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In today’s highly competitive and economically driven commercial aviation market, the trend is to make aircraft systems simpler and to design and develop them faster resulting in lower production and operational costs. One such system is the high‐lift system. A methodology has been developed which merges aerodynamic data with kinematic analysis of the trailing‐edge flap mechanism with minimum mechanism definition required. This methodology provides quick and accurate aerodynamic performance prediction of the flap deployment mechanism early on in the high‐lift system preliminary design stage. Sample analysis results for four different deployment mechanisms are presented as well as descriptions of the aerodynamic and mechanism data required for evaluation. Extensions to interactive design capabilities are also discussed.

Abstract-We present a distributed control mechanism allowing a swarm of non-holonomic autonomous surface vehicles (ASVs) to synchronously arrange around a rectangular floating object in a grasping formation; the swarm is then able to collaboratively transport the object to a desired final position and orientation. We analytically consider the problem of synchronizing the ASVs' arrival at the object, with regard to their initial random positions. We further analytically construct a set of acceptable trajectories, allowing the transport of the grasped object to its final desired position. Numerical simulations illustrate the performance of the presented control mechanism. We present experimental results, to demonstrate the feasibility and relevance of our strategy.

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Multi-agent foraging (MAF) involves distributing a team of agents to search an environment and extract resources from it. Many foraging algorithms use biologicallyinspired signaling mechanisms, such as pheromones, to help agents navigate from resources back to a central nest while relying on local sensing only. However, these approaches often rely on predictable pheromone dynamics and/or perfect robot localization. In nature, certain environmental factors (e.g., heat or rain) can disturb or destroy pheromone trails, while imperfect sensing can lead robots astray. In this work, we propose ForMIC, a distributed reinforcement learning MAF approach that relies on pheromones as a way to endow agents with implicit communication abilities via their shared environment. Specifically, full agents involuntarily lay trails of pheromones as they move; other agents can then measure the local levels of pheromones to guide their individual decisions. We show how these stigmergic interactions among agents can lead to a highlyscalable, decentralized MAF policy that is naturally resilient to common environmental disturbances, such as depleting resources and sudden pheromone disappearance. We present simulation results that compare our learning policy against existing stateof-the-art MAF algorithms, in a set of experiments varying team sizes, number and placement of resources, and key environmental disturbances. Our results demonstrate that our learned policy outperforms these baselines, approaching the performance of a planner with full observability and centralized agent allocation.