arXiv subjects

Fei Gao

Publications and source records attributed to Fei Gao.

At least 127 records · Page 7Linked to original sources

arXiv2025

Number Adaptive Formation Flight Planning via Affine Deformable Guidance in Narrow Environments

Formation maintenance with varying number of drones in narrow environments hinders the convergence of planning to the desired configurations. To address this challenge, this paper proposes a formation planning method guided by Deformable Virtual Structures (DVS) with continuous spatiotemporal transformation. Firstly, to satisfy swarm safety distance and preserve formation shape filling integrity for irregular formation geometries, we employ Lloyd algorithm for uniform $\underline{PA}$rtitioning and Hungarian algorithm for $\underline{AS}$signment (PAAS) in DVS. Subsequently, a spatiotemporal trajectory involving DVS is planned using primitive-based path search and nonlinear trajectory optimization. The DVS trajectory achieves adaptive transitions with respect to a varying number of drones while ensuring adaptability to narrow environments through affine transformation. Finally, each agent conducts distributed trajectory planning guided by desired spatiotemporal positions within the DVS, while incorporating collision avoidance and dynamic feasibility requirements. Our method enables up to 15\% of swarm numbers to join or leave in cluttered environments while rapidly restoring the desired formation shape in simulation. Compared to cutting-edge formation planning method, we demonstrate rapid formation recovery capacity and environmental adaptability. Real-world experiments validate the effectiveness and resilience of our formation planning method.

cs.RO↗

arXiv2025

A Low-cost Quasi-planar Array Probe for Photoacoustic Imaging

Photoacoustic imaging (PAI) is a novel hybrid imaging technique that combines the benefits of both optical and acoustic imaging modalities, which provides functional and molecular optical contrasts of deep tissue. Commonly used ultrasound transducers for PAI include linear and planar arrays, which can provide two-dimensional (2D) and three-dimensional (3D) image reconstruction, respectively. However, linear arrays cannot provide reconstruction of 3D images, which makes it impossible to locate chromophores in 3D space. Although planar array can provide fast 3D imaging in real time, it usually requires thousands of analog-to-digital conversion channels for data acquisition, which is costly. To fill the gap between 2D and 3D PAI, we propose a quasi-planar array that uses double 16-elements-linear arrays arranged in parallel to achieve real-time 3D imaging. We first conducted simulation studies to prove that the quasi-planar probe can perform 3D imaging to localize simple chromophores. Then, the agarose phantom experiment demonstrated that the probe can reconstruct 3D imaging of multiple absorbers in different depths. A potential application of this device is to provide a low-cost 3D PAI solution for fast tracking of needle tip during needle biopsy, which will be further explored in our future work.

physics.med-ph↗

arXiv2025

Embodied Navigation Foundation Model

Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments following language instructions. Despite significant progress in large Vision-Language Models (VLMs), which exhibit remarkable zero-shot performance on general vision-language tasks, their generalization ability in embodied navigation remains largely confined to narrow task settings and embodiment-specific architectures. In this work, we introduce a cross-embodiment and cross-task Navigation Foundation Model (NavFoM), trained on eight million navigation samples that encompass quadrupeds, drones, wheeled robots, and vehicles, and spanning diverse tasks such as vision-and-language navigation, object searching, target tracking, and autonomous driving. NavFoM employs a unified architecture that processes multimodal navigation inputs from varying camera configurations and navigation horizons. To accommodate diverse camera setups and temporal horizons, NavFoM incorporates identifier tokens that embed camera view information of embodiments and the temporal context of tasks. Furthermore, to meet the demands of real-world deployment, NavFoM controls all observation tokens using a dynamically adjusted sampling strategy under a limited token length budget. Extensive evaluations on public benchmarks demonstrate that our model achieves state-of-the-art or highly competitive performance across multiple navigation tasks and embodiments without requiring task-specific fine-tuning. Additional real-world experiments further confirm the strong generalization capability and practical applicability of our approach.

cs.RO↗

arXiv2025

Tracailer: An Efficient Trajectory Planner for Tractor-Trailer Robots in Unstructured Environments

