiffness on vehicle with tail gate. Therefore, since D-pillar body ring structure for high stiffness and lightweight is required, an optimized design methodology that simultaneously satisfies the requirements was studied. It focused on a methodology that body engineering designers can optimize design parameters easily and quickly by themselves in the preceding stages of vehicle’s styling distribution and design conceptual planning. First, it is important to establish the body stiffness design strategy by predicting the body stiffness with the vehicle’s styling at early design stage. The methodology to predict body stiffness with the styling and body dimension specification parameters was introduced. Next, design parameters such as a cross-section area, material and
Just like us, robots can’t see through walls. Sometimes they need a little help to get where they’re going. Engineers at Rice University have developed a method that allows humans to help robots “see” their environments and carry out tasks
Efforts toward the mechanization of aircraft manufacturing began as a divided focus between devices like power tools that augment human worker capability and purpose-designed, “monument” automation. While both have benefits and limitations, the capability of modern industrial robots has grown to the point of being able to effectively fill the capability gap between them, offering a third option in the mechanization toolbox. Moreover, increasing computer processing power continues to enable more advanced approaches to perception to inform task planning and execution. Higher performance robots supplemented with greater ability to adapt to various conditions and scenarios have also led to the ability to operate reliably and safely outside traditional fixed-installation, caged work cells. This in turn has made it feasible for robot systems to work in ever more complex environments and applications, including the world of aircraft assembly with its numerous challenges like workpiece scale
They say experience is the best teacher, and the manufacturing sector has learned some hard lessons over the past two years. Unprecedented and unrelenting market turbulence has shown that the old ways of supply chain planning and manufacturing production are outdated and vulnerable to disruption. Manufacturers now have evidence of what happens to a “just-in-time” globe-spanning supply chain that operates without contingency planning
Accurate surrounding vehicle motion prediction is critical for enabling safe, high quality autonomous driving decision-making and motion planning. Aiming at the problem that the current deep learning-based trajectory prediction methods are not accurate and effective for extracting the interaction between vehicles and the road environment information, we design a target vehicle intention-aware dual attention network (IDAN), which establishes a multi-task learning framework combining intention network and trajectory prediction network, imposing dual constraints. The intention network generates an intention encoding representing the driver’s intention information. It inputs it into the attention module of the trajectory prediction network to assist the trajectory prediction network to achieve better prediction accuracy. The attention module in the trajectory prediction network mainly includes spatial attention module and channel attention module to reflect the relative importance of the
The SJD Barcelona Children’s Hospital’s pediatric maxillofacial surgery team has used 3D printing technology to successfully perform a complicated operation to resect a malignant tumor in an 11-year-old boy. Given the complexity of the operation, the medical team, led by Dr. Josep Rubio, head of the maxillofacial surgery unit at SJD, decided to carry out preoperative planning and simulation using BCN3D’s technology and 3D anatomical models of the parts of the patient’s skull
Transportation has significant and long-lasting economic, social and environmental impacts which makes it an important dimension of urban sustainability. The World is witnessing rapid changes in modern traveling behavior, and efforts are continuously being made to stimulate sustainable mobility solutions with smart policies, new business models, and advanced technologies (connected cars, sensors, electrification). However, the shift is gradual in India when compared to developed countries due to unique barriers to emerging green mobility solutions. This paper empirically investigates public travel satisfaction and the primary factors for the selection of modes for different types of commutes. Quantitative data were collected including socio-demographic, travel mode choices, and preferred future mobility solutions from the western states of India. This study analyses the correspondence between demography and traveling behavior for various types of commutes like daily work, intercity
One-way car-sharing services (CSSs) are believed to be a promising transportation mode for urban mobility. Due to the disparity of city functional areas and population, travel demand and vehicle supply in a CSS may inevitably tend to be imbalanced as well. Therefore, an essential requirement of one-way CSSs is the capability of providing fleet management solutions to improve quality of service and system performance. In other words, a CSS depends heavily on technologies that offer strategic decisions on topics like Fleet sizing Location and capacity of depots and charging stations Matching of travelers with vehicles Relocation of vehicles and dispatchers for fleet rebalancing Balancing and charging schedules of electric vehicles Car-sharing Mobility-on-Demand Systems addresses trending CSS technologies and outlines some insights into the existing unsettled issues and potential solutions. The discussions and outlook are presented as a collection of key points encountered in system
Data is information that has been recorded in a form or format convenient to move or process. It is important to distinguish between data and the format. The format is a structured way to record information, such as engineering drawings and other documents, software, pictures, maps, sound, and animation. Some formats are open source, others proprietary. Regardless of the format, there are three broad types of data. Table 1 lists these types of data and provides examples. DM, from the perspective of this standard, consists of the disciplined processes and systems utilized to plan for, acquire, and provide management and oversight for product and product-related business data, consistent with requirements, throughout the product and data life cycles. Thus, this standard primarily addresses product data and the business data required for stakeholder collaboration extending through the supply chain during product acquisition and sustainment life cycle. This standard has broader application
This standard applies to the aerospace and defense industries and their supply chains
As robots increasingly join people on the factory floor, in warehouses and elsewhere on the job, dividing up who will do which tasks grows in complexity and importance. Researchers at Carnegie Mellon University’s Robotics Institute have developed an algorithmic planner that helps delegate tasks to humans and robots. The planner, “Act, Delegate or Learn” (ADL), considers a list of tasks and decides how best to assign them. The researchers asked three questions: When should a robot act to complete a task? When should a task be delegated to a human? And when should a robot learn a new task
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