AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

A survey of transfer learning

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Design and use paradigms for gazebo, an open-source multi-robot simulator

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An introduction to inertial navigation

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RotorS—A Modular Gazebo MAV Simulator Framework

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PIXHAWK: A system for autonomous flight using onboard computer vision

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Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo

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Practical Meteorology : An Algebra-based Survey of Atmospheric Science

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Introduction to Fluid Mechanics

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Classical Mechanics

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Detection-Aware Trajectory Generation for a Drone Cinematographer

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Integrate multi-agent simulation environment and multi-agent reinforcement learning (MARL) for real-world scenario

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A Novelty Approach to Emulate Field Data Captured by Unmanned Aerial Vehicles for Training Deep Learning Algorithms Used for Search-and-Rescue Activities at Sea

Trong, T. D., Hai, Q. T., Duc, N.-T., & Han, T. T. (2021) · International Conference on Communications and Electronics (ICCE) · doi:10.1109/icce48956.2021.9352109 · Cited by 14

OpenUAV Cloud Testbed: a Collaborative Design Studio for Field Robotics

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ESPADA: Extended Synthetic and Photogrammetric Aerial-Image Dataset

Campos, R. L., & Martínez-Carranza, J. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3101879 · Cited by 13

BotNet: A Simulator for Studying the Effects of Accurate Communication Models on Multi-Agent and Swarm Control

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USVs‐Sim: A general simulation platform for unmanned surface vessels autonomous learning

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Semantic Concept Testing in Autonomous Driving by Extraction of Object-Level Annotations from CARLA

Gannamaneni, S. S., Houben, S., & Akila, M. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00117 · Cited by 13

HARPS: An Online POMDP Framework for Human-Assisted Robotic Planning and Sensing

Burks, L., Ray, H. M., McGinley, J., Vunnam, S., & Ahmed, N. R. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3263460 · Cited by 13

Augmented driver behavior models for high‐fidelity simulation study of crash detection algorithms

Jami, A., Razzaghpour, M., Alnuweiri, H., & Fallah, Y. P. (2023) · IET Intelligent Transport Systems · doi:10.1049/itr2.12373 · Cited by 13

Shot Type Feasibility in Autonomous UAV Cinematography

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AILiveSim: An Extensible Virtual Environment for Training Autonomous Vehicles

Leudet, J., Christophe, F., Mikkonen, T., & Männistö, T. (2019) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2019.00074 · Cited by 13

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Tani, J., Daniele, A. F., Bernasconi, G., Camus, A., Petrov, A., Courchesne, A., Mehta, B., Suri, R., Zaluska, T. J., Walter, M. R., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341677 · Cited by 13

Devising a Distributed Co-Simulator for a Multi-UAV Network

Park, S., La, W. G., Lee, W., & Kim, H. (2020) · Sensors · doi:10.3390/s20216196 · Cited by 13

Customized Co-Simulation Environment for Autonomous Driving Algorithm Development and Evaluation

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Learn by Observation: Imitation Learning for Drone Patrolling from Videos of A Human Navigator

Fan, Y. B., Chu, S., Zhang, W., Song, R., & Li, Y. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340691 · Cited by 13

A Monocular Visual Odometry Method Based on Virtual-Real Hybrid Map in Low-Texture Outdoor Environment

Xie, X., Yang, T., Ning, Y., Zhang, F., & Zhang, Y. (2021) · Sensors · doi:10.3390/s21103394 · Cited by 13

Real-Time Hazard Symbol Detection and Localization Using UAV Imagery

Tijtgat, N., Volckaert, B., & Turck, F. D. (2017) · IEEE Vehicular Technology Conference · doi:10.1109/vtcfall.2017.8288259 · Cited by 13

CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous drivingPreprint

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CausalCity: Complex Simulations with Agency for Causal Discovery and ReasoningPreprint

McDuff, D., Song, Y., Lee, J., Vineet, V., Vemprala, S. H., Gydé, N., Salman, H., Ma, S., Sohn, K., & Kapoor, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13364 · Cited by 13

UAV Operations and Vertiport Capacity Evaluation with a Mixed-Reality Digital Twin for Future Urban Air Mobility Viability

Zhao, J., Wen, Z. Z., Mohanta, K., Subasu, S., Fremond, R., Su, Y., Kallaka, R., & Tsourdos, A. (2025) · Drones · doi:10.3390/drones9090621 · Cited by 12

Embodied AI: From LLMs to World Models [Feature]

Feng, T., Wang, X., Jiang, Y., & Zhu, W. (2025) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2025.3603693 · Cited by 59

Gesture Learning For Self-Driving Cars

Shaotran, E., Cruz, J. J., & Reddi, V. J. (2021) · IEEE International Conference on Autonomous Systems (ICAS) · doi:10.1109/icas49788.2021.9551186 · Cited by 12

Directional Sensor Planning for Occlusion Avoidance

Gemerek, J., Fu, B., Chen, Y., Liu, Z., Zheng, M., van Wijk, D. E. J., & Ferrari, S. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3180628 · Cited by 12

AirLoop: A Simulation Framework for Testing of UAV Services

Giovagnola, J., Megías, J. B. M., Molina, M., Cuéllar, M. P., & Morales, D. P. (2023) · IEEE Access · doi:10.1109/access.2023.3253788 · Cited by 12

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

Lv, S., Gao, Y., Che, J., & Quan, Q. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161383 · Cited by 12

An Adaptive Multi-Fidelity Sampling Framework for Safety Analysis of Connected and Automated Vehicles

Gong, X., Feng, S., & Pan, Y. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3296716 · Cited by 12

Reinforcement learning with model-based feedforward inputs for robotic table tennis

Ma, H., Büchler, D., Schölkopf, B., & Muehlebach, M. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10140-6 · Cited by 17

Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network

Zhang, Y., Deng, J., Liu, P., Li, W., & Zhao, S. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3370147 · Cited by 14

A Learner-Centric Explainable Educational Metaverse for Cyber–Physical Systems Engineering

Yun, S., Kwon, J.-W., Lee, Y.-H., Kim, J.-H., & Kim, W.-T. (2024) · Electronics · doi:10.3390/electronics13173359 · Cited by 12

A Novel Multimodal Fusion Sensing-Based Channel Prediction Method for UAV Communications

Xin, Z., Liu, Y., Xing, J., Huang, J., Bian, J., & Zhang, Y. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3482410 · Cited by 12

UCDNet: Multi-UAV Collaborative 3-D Object Detection Network by Reliable Feature Mapping

Tian, P., Wang, Z., Cheng, P., Wang, Y., Wang, Z., Zhao, L., Yan, M., Yang, X., & Sun, X. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3517594 · Cited by 12

A Survey on Passing-Through Control of Multi-Robot Systems in Cluttered Environments

Gao, Y., Bai, C., & Quan, Q. (2025) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2025.000095 · Cited by 12

In Search of the Ultimate Autonomous Driving Simulator

Sułkowski, T., Bugiel, P., & Izydorczyk, J. (2018) · International Conference on Signals and Electronic Systems · doi:10.1109/icses.2018.8507288 · Cited by 12

Analysis of Classifier Training on Synthetic Data for Cross-Domain Datasets

Cortés, A., Rodríguez, C., Vélez, G., Barandiarán, J., & Nieto, M. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3009186 · Cited by 12

Velocity Obstacle Based Strategy for Multi-agent Collision Avoidance of Unmanned Aerial Vehicles

Lombard, A., Durand, L., & Galland, S. (2020) · IEEE International Conference on Sensing, Communication, and Networking (SECON) · doi:10.1109/seconworkshops50264.2020.9149770 · Cited by 12

DronePose: Photorealistic UAV-Assistant Dataset Synthesis for 3D Pose Estimation via a Smooth Silhouette Loss

Albanis, G., Zioulis, N., Dimou, A., Zarpalas, D., & Daras, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_44 · Cited by 14

Machine Vision for Construction Equipment by Transfer Learning with Scale Models

Borngrund, C., Bodin, U., & Sandin, F. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207577 · Cited by 12

Bebop 2 Quadrotor as a Platform for Research and Education in Robotics and Control Engineering

Giernacki, W., Kozierski, P., Michalski, J., Retinger, M., Madoński, R., & Campoy, P. (2020) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas48674.2020.9213872 · Cited by 12

AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning

Alzayer, H., Lin, H., & Bala, K. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636788 · Cited by 13

Improving Off-road Planning Techniques with Learned Costs from Physical Interactions

Sivaprakasam, M., Triest, S., Wang, W., Yin, P., & Scherer, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561881 · Cited by 12

FlockAI: A Testing Suite for ML-Driven Drone Applications

Trihinas, D., Agathocleous, M., Avogian, K., & Katakis, I. (2021) · Future Internet · doi:10.3390/fi13120317 · Cited by 12

The Role of Compute in Autonomous Micro Aerial Vehicles: Optimizing for Mission Time and Energy Efficiency

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Coach-assisted multi-agent reinforcement learning framework for unexpected crashed agents

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Robust 3D Object Detection in Cold Weather Conditions

Piroli, A., Dallabetta, V., Walessa, M., Meissner, D., Kopp, J., & Dietmayer, K. (2022) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv51971.2022.9827398 · Cited by 12

Verification of Camera-Based Autonomous Systems

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UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning

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Dynamic Mobile Sink Path Planning for Unsynchronized Data Collection in Heterogeneous Wireless Sensor Networks

Yang, M., Liu, N., Feng, Y., Gong, H., Wang, X., & Liu, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3294232 · Cited by 14

Context-Aware Data Augmentation for LIDAR 3d Object Detection

Hu, X., Duan, Z., Huang, X., Xu, Z., Ming, D., & Ma, J. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222773 · Cited by 12

Deep Reinforcement Learning Tf-Agent-Based Object Tracking With Virtual Autonomous Drone in a Game Engine

Farkhodov, K., Lee, S., Platoš, J., & Kwon, K. (2023) · IEEE Access · doi:10.1109/access.2023.3325062 · Cited by 13

Distributed multi-target tracking and active perception with mobile camera networks

Casao, S., Serra-Gómez, Á., Murillo, A. C., Böhmer, W., Alonso–Mora, J., & Montijano, E. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103876 · Cited by 12

How Challenging is a Challenge? CEMS: a Challenge Evaluation Module for SLAM Visual Perception

Zhao, X., Gao, Z. Y., Li, H., Ji, H., Yang, H., Li, C.-Y., Fang, H., & Chen, B. M. (2024) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-024-02077-4 · Cited by 13

REAL-TIME ON-BOARD OBSTACLE AVOIDANCE FOR UAVS BASED ON EMBEDDED STEREO VISION

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Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

Urrea, C., Garrido, F., & Kern, J. (2021) · Sensors · doi:10.3390/s21020492 · Cited by 12

ISETAuto: Detecting Vehicles With Depth and Radiance Information

Liu, Z., Farrell, J., & Wandell, B. A. (2021) · IEEE Access · doi:10.1109/access.2021.3063692 · Cited by 13

A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots

Ojeda, P., Monroy, J., & González-Jiménez, J. (2021) · Sensors · doi:10.3390/s21062041 · Cited by 12

Spatial Language Understanding for Object Search in Partially Observed City-scale Environments

Zheng, K., Bayazit, D., Mathew, R., Pavlick, E., & Tellex, S. (2021) · IEEE RO-MAN · doi:10.1109/ro-man50785.2021.9515426 · Cited by 12

YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System

Lindenheim-Locher, W., Świtoński, A., Krzeszowski, T., Paleta, G., Hasiec, P., Josiński, H., Paszkuta, M., Wojciechowski, K. W., & Rosner, J. (2023) · Sensors · doi:10.3390/s23146396 · Cited by 13

Near Real-Time Detection of Poachers from Drones in AirSim

Bondi, E., Kapoor, A., Dey, D., Piavis, J., Shah, S., Hannaford, R., Iyer, A., Joppa, L., & Tambe, M. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/847 · Cited by 12

Vision Based Collaborative Path Planning for Micro Aerial Vehicles

Vemprala, S. H., & Saripalli, S. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8462910 · Cited by 12

IV-SLAM: Introspective Vision for Simultaneous Localization and MappingPreprint

Rabiee, S., & Biswas, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.02760 · Cited by 12

Representation Learning for Event-based Visuomotor PoliciesPreprint

Vemprala, S. H., Mian, S., & Kapoor, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.00806 · Cited by 12

Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning

Muller-Brockhausen, M., Preuss, M., & Plaat, A. (2021) · 2021 IEEE Conference on Games (CoG) · doi:10.1109/cog52621.2021.9619000 · Cited by 11

Entropy-Based Distributed Behavior Modeling for Multi-Agent UAVs

Fina, L., Smith, D. S., Carnahan, J., & Sevil, H. E. (2022) · Drones · doi:10.3390/drones6070164 · Cited by 11

The Robotarium: A Remotely-Accessible, Multi-Robot Testbed for Control Research and Education

Wilson, S., & Egerstedt, M. (2022) · IEEE Open Journal of Control Systems · doi:10.1109/ojcsys.2022.3231523 · Cited by 11

Bridging the Gap between Simulation and Real Autonomous UAV Flights in Industrial Applications

Pérez-Seguí, R., Arias‐Perez, P., Melero‐Deza, J., Fernández-Cortizas, M., Pérez-Saura, D., & Campoy, P. (2023) · Aerospace · doi:10.3390/aerospace10090814 · Cited by 14

ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning

Yu, X., Shi, R., Feng, P., Tian, Y., Luo, J., & Wu, W. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230609 · Cited by 14

A Reinforcement Learning Benchmark for Autonomous Driving in General Urban Scenarios

Jiang, Y., Zhan, G., Lan, Z., Liu, C., Cheng, B., & Li, S. E. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3329823 · Cited by 11

Semantic and Geometric-Aware Day-to-Night Image Translation Network

Bang, G., Lee, J., Endo, Y., Nishimori, T., Nakao, K., & Kamijo, S. (2024) · Sensors · doi:10.3390/s24041339 · Cited by 11

Terrain traversability prediction through self-supervised learning and unsupervised domain adaptation on synthetic data

Vecchio, G., Palazzo, S., Guastella, D. C., Giordano, D., Muscato, G., & Spampinato, C. (2024) · Autonomous Robots · doi:10.1007/s10514-024-10158-4 · Cited by 11

Dynamic Obstacle Avoidance for UAVs using MPC and GP-Based Motion Forecast

Olcay, E., Meeß, H., & Elger, G. (2024) · European Control Conference (ECC) · doi:10.23919/ecc64448.2024.10591083 · Cited by 11

Dynamic LiDAR Re-Simulation Using Compositional Neural Fields

Wu, H., Zuo, X., Leutenegger, S., Litany, O., Schindler, K., & Huang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01889 · Cited by 11

Investigating the Sim-to-Real Generalizability of Deep Learning Object Detection Models

Rüter, J., Durak, U., & Dauer, J. C. (2024) · Journal of Imaging · doi:10.3390/jimaging10100259 · Cited by 12

Match-Stereo-Videos: Bidirectional Alignment for Consistent Dynamic Stereo Matching

Jing, J., Mao, Y., & Mikolajczyk, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73027-6_24 · Cited by 11

SpecGuard: Specification Aware Recovery for Robotic Autonomous Vehicles from Physical Attacks

Dash, P., Chan, E., & Pattabiraman, K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3690210 · Cited by 10

MetroLoc: Metro Vehicle Mapping and Localization With LiDAR-Camera-Inertial Integration

Wang, Y., Song, W., Wang, Y., Dai, X., & Lou, Y. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3512000 · Cited by 11

A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation

Gaigalas, J., Perkauskas, L., Gricius, H., Kanapickas, T., & Kriščiūnas, A. (2025) · Drones · doi:10.3390/drones9040236 · Cited by 11

3D Reconstruction in Robotics: A Comprehensive Review

Selvaratnam, D., & Bazazian, D. (2025) · Computers & Graphics · doi:10.1016/j.cag.2025.104256 · Cited by 11

Human inspired deep learning to locate and classify terrestrial and arboreal animals in thermal drone surveys

Backman, K., Wood, J., Brandimarti, M. E., Beranek, C. T., & Roff, A. (2025) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.70006 · Cited by 11

Multi-Vehicle Mixed Reality Reinforcement Learning for Autonomous Multi-Lane Driving

Mitchell, R., Fletcher, J., Panerati, J., & Prorok, A. (2020) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/lqgx1131 · Cited by 11

Leveraging Active Perception for Improving Embedding-based Deep Face Recognition

Passalis, N., & Tefas, A. (2020) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp48831.2020.9287085 · Cited by 12

A Study on an Enhanced Autonomous Driving Simulation Model Based on Reinforcement Learning Using a Collision Prevention Model

Kim, J., Huh, J., Jung, S.-H., & Sim, C.-B. (2021) · Electronics · doi:10.3390/electronics10182271 · Cited by 11

Instance Segmentation in CARLA: Methodology and Analysis for Pedestrian-oriented Synthetic Data Generation in Crowded Scenes

Lyssenko, M., Gladisch, C., Heinzemann, C., Woehrle, M., & Triebel, R. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00115 · Cited by 11

Building a Realistic Virtual Simulator for Unmanned Aerial Vehicle Teleoperation

Mora-Soto, M. E., Maldonado-Romo, J., Rodríguez-Molina, A., & Aldape-Pérez, M. (2021) · Applied Sciences · doi:10.3390/app112412018 · Cited by 11

IRIS: High-fidelity Perception Sensor Modeling for Closed-Loop Planetary Simulations

Aiazzi, C., Jain, A., Gaut, A., Young, A., & Elmquist, A. (2022) · AIAA SciTech Forum · doi:10.2514/6.2022-1433 · Cited by 11

A Real-Time Method for Time-to-Collision Estimation from Aerial Images

Tøttrup, D., Skovgaard, S. L., Sejersen, J. L. F., & de Figueiredo, R. P. (2022) · Journal of Imaging · doi:10.3390/jimaging8030062 · Cited by 11

GAN-Based Semantic-Aware Translation for Day-to-Night Images

Shiotsuka, D., Lee, J., Endo, Y., Javanmardi, E., Takahashi, K., Nakao, K., & Kamijo, S. (2022) · International Conference on Consumer Electronics · doi:10.1109/icce53296.2022.9730532 · Cited by 11

Lean and BIM based flight planning for automated data acquisition of bridge structures with LiDAR UAV during construction phase

Tschickardt, T., Kaufmann, F., & Glock, C. (2022) · Computing in construction · doi:10.35490/ec3.2022.146 · Cited by 11

The future of mobility-as-a-service: trust transfer across automated mobilities, from road to sidewalk

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Dynamic Certification for Autonomous Systems

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