Browse Topic: Planning / scheduling
The safe integration of Unmanned Aerial Vehicles (UAVs) into shared airspace necessitates robust conflict detection and avoid (DAA) methods that scale effectively with multiple dynamic intruders. Geometric methods, such as those in the DO-365 standard, are provably safe for pairwise encounters but become intractable in dense environments. Conversely, applying kinodynamic motion planners designed for static obstacles to dynamic scenarios leads to unstable behavior, characterized by excessive re-planning and oscillatory motion, as they lack a predictive model of intruder trajectories. This paper introduces a closed-loop planning framework based on the Closed-Loop Rapidly-exploring Random Tree* (CL-RRT*) algorithm to prevent Loss of Well-Clear (LoWC) in multi-intruder scenarios. Our approach integrates a closed-loop dynamics model to guarantee dynamically feasible trajectories and incorporates a spatiotemporal planning strategy. A time-to-come metric is propagated from the tree root to all nodes, enabling prediction of the state and time at future trajectory points. Predicted states are continuously evaluated against known intruder trajectories (from ADS-B or perception system) using the formal DO-365 well-clear criteria, checking each point against the Hazard Area Zone (HAZ) via Horizontal Miss Distance (HMD) and Distance-Modification-for-Tau (DMOD) metrics. Simulations demonstrate that the proposed planner successfully generates safe and feasible trajectories that prevent LoWC in complex multi-intruder scenarios.
The rapid expansion of electric aviation and eVTOL operations introduces tightly coupled challenges related to energy‑constrained aircraft design, battery and thermal management, mission planning, and the generation of certification‑relevant evidence. This paper presents an integrated simulation workflow developed by AVL, Unisphere, and blueflite that combines high‑fidelity electric powertrain and battery models with a guidance‑level, digital‑twin‑based 4‑D trajectory simulation driven by historical weather and operational constraints. At each mission time step, the trajectory layer provides time‑resolved environmental and routing conditions, while the system‑level models compute instantaneous power demand, state‑of‑charge evolution, and thermal response, enabling mission feasibility assessment under realistic wind, temperature, and airspace effects. The workflow is calibrated and validated using flight telemetry from blueflite's active eVTOL cargo aircraft development, ensuring alignment between simulation assumptions and real‑world mission execution. The validated framework is subsequently applied to seasonal route studies and large‑scale virtual flight campaigns spanning multiple regions and years, enabling statistically robust assessment of energy margins, thermal behavior, and mission‑duration variability. The results demonstrate how integrated, traceable simulation can bridge conceptual design and real‑world electric flight operations, supporting informed decision‑making by OEMs and operators in aircraft design, validation, and deployment planning.
Developing efficient fast-charging infrastructure along highway corridors is critical for reducing range anxiety and promoting long-distance electric travel. However, traditional static location approaches often fail to account for the stochastic interactions between continuous traffic flows and the stochastic variability of remaining driving ranges. To address these methodological gaps, this study develops a demand-driven optimization framework that integrates an improved Genetic Algorithm with the flow-capturing location-allocation model (GA-FCLM). Unlike static facility location approaches, the flow-capturing location-allocation component is specifically selected to maximize the interception of continuous traffic flows under strict range constraints, while the genetic algorithm efficiently navigates the high-dimensional discrete search space of simultaneous siting and sizing decisions. By synthesizing segment-level traffic flows with Monte Carlo simulations of state of charge (SOC) trajectories, the model accurately reconstructs corridor-level charging demand. For the Beijing-Hong Kong-Macao Expressway, the optimization identifies a robust layout with twenty active stations and 204 fast chargers, requiring a capital investment of 32 million Chinese Yuan (CNY). This configuration achieves a 99% aggregate coverage ratio, effectively eliminating long uncovered segments. Sensitivity analysis reveals that while profit increases linearly with pricing, infrastructure capacity exhibits a nonlinear response to rising electric vehicle (EV) penetration, necessitating strategic spatial rebalancing. The proposed GA-FCLM framework thus provides a scalable and methodologically superior tool for balancing investment costs, coverage continuity, and spatial equity on national highway networks.
Recent geopolitical events in Venezuela, Ukraine and other hot spots are a stark reminder that the long-term planning environment is fraught with challenges and opportunities that suppliers cannot control. The initiation of U.S. tariffs on its trading partners and various embargos also underscored that we have to be flexible in how we dole out capital and the risk we are assuming. The supply base is at the end of that chain. Any issues upstream will reverberate exponentially. It is obvious that the automotive world is re-regionalizing, and quickly. Why the concern? Some context. Until the '70s, every region essentially rowed its own boat. While there were some exports from one major region to another, there were regional OEMs that were sponsored by national governments due to job creation, tax base considerations and bragging rights. The U.S, France, Italy, Germany, Japan, South Korea and a host of others wanted to build national OEMs that could drive scale and become a global force.