The tractor-trailer robot consists of a drivable tractor and one or more non-drivable trailers connected via hitches. Compared to typical car-like robots, the addition of trailers provides greater transportation capability. However, this also complicates motion planning due to the robot's complex kinematics, high-dimensional state space, and deformable structure. To efficiently plan safe, time-optimal trajectories that adhere to the kinematic constraints of the robot and address the challenges posed by its unique features, this paper introduces a lightweight, compact, and high-order smooth trajectory representation for tractor-trailer robots. Based on it, we design an efficiently solvable spatial-temporal trajectory optimization problem. To deal with deformable structures, which leads to difficulties in collision avoidance, we fully leverage the collisionfree regions of the environment, directly applying deformations to trajectories in continuous space. This approach not requires constructing safe regions from the environment using convex approximations through collision-free seed points before each optimization, avoiding the loss of the solution space, thus reducing the dependency of the optimization on initial values. Moreover, a multi-terminal fast path search algorithm is proposed to generate the initial values for optimization. Extensive simulation experiments demonstrate that our approach achieves severalfold improvements in efficiency compared to existing algorithms, while also ensuring lower curvature and trajectory duration. Real-world experiments involving the transportation, loading and unloading of goods in both indoor and outdoor scenarios further validate the effectiveness of our method. The source code is accessible at https://github.com/Tracailer/Tracailer.

cs.RO↗

arXiv2025

Quantum-Assisted Recursive Algorithm for Solving the Exact Cover Problem

The exact cover problem is an NP-complete problem with broad applications. Studies show that although applying the Quantum Approximate Optimization Algorithm (QAOA) to this problem can yield improved solution quality with deeper circuit depth, it can limit the algorithm's applicability on noisy intermediate-scale quantum devices. To improve solution quality at shallow depth, we propose a Quantum-Assisted Recursive Algorithm (QARA) for solving the exact cover problem. QARA addresses the problem by alternately applying classical and quantum pruning. Classical pruning is a repeatable pre-processing step to simplify the problem. When the classical pruning cannot promote the problem simplification, quantum pruning is invoked. During quantum pruning, QARA extracts information from the QAOA's output state to identify the subset with the strongest selection bias. This subset is then used to prune the problem based on our problem-tailored reduction rules. Furthermore, QARA incorporates a local verification and rollback mechanism to assistively judge the effectiveness of the quantum simplification. After quantum pruning, classical pruning is applied again to the reduced problem if the remaining subsets and element set are not null. This alternating process repeats until the original problem is fully resolved. In our numerical simulations, we evaluate the performance of QARA at one-layer depth on 140 instances with subset sizes ranging from 8 to 20. Numerical results show that the probability of QARA in finding an exact solution is approximately 60\% higher than that of both QAOA and Recursive QAOA, highlighting its efficiency.

quant-ph↗

arXiv2025

Unbounded-input explicit Bell inequalities for general quantum networks

Quantum nonlocality in networks featuring multiple independent sources underpins large-scale quantum communication and poses fundamental challenges for its characterization. In this work, we construct a family of explicit nonlinear Bell inequalities to verify the nonlocality across the general multi-input quantum networks. The construction of these inequalities relies on the number of leaf nodes, a network parameter that can be identified by a linear-time algorithm. Our approach establishes a structural connection between bipartite full-correlation Bell inequalities and network Bell inequalities, enabling the analytical derivation of optimal quantum violations and the conditions under which they occur. We further quantify the upper bound on maximal violations achievable by arbitrary two-qubit mixed states in such networks, under separable measurements, and evaluate the noise robustness of the proposed inequalities via the visibilities of Werner states. Finally, we demonstrate that these inequalities can, in a device-independent manner, distinguish between network topologies of equal size that differ in the number of leaf nodes.

quant-ph↗

arXiv2025

Preparation and measurement of an $\rm ^{37}$Ar source for liquid xenon detector calibration

We present the preparation and measurement of the radioactive isotope $\rm ^{37}Ar$, which was produced using thermal neutrons from a reactor, as a calibration source for liquid xenon time projection chambers. $\rm ^{37}Ar$ is a low-energy calibration source with a half-life of 35.01 days, making it suitable for calibration in the low-energy region of liquid xenon dark-matter experiments. Radioactive isotope $\rm ^{37}Ar$ was produced by irradiating $\rm ^{36}Ar$ with thermal neutrons. It was subsequently measured in a gaseous xenon time projection chamber (GXe TPC) to validate its radioactivity. Our results demonstrate that $\rm ^{37}Ar$ is an effective and viable calibration source that offers precise calibration capabilities in the low-energy domain of xenon-based detectors.

