As Belgian Defence is now in a fast pace integrating new Remotely Piloted Aircraft Systems (RPAS) / drone systems into its operations, it is developing also a doctrine to manage and deploy these RPAS systems in a safe way. This research study seeks to support the actors within Belgian Defence in this process by developing a strategy to incorporate human factors in the evaluation of RPAS and RPAS operators. A qualitative and quantitative benchmarking tool based on standardized test methodologies will be developed, integrated into a realistic simulation environment.
This will enable:
- On-the-job pilot training in a safe simulation environment with qualitative and quantitative assessment of the pilot skills, which will support the military training of Belgian Defence RPAS pilots
- A simulation tool for the quick risk assessment for the certification of novel RPAS systems, providing support to the Belgian Military Airworthiness Agency (MR-SYS)
- A simulation tool for commanders in the field to practice for certain risky operations before deploying the real RPAS, thereby minimizing risks and operational losses
Hans De Smet
Project Publications
2023
- D. Doroftei, G. De Cubber, and H. De Smet, “Human factors assessment for drone operations: towards a virtual drone co-pilot," in Human Factors in Robots, Drones and Unmanned