Achieving noise reduction in rotorcraft requires an analysis of various design parameters and flight conditions. However, high-fidelity methods are computationally expensive. To overcome this limitation, reduced order model (ROM)-based surrogate models have been applied to aerodynamics and aeroacoustics prediction. This study proposes a ROM-based surrogate model employing a variational autoencoder (VAE) to predict rotor aerodynamic loads and associated noise. Train and test datasets were generated using reformulated vortex particle method across a wide range of flight conditions. The proposed framework was applied to a single rotor, and its performance was evaluated qualitatively and quantitively in comparison with proper orthogonal decomposition (POD)-based surrogate model. The results show that VAE-based model consistently outperformed the POD model in noise prediction. These results demonstrate that the proposed framework enables accurate rotor noise prediction under various flight conditions and provides a promising approach for low-noise rotorcraft design and operational planning.
We present a novel processing approach to extract a ship traffic flow framework in order to cope with problems such as large volume, high noise levels and complexity spatio-temporal nature of AIS data. We preprocess AIS data using covariance matrix-based abnormal data filtering, develop improved Douglas-Peucker (DP) algorithm for multi-granularity trajectory compression, identify navigation hotspots and intersections using density-based spatial clustering and visualize chart overlays using Mercator projection. In experiments with AIS data from the Laotieshan waters in the Bohai Bay, we achieve compression rate up to 97% while maintaining a key trajectory feature retention error less than 0.15 nautical miles. We identify critical areas such as waterway intersections and generate traffic flow heatmap for maritime management, route planning, etc.
Large farms cultivating forage crops for the dairy and livestock sectors require high-quality, dense bales with substantial nutritional value. The storage of hay becomes essential during the colder winter months when grass growth and field conditions are unsuitable for animal grazing. Bale weight serves as a critical parameter for assessing field yields, managing inventory, and facilitating fair trade within the industry. The agricultural sector increasingly demands innovative solutions to enhance efficiency and productivity while minimizing the overhead costs associated with advanced systems. Recent weighing system solutions rely heavily on load cells mounted inside baling machines, adding extra costs, complexity and weight to the equipment. This paper addresses the need to mitigate these issues by implementing an advanced model-based weighing system that operates without the use of load cells, specifically designed for round baler machines. The weighing solution utilizes mathematical models and dynamic torque monitoring techniques to estimate the weight of bales immediately after the bale wrap process, before the bale is dropped onto the field. With the capability to function effectively in off-road conditions and diverse terrains, this system represents a substantial technological advancement that addresses the evolving challenges of modern agriculture. By demonstrating the potential of this design, the paper illustrates how advanced engineering solutions can enhance resource allocation, optimize feed management and distribution, support long-term planning, and contribute to the sustainability of agricultural practices without incurring additional costs. The weight of each bale can be used by farmers to analyze the current harvest based on bale weight variability and to make improvements before the next harvest. It also aids in key decision-making processes such as bale handling, transportation, sales and storage for future seasons. This advancement has significant advantages for scalability and profitability, allowing for optimized decision-making processes in agricultural operations.
Cargo Routing Problem or Container Allocation Problem is key decision-making challenge in the maritime industry at operational level. Existing research focus on static environment or planning decisions, ignoring the dynamic arrival property of shipping request in practical world. In this paper, we introduced the Online Cargo Routing problem and formulation the path-based models under a space-time network. We proposed an online algorithm under the online primal-dual scheme: re-solving strategy. We further conducted simulation experiments under different demand distributions to demonstrate the performance of the proposed algorithm over the offline baselines.
The high rate of structural changes to the North American Light Vehicle market demands a new approach by the supply base towards strategic planning. A new Supplier Strategy Playbook is in order. First, some historical perspective. For the last several decades, suppliers grew accustomed to a product cadence of approximately five years between all-new platforms and major revisions. In North America, we were constantly pressed to continue improving vehicle efficiency and reduce emissions. Improved powertrain efficiency, vehicle lightweighting, and the advent of enhanced aerodynamics helped an industry that required constant innovation. Additionally, many programs were global in scope, requiring production and tooling in the major regions to launch in close sequence with global scale in tow. Wash, Rinse, Repeat. The textbook for suppliers was complex, though relatively predictable.