physics.ins-det↗

arXiv2025

HDN: Hybrid Deep-Learning and Non-Line-of-Sight Reconstruction Framework for Transcranial Photoacoustic Imaging of Human Brain

Photoacoustic imaging combines the high contrast of optical imaging with the deep penetration depth of ultrasonic imaging, showing great potential in cerebrovascular disease detection. However, the ultrasonic wave suffers strong attenuation and multi-scattering when it passes through the skull tissue, resulting in the distortion of the collected photoacoustic signal. In this paper, inspired by the principles of deep learning and non-line-of-sight imaging, we propose an image reconstruction framework named HDN (Hybrid Deep-learning and Non-line-of-sight), which consists of the signal extraction part and difference utilization part. The signal extraction part is used to correct the distorted signal and reconstruct an initial image. The difference utilization part is used to make further use of the signal difference between the distorted signal and corrected signal, reconstructing the residual image between the initial image and the target image. The test results on a photoacoustic digital brain simulation dataset show that compared with the traditional method (delay-and-sum) and deep-learning-based method (UNet), the HDN achieved superior performance in both signal correction and image reconstruction. Specifically for the structural similarity index, the HDN reached 0.661 in imaging results, compared to 0.157 for the delay-and-sum method and 0.305 for the deep-learning-based method.

physics.med-ph↗

arXiv2025

Sem-RaDiff: Diffusion-Based 3D Radar Semantic Perception in Cluttered Agricultural Environments

Accurate and robust environmental perception is crucial for robot autonomous navigation. While current methods typically adopt optical sensors (e.g., camera, LiDAR) as primary sensing modalities, their susceptibility to visual occlusion often leads to degraded performance or complete system failure. In this paper, we focus on agricultural scenarios where robots are exposed to the risk of onboard sensor contamination. Leveraging radar's strong penetration capability, we introduce a radar-based 3D environmental perception framework as a viable alternative. It comprises three core modules designed for dense and accurate semantic perception: 1) Parallel frame accumulation to enhance signal-to-noise ratio of radar raw data. 2) A diffusion model-based hierarchical learning framework that first filters radar sidelobe artifacts then generates fine-grained 3D semantic point clouds. 3) A specifically designed sparse 3D network optimized for processing large-scale radar raw data. We conducted extensive benchmark comparisons and experimental evaluations on a self-built dataset collected in real-world agricultural field scenes. Results demonstrate that our method achieves superior structural and semantic prediction performance compared to existing methods, while simultaneously reducing computational and memory costs by 51.3% and 27.5%, respectively. Furthermore, our approach achieves complete reconstruction and accurate classification of thin structures such as poles and wires-which existing methods struggle to perceive-highlighting its potential for dense and accurate 3D radar perception.

cs.RO↗

arXiv2025

Dynamical and finite-size effects on the criterion of first-order phase transition

To identify first-order phase transitions in the dynamical process similar to the relativistic heavy-ion collisions, we investigate the dynamical behaviors of the first-order phase transition criterion in the Fokker-Planck framework. In the thermodynamic limit, the criterion can be expressed as combinations of cumulants or coefficients of an Ising-like effective potential. Our study reveals that factors such as phase transition scenarios, initial temperature, system volume, relaxation rate, and evolution trajectory have great impacts on the criterion, a larger initial temperature, a smaller volume, a larger relaxation rate, or bending of the trajectory will all lead to a reduction of the first-order phase transition signal, while volume expansion over time preserves signal integrity. Analysis along a hypothetical freezeout line shows that the signal is possibly preserved at relatively large chemical potentials.

nucl-th↗

arXiv2025

Toward Real-World Cooperative and Competitive Soccer with Quadrupedal Robot Teams

