Browse Topic: Planning / scheduling
The automotive industry is facing unprecedented pressure to reduce costs without compromising on quality and performance, particularly in the design and manufacturing. This paper provides a technical review of the multifaceted challenges involved in achieving cost efficiency while maintaining financial viability, functional integrity, and market competitiveness. Financial viability stands as a primary obstacle in cost reduction projects. The demand for innovative products needs to be balanced with the need for affordable materials while maintaining structural integrity. Suppliers’ cost structures, raw material fluctuations, and production volumes must be considered on the way to obtain optimal costs. Functional aspects lead to another layer of complexity, once changes in design or materials should not compromise safety, durability, or performance. Rigorous testing and simulation tools are indispensable to validate changes in the manufacturing process. Marketing considerations are also
From a mission operations perspective, swarms pose a planning challenge due to the limited scalability of ground operations. The capabilities needed to support swarm missions go beyond operator-specified geometry, alignment, or separation, but also crosslink communication with maintaining position in the formation. To address scalable control of orbital dynamics, NASA Ames Research Center has patented Swarm Orbital Dynamics Advisor (SODA) — a solution that accepts high-level configuration commands and provides the orbital maneuvers needed to achieve the desired type of swarm relative motion.
The impact of the upcoming U.S. federal election, global trade turmoil, a mediocre U.S. economy and the slumping ICE-to-EV (internal combustion engine to electric vehicle) transition must be considered. In my last column, we explored the growing use of scenarios to provide guardrails for future strategy. Suppliers can no longer rely upon a single forecast to drive future planning. The main culprits clouding the planning environment are program delays, rescopes and EV strategy shifts accompanied by the extension of ICE/hybrid models. The trajectory of EV launches and new offerings is decidedly ahead of the skis of consumer acceptance. This supply-and-demand mismatch is an ongoing challenge. It is important to understand the severity of program changes amid this slowing EV growth environment.
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.
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.
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
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
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.
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