UAV swarms have emerged as transformative systems for complex missions including wildfire surveillance ( Julian and Kochenderfer 2019), intelligence surveillance and reconnaissance ( Kolar 2020), situational awareness ( Scharre 2018), and cooperative interception ( Balhance et al. 2017). In these applications, trajectory optimization is the cornerstone for ensuring both mission success and operational s afety ( Sezer 2022; Qian et al. 2020; Sanchez-Lopez et al. 2020). Over the past decade, trajectory optimization techniques hav e evolved from sophisticated single-agent formulations to distributed multi-agent frameworks, driven by the increasing scale and complexity of swarm-based missions ( Saravanos et al. 2023). For individual UAV trajectory optimization, a variety of numerical m ethods have demonstrated strong performance. Pseudospectral methods achieve high-accuracy solution s by discretizing continuous-time problems ( Chai et al. 2017), while sequential quadratic programming (SQP) ( Hong et al. 2021) and sequential convex programming (SCP) ( Deligiannis et al. 2019) provide flexible tools for handling nonlinear dynamics and constraint s.

Motion planning and trajectory generation are crucial technologies in various domains including the control of Unmanned Aerial Vehicles (UAV), manipulators, and rockets. However, optimization-based real-time motion planning becomes increasingly challenging due to the problem's probable non-convexity and the inherent limitations of Non-Linear Programming algorithms. Highly nonlinear dynamics, obstacle avoidance constraints, and non-convex inputs can exacerbate these difficulties. To address these hurdles, this paper proposes a two-layer optimization algorithm for 2D vehicles by dynamically reformulating small time horizon convex programming subproblems, aiming to provide real-time guarantees for trajectory optimization. Our approach involves breaking down the original problem into small horizon-based planning cycles with fixed final times, referred to as planning cycles. Each planning cycle is then solved within a series of restricted convex sets identified by our customized search algorithms incrementally. The key benefits of our proposed algorithm include fast computation speeds and lower task time. We demonstrate these advantages through mathematical proofs under some moderate preconditions and experimental results.

Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.

The games industry has been doing its best impression of British springtime's bewildering mix of sunshine and torrential rain with its own rapid cycles of joy and sadness. Holding up the joy end were two magnificent follies: a man managed to get stupid single-button-pressing game Flappy Bird to play on the screen of an e-cigarette, and someone else installed Windows 95 on an Apple Watch. But in that same month we also lost seminal British development studio Lionhead. It was responsible for all-time classics like giant pet-raising game Black & White, and Fable, an RPG that used the full gamut of English regional accents, as well as eccentricities such as The Movies, in which you could produce entire miniature feature films. Lionhead was latterly bought by Microsoft, and suffered the accidental destruction that large corporations routinely inflict on small, quirky developers. Electronic Arts, another well-meaning but lethal behemoth, has acquired and inadvertently milked to death nearly a dozen smaller studios in the last two decades, from SimCity developer Maxis to Westwood, maker of the once-great Command & Conquer series.