Selected work

Representative Papers

The Impact of Operational-Data Fidelity when Assessing Safety-Critical Autonomous-Vehicle Software

Aug 09, 2026

Low-granularity operational data can lead to overly optimistic assessments of autonomous driving software reliability, thereby undermining the credibility of safety certification. This work proposes a systematic approach based on Conservative Bayesian Inference (CBI) to quantify, for the first time, the adverse impact of insufficient data fidelity on the robustness of reliability claims. By integrating statistical robustness analysis with software reliability modeling, the study demonstrates that even conservative inference strategies may yield misleading conclusions when applied to low-fidelity data. The paper establishes the first conservative estimation framework that explicitly accounts for the influence of data granularity on reliability assessment, highlighting the critical importance of high-fidelity operational data in safety certification of autonomous driving systems.

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Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks

Aug 06, 2026

This study addresses the challenge of semantic interoperability in the defense and national security domain, where numerous highly heterogeneous and specialized ontologies impede effective integration. To bridge this gap, the authors systematically analyze over 60 publicly available ontologies and establish the first Ontology Alignment Evaluation Initiative (OAEI) benchmark track dedicated to this domain, comprising eight alignment tasks. Leveraging multiple state-of-the-art ontology matching systems, they generate automatic alignments, aggregate them into a consensus mapping, and refine the results through expert manual validation to produce a high-quality silver-standard dataset. This work fills a critical void in standardized evaluation for ontology alignment in defense and security contexts, significantly advancing semantic interoperability within the field.

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Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller

Jul 28, 2026

This work addresses the challenge of cooperative navigation and collision avoidance for multiple unmanned aerial vehicles (UAVs) in complex indoor environments under GNSS-denied conditions. The proposed framework, termed “world-coordinate fusion, ego-coordinate execution,” employs a shared voxel map to unify spatial representation across agents, fuses 360° LiDAR observations from multiple UAVs into local bird’s-eye-view maps, and leverages a multi-agent Soft Actor-Critic algorithm for decentralized continuous control. Operating within a centralized training and decentralized execution paradigm, the system enables scalable and consistent collaborative navigation, further enhanced by offline imitation fine-tuning to improve policy robustness. In simulations, the approach achieves a 90.3% success rate in corridor navigation, significantly outperforming A* and potential field methods; real-world experiments demonstrate successful stable traversal of complex obstacle layouts by two UAVs.

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Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning

Jul 28, 2026

This work addresses key challenges in multi-UAV cooperative navigation within complex environments—namely, susceptibility to local optima, sparse rewards, imbalanced learning dynamics, and poor cross-scenario generalization. To overcome these issues, the authors propose a systematic multi-agent deep reinforcement learning framework that integrates a collaborative exploration mechanism leveraging memory and directional novelty, a hierarchical cooperative demonstration buffer, a safety-aware dual-conditioned curriculum scheduler, and a structure-aware policy generalization method grounded in local geometric features. By synergistically combining behavior cloning, curriculum learning, and a mixture-of-experts architecture, the approach significantly enhances collaborative success rates, navigation robustness, zero-shot transferability across unseen scenarios, and adaptability to dynamic perturbations in mixed static-dynamic obstacle environments.

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Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

Jul 14, 2026

This work addresses the performance degradation and unphysical bang-bang control often observed when gradient-based multi-agent reinforcement learning methods, trained on small domains, are transferred to large-scale turbulent channel flows. For the first time, evolutionary strategies (ES) are directly applied to optimize a recurrent neural network-based closed-loop controller in a full-scale turbulent channel. The approach employs an energy-aware reward function that evaluates the entire flow evolution and leverages parallel policy evaluation for efficient training. By circumventing the control degeneracy inherent in gradient-based methods, the proposed strategy yields physically plausible actuation aligned with near-wall turbulence dynamics. In direct numerical simulations, it achieves approximately 26% drag reduction in skin friction, substantially outperforming both GRU-MARL trained on small domains (17%) and classical opposition control (22%).

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Recent publications

Latest Papers

The Impact of Operational-Data Fidelity when Assessing Safety-Critical Autonomous-Vehicle Software

Aug 09, 2026

Low-granularity operational data can lead to overly optimistic assessments of autonomous driving software reliability, thereby undermining the credibility of safety certification. This work proposes a systematic approach based on Conservative Bayesian Inference (CBI) to quantify, for the first time, the adverse impact of insufficient data fidelity on the robustness of reliability claims. By integrating statistical robustness analysis with software reliability modeling, the study demonstrates that even conservative inference strategies may yield misleading conclusions when applied to low-fidelity data. The paper establishes the first conservative estimation framework that explicitly accounts for the influence of data granularity on reliability assessment, highlighting the critical importance of high-fidelity operational data in safety certification of autonomous driving systems.

0 citationsRead paper

Improving Interoperability among Defence and National Security Ontologies: Analysis and Evaluation Tasks

Aug 06, 2026

This study addresses the challenge of semantic interoperability in the defense and national security domain, where numerous highly heterogeneous and specialized ontologies impede effective integration. To bridge this gap, the authors systematically analyze over 60 publicly available ontologies and establish the first Ontology Alignment Evaluation Initiative (OAEI) benchmark track dedicated to this domain, comprising eight alignment tasks. Leveraging multiple state-of-the-art ontology matching systems, they generate automatic alignments, aggregate them into a consensus mapping, and refine the results through expert manual validation to produce a high-quality silver-standard dataset. This work fills a critical void in standardized evaluation for ontology alignment in defense and security contexts, significantly advancing semantic interoperability within the field.

0 citationsRead paper

Shared Voxel-Map-Based Cooperative Indoor UAV Guidance with a Multi-Agent Soft Actor-Critic Controller

Jul 28, 2026

This work addresses the challenge of cooperative navigation and collision avoidance for multiple unmanned aerial vehicles (UAVs) in complex indoor environments under GNSS-denied conditions. The proposed framework, termed “world-coordinate fusion, ego-coordinate execution,” employs a shared voxel map to unify spatial representation across agents, fuses 360° LiDAR observations from multiple UAVs into local bird’s-eye-view maps, and leverages a multi-agent Soft Actor-Critic algorithm for decentralized continuous control. Operating within a centralized training and decentralized execution paradigm, the system enables scalable and consistent collaborative navigation, further enhanced by offline imitation fine-tuning to improve policy robustness. In simulations, the approach achieves a 90.3% success rate in corridor navigation, significantly outperforming A* and potential field methods; real-world experiments demonstrate successful stable traversal of complex obstacle layouts by two UAVs.

0 citationsRead paper

Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning

Jul 28, 2026

This work addresses key challenges in multi-UAV cooperative navigation within complex environments—namely, susceptibility to local optima, sparse rewards, imbalanced learning dynamics, and poor cross-scenario generalization. To overcome these issues, the authors propose a systematic multi-agent deep reinforcement learning framework that integrates a collaborative exploration mechanism leveraging memory and directional novelty, a hierarchical cooperative demonstration buffer, a safety-aware dual-conditioned curriculum scheduler, and a structure-aware policy generalization method grounded in local geometric features. By synergistically combining behavior cloning, curriculum learning, and a mixture-of-experts architecture, the approach significantly enhances collaborative success rates, navigation robustness, zero-shot transferability across unseen scenarios, and adaptability to dynamic perturbations in mixed static-dynamic obstacle environments.

0 citationsRead paper

Gradient-free learning of a closed-loop wall controller for turbulent drag reduction

Jul 14, 2026

This work addresses the performance degradation and unphysical bang-bang control often observed when gradient-based multi-agent reinforcement learning methods, trained on small domains, are transferred to large-scale turbulent channel flows. For the first time, evolutionary strategies (ES) are directly applied to optimize a recurrent neural network-based closed-loop controller in a full-scale turbulent channel. The approach employs an energy-aware reward function that evaluates the entire flow evolution and leverages parallel policy evaluation for efficient training. By circumventing the control degeneracy inherent in gradient-based methods, the proposed strategy yields physically plausible actuation aligned with near-wall turbulence dynamics. In direct numerical simulations, it achieves approximately 26% drag reduction in skin friction, substantially outperforming both GRU-MARL trained on small domains (17%) and classical opposition control (22%).

0 citationsRead paper