Employment of Robotic and Autonomous Systems requires a different paradigm of mission planning, one which considers not only the tasks to be performed by the RAS themselves but regards the flow of information to support the observability of the RAS by the operator. GTRI has developed an initial capability for mission planning of mixed motive, heterogeneous, autonomous systems for management of macro level metrics that support the decision making of the operator or user during employment. The work is ongoing, extensible to additional capability sets, and modular to support integration of other autonomous capabilities.
September is unofficially known in the industry as a key forecasting month. It's when several suppliers lock in their revenue forecasts for the next year. As we approach 2026, there are still several balls in the air with respect to the trajectory of the light vehicle market. Looming U.S. tariffs, negative economic and geo-political shifts, and the impact of changes to U.S. vehicle emission legislation have all brought with them a cloud of uncertainty that hovers over the industry. An industry that requires greater planning clarity, not less. Let's start with the tariffs. As of this writing, the major vehicle and parts importers outside of North America have agreed to 15% U.S. tariffs for vehicles and parts. In the case of Japan and the European Union, this is 12.5 percentage points higher than 2024 levels. In the case of South Korea, it's 15 points more, as there was a free trade agreement in force. While these framework agreements drive some level of certainty, the final details still need to be hashed out.
The automotive industry faces the challenge of developing vehicles that meet current customer needs while being future-proof. Surveys conducted for this study show that customers are concerned about the financial risks of essential components such as energy storage systems, mainly due to aging and performance degradation, which significantly affect vehicle lifespans. Based on vehicle developer surveys, a clear need for action was identified. Given the rapid technological advancements in electrified drive systems, there is a need for innovative approaches that can easily adapt to changing requirements. Therefore, this paper presents a strategy combining foresight-based planning of system upgrades with product architecture design to create adaptable and sustainable vehicles through modularity. First, dynamic subsystem characteristics are identified to establish future energy storage technology requirements. Subsequently, future energy storage system technologies are examined to determine those that meet the identified dynamic characteristics. Based on this information, the technologies are analyzed technically-functional and geometrically to create flexible design spaces within the product architecture. This enables the future integration of new, more efficient, or higher-performance energy storage technologies into vehicles during their utilization phase. The integrability and functional efficacy of the selected technologies are assessed through a combination of impact and criticality analysis based on virtual modeling, resulting in a ranking of the most suitable energy storage technologies. Implementing upgradable mechatronic systems during the development process already considers future requirements. The result is a product architecture with flexible design spaces and standardized interfaces that facilitate the integration of future performance-adapted technologies. This enhances the sustainability of vehicles, extends their service life, and improves resale value, benefiting both customers and manufacturers.
Time-Sensitive Networking (TSN) is an emerging technology that has garnered popularity among the US DoD and others for its deterministic properties while using flexible, ubiquitous Ethernet as its core. However, individual TSN devices will support the TSN features of only some of the vast array of amendments and extensions that make up the full IEEE 802 TSN standards. This functional and modular approach offers great flexibility, but it also increases the complexity of network planning, analysis, verification, etc. as well as potentially leading to unexpected emergent behavior that must be addressed before a TSN network can be truly said to be qualified for use with safety-critical systems. Using industry experience gained certifying other deterministic networks to DO-254 and DO-178C Design Assurance Level A (DAL-A) and applying it to the analysis, testing, and validation of a deterministic TSN Ethernet digital backbone offers a roadmap for overcoming these challenges. Such an approach must seek to satisfy the three basic building-blocks of 1) Device-Level Standards Conformance, 2) System-Level Performance and Interoperability, and 3) Network Composability and Determinism.
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 significant to the success of cost reduction strategies. Brand reputation and customer perceptions of quality must be safeguarded when changes are implemented. To that end, communication strategies to convey the benefits of cost reduction without compromising perceived value are a key factor to enhance market acceptance. Another crucial element in the execution of cost reduction projects is operational efficiency. Streamlining production processes, optimizing supply chain logistics, and embracing automation technologies require careful planning and implementation to avoid disruptions in production schedules. In conclusion, addressing the challenges of reducing costs in automotive body exterior parts demands a holistic approach that considers financial, functional, and marketing aspects. Striking the right balance between these elements is essential for the success of cost reduction initiatives, ensuring that the automotive industry remains competitive while meeting the demands of a cost-conscious market. A solid technical background for all the parties involved is imperative, and this text provides an overview of the pivotal topics in that context.
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