With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. 

pdf/generative-ai-for-unmanned-vehicle-swarms-challenges-22jhg9pia5.pdf

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and
  Opportunities

Cyber-Physical Systems, as a discipline, is relatively new, appearing prominently between 2000-2010, but has rapidly made contributions in many disciplines. The aerospace industry has been a primary application domain for CPS from the beginning, with much focus on autonomous systems, particularly Unmanned Aircraft Systems. Aerospace systems provide an opportune, complex, safety-critical system on which to prove out CPS research, techniques, and strategies where robustness, agility, and provable performance guarantees are important. In this paper we survey and discuss the synthesis of core CPS research areas into four key areas of autonomous aerospace systems. We illuminate how these have been applied across aviation and space systems that interface with human operators and/or bystanders. We also discuss how computing and select CPS concepts more prominently applied in other applications could be applied in aerospace systems. Finally, we elaborate on existing shortcomings in cyber-physical aerospace systems and propose future avenues of exploration and application to pursue. 

Impact and Influence of Cyber-Physical Systems Research on Autonomous Aerospace Systems

In the realm of decision-making for defense and security applications, it is paramount to swiftly and accurately identify the intentions of incoming swarms. Conventional identification methods predominantly focus on single-target applications and overlook the perturbations introduced by measurement noise. In this study, we propose a novel concept: the Dynamic Distribution Probability (DDP) image, which is constructed using the estimated state and its covariance matrix. Each grayscale pixel value within the image signifies the probability of the presence of the agent within the swarm. Our proposed identification scheme integrates the use of Extended Kalman Filter (EKF), Convolutional Neural Network (CNN), Back Propagation (BP) network, and Gated Recurrent Unit (GRU) network. Specifically, the DDP image is processed through a CNN to distill the formation characteristics, and the estimated swarm state from EKF is inputted into a BP network to deduce the kinematic information. The outputs from both networks are summed and subsequently channeled into a GRU network to capture temporal dynamics. Extensive numerical simulations and flight experiments are presented to demonstrate the robust anti-noise capability of the proposed scheme compared with conventional methods, as well as its superior training efficiency. 

Swarm intention identification via dynamic distribution probability image

Reinforcement learning-based path planning for multi-agent systems of varying size constitutes a research topic with increasing significance as progress in domains such as urban air mobility and autonomous aerial vehicles continues. Reinforcement learning with continuous state and action spaces is used to train a policy network that accommodates desirable path planning behaviors and can be used for time-critical applications. A Long Short-Term Memory module is proposed to encode an unspecified number of states for a varying, indefinite number of agents. The described training strategies and policy architecture lead to a guidance that scales to an infinite number of agents and unlimited physical dimensions, although training takes place at a smaller scale. The guidance is implemented on a low-cost, off-theshelf onboard computer. The feasibility of the proposed approach is validated by presenting flight test results of up to four drones, autonomously navigating collision-free in a real-world environment.

Long Short-Term Memory for Spatial Encoding in Multi-Agent Path Planning

Safe and reliable state estimation techniques are a critical component of next-generation robotic systems. Agents in such systems must be able to reason about the intentions and trajectories of other agents for safe and efficient motion planning. However, classical state estimation techniques such as Gaussian filters often lack the expressive power to represent complex underlying distributions, especially if the system dynamics are highly nonlinear or if the interaction outcomes are multi-modal. In this work, we use normalizing flows to learn an expressive representation of the belief over an agent's true state. Furthermore, we improve upon existing architectures for normalizing flows by using more expressive deep neural network architectures to parameterize the flow. We evaluate our method on two robotic state estimation tasks and show that our approach outperforms both classical and modern deep learning-based state estimation baselines.

pdf/deep-normalizing-flows-for-state-estimation-3ujby952.pdf

Deep Normalizing Flows for State Estimation

Importance sampling is a Monte Carlo technique for efficiently estimating the likelihood of rare events by biasing the sampling distribution towards the rare event of interest. By drawing weighted samples from a learned proposal distribution, importance sampling allows for more sample-efficient estimation of rare events or tails of distributions. A common choice of proposal density is a Gaussian mixture model (GMM). However, estimating full-rank GMM covariance matrices in high dimensions is a challenging task due to numerical instabilities. In this work, we propose using mixtures of probabilistic principal component analyzers (MPPCA) as the parametric proposal density for importance sampling methods. MPPCA models are a type of low-rank mixture model that can be fit quickly using expectation-maximization, even in high-dimensional spaces. We validate our method on three simulated systems, demonstrating consistent gains in sample efficiency and quality of failure distribution characterization.

https://export.arxiv.org/pdf/2505.13335v1.pdf

Scalable Importance Sampling in High Dimensions with Low-Rank Mixture Proposals

Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capturing a wide variety of potential failures. Our model does not require any external training dataset, can perform training and inference with modest computing resources, and does not assume any prior knowledge of the system under test, with applicability to safety validation for traffic intersections.

Diffusion Models for Safety Validation of Autonomous Driving Systems

Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the cost of running many accurate simulation can be high. The validation can be accelerated by identifying critical failure scenarios for the system under test and by reducing the simulation runtime. We propose a Bayesian approach that integrates meta-learning strategies with a multi-armed bandit framework. Our method involves learning distributions over scenario parameters that are prone to triggering failures in the system under test, as well as a distribution over fidelity settings that enable fast and accurate simulations. In the spirit of meta-learning, we also assess whether the learned fidelity settings distribution facilitates faster learning of the scenario parameter distributions for new scenarios. We showcase our methodology using a cutting-edge 3D driving simulator, incorporating 16 fidelity settings for an autonomous vehicle stack that includes camera and lidar sensors. We evaluate various scenarios based on an autonomous vehicle pre-crash typology. As a result, our approach achieves a significant speedup, up to 18 times faster compared to traditional methods that solely rely on a high-fidelity simulator.

SAVME: Efficient Safety Validation for Autonomous Systems Using Meta-Learning

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