Achieving coordinated teamwork among legged robots requires both fine-grained locomotion control and long-horizon strategic decision-making. Robot soccer offers a compelling testbed for this challenge, combining dynamic, competitive, and multi-agent interactions. In this work, we present a hierarchical multi-agent reinforcement learning (MARL) framework that enables fully autonomous and decentralized quadruped robot soccer. First, a set of highly dynamic low-level skills is trained for legged locomotion and ball manipulation, such as walking, dribbling, and kicking. On top of these, a high-level strategic planning policy is trained with Multi-Agent Proximal Policy Optimization (MAPPO) via Fictitious Self-Play (FSP). This learning framework allows agents to adapt to diverse opponent strategies and gives rise to sophisticated team behaviors, including coordinated passing, interception, and dynamic role allocation. With an extensive ablation study, the proposed learning method shows significant advantages in the cooperative and competitive multi-agent soccer game. We deploy the learned policies to real quadruped robots relying solely on onboard proprioception and decentralized localization, with the resulting system supporting autonomous robot-robot and robot-human soccer matches on indoor and outdoor soccer courts.

cs.RO↗

arXiv2025

Baryogenesis and first-order QCD transition with gravitational waves from a large lepton asymmetry

A large primordial lepton asymmetry can lead to successful baryogenesis by preventing the restoration of electroweak symmetry at high temperatures, thereby suppressing the sphaleron rate. This asymmetry can also lead to a first-order cosmic QCD transition, accompanied by detectable gravitational wave (GW) signals. By employing next-to-leading order dimensional reduction we determine that the necessary lepton asymmetry is approximately one order of magnitude smaller than previously estimated. Incorporating an updated QCD equation of state that harmonizes lattice and functional QCD outcomes, we pinpoint the range of lepton flavor asymmetries capable of inducing a first-order cosmic QCD transition. To maintain consistency with observational constraints from the Cosmic Microwave Background and Big Bang Nucleosynthesis, achieving the correct baryon asymmetry requires entropy dilution by approximately a factor of ten. However, the first-order QCD transition itself can occur independently of entropy dilution. We propose that the sphaleron freeze-in mechanism can be investigated through forthcoming GW experiments such as $μ$Ares.

hep-ph↗

arXiv2025

Transcranial Photoacoustic Imaging for Human Intracranial Pressure Evaluation

Photoacoustic imaging (PAI), by combining high optical contrast with ultrasonic resolution, offers a promising noninvasive approach for dynamic monitoring of cerebral vasculature. However, transcranial PAI still faces significant challenges due to strong attenuation of both optical and acoustic signals by the skull. In this study, we propose a multi-wavelength photoacoustic tomography system and method for intracranial pressure (ICP) assessment, enabling visualization of cross-sectional structures of the middle cerebral artery (MCA) through the human temporal bone. By utilizing multi-wavelength excitation in the near-infrared-I (NIR-I) window, quantitative maps of blood oxygen saturation ($\mathbf{sO_2}$) are reconstructed, and the relationship between oxygenation dynamics and ICP variations is established. Experimental results demonstrate that the proposed system can successfully capture dynamic $\mathbf{sO_2}$ fluctuations in the MCA despite skull attenuation, revealing its characteristic responses to ICP changes. This work provides a high-precision, noninvasive imaging tool for early stroke diagnosis, cerebral vascular function assessment, and neurointerventional guidance, highlighting the clinical translational potential of PAI in neuroscience.

physics.med-ph↗

arXiv2025

Quantum circuit for implementing AES S-box with low costs

The Advanced Encryption Standard (AES) is widely used and well-studied for its efficiency and strong security. This paper presents quantum circuit designs for the AES S-box by introducing the composite field $ F((2^4)^2) $ to replace the traditional field $ F(2^8) $, enabling the inversion to be decomposed into operations over $ F(2^4) $. This work reduces the quantum resource overhead required for implementing the S-box by decreasing the number of $CNOT$ gates in the matrix multiplication, lowering the depth of $T$ gates in both the inversion circuit and the multiplication circuit. Besides, the widths for the S-box quantum circuits are also optimized during the inversion circuit and multiplication circuit. With a linear key schedule, the resulting AES-128 quantum circuit reduce the product of the circuit width and $T$ depth to 102800, which is the lowest known to date.

quant-ph↗

arXiv2025

Quantum Knowledge Distillation for Large Language Models

As foundational tools in natural language processing, Large Language Models (LLMs) have immense parameter scales, which makes deployment and inference increasingly prohibitive, especially in resource-constrained devices. Therefore, knowledge distillation for LLMs, i.e., compressing the LLM to a smaller model, is meaningful. With strong parameter representation capacity, quantum computing is regarded as a promising solution. Here, we propose a Quantum knowledge Distillation model for LLMs (QD-LLM) that leverages variational quantum circuits to learn from LLMs. In classical simulation, QD-LLM outperforms several mainstream distillation methods on multiple text classification tasks in terms of both accuracy and efficiency using only 11 qubits. The results reveal an interesting phenomenon that the simulation of quantum student models may be regarded as a new class of quantum-inspired classical algorithms. Remarkably, we deploy the obtained circuits on the Baihua superconducting quantum processor via the Quafu platform to assess practical feasibility. The model maintains stable inference performance despite hardware constraints such as decoherence and finite sampling. In summary, QD-LLM marks a foundational step in connecting quantum computing with LLMs, demonstrating the feasibility of quantum-native approaches that aim to compress and deploy models of increasingly larger scales. The code of this article has been open-sourced at https://github.com/Lilingxiao-bupt/QD-LLM.

quant-ph↗

arXiv2025

Quantum Key-Recovery Attacks on FBC Algorithm

With the advancement of quantum computing, symmetric cryptography faces new challenges from quantum attacks. These attacks are typically classified into two models: Q1 (classical queries) and Q2 (quantum superposition queries). In this context, we present a comprehensive security analysis of the FBC algorithm considering quantum adversaries with different query capabilities. In the Q2 model, we first design 4-round polynomial-time quantum distinguishers for FBC-F and FBC-KF structures, and then perform $r(r>6)$-round quantum key-recovery attacks. Our attacks require $O(2^{(2n(r-6)+3n)/2})$ quantum queries, reducing the time complexity by a factor of $2^{4.5n}$ compared with quantum brute-force search, where $n$ denotes the subkey length. Moreover, we give a new 6-round polynomial-time quantum distinguisher for FBC-FK structure. Based on this, we construct an $r(r>6)$-round quantum key-recovery attack with complexity $O(2^{n(r-6)})$. Considering an adversary with classical queries and quantum computing capabilities, we demonstrate low-data quantum key-recovery attacks on FBC-KF/FK structures in the Q1 model. These attacks require only a constant number of plaintext-ciphertext pairs, then use the Grover algorithm to search the intermediate states, thereby recovering all keys in $O(2^{n/2})$ time.

quant-ph↗

arXiv2025

Experimental Study on Reference-Path-Aided System Calibration for mmWave Bistatic ISAC Systems

Integrated sensing and communications (ISAC) has been regarded as a key enabling technology for next-generation wireless networks. Compared to monostatic ISAC, bistatic ISAC can eliminate the critical challenge of self-interference cancellation and is well compatible with the existing network infrastructures. However, the synchronization between the transmitter and the sensing receiver becomes a crucial problem. The extracted channel state information (CSI) for sensing under communication synchronization contains different types of system errors, such as the sampling time offset (STO), carrier frequency offset (CFO), and random phase shift, which can severely degrade sensing performance or even render sensing infeasible. To address this problem, a reference-path-aided system calibration scheme is designed for mmWave bistatic ISAC systems, where the line-of-sight (LoS) path can be blocked. By exploiting the delay-angle sparsity feature in mmWave ISAC systems, the reference path, which can be either a LoS or a non-LoS (NLoS) path, is first identified. By leveraging the fact that all the paths suffer the same system errors, the channel parameter extracted from the reference path is utilized to compensate for the system errors in all other paths. A mmWave ISAC system is developed to validate our design. Experimental results demonstrate that the proposed scheme can support precise estimation of Doppler shift and delay, maintaining time-synchronization errors within 1 nanosecond.

eess.SP↗

arXiv2025

Autonomous Exploration with Terrestrial-Aerial Bimodal Vehicles

Terrestrial-aerial bimodal vehicles, which integrate the high mobility of aerial robots with the long endurance of ground robots, offer significant potential for autonomous exploration. Given the inherent energy and time constraints in practical exploration tasks, we present a hierarchical framework for the bimodal vehicle to utilize its flexible locomotion modalities for exploration. Beginning with extracting environmental information to identify informative regions, we generate a set of potential bimodal viewpoints. To adaptively manage energy and time constraints, we introduce an extended Monte Carlo Tree Search approach that strategically optimizes both modality selection and viewpoint sequencing. Combined with an improved bimodal vehicle motion planner, we present a complete bimodal energy- and time-aware exploration system. Extensive simulations and deployment on a customized real-world platform demonstrate the effectiveness of our system.

cs.RO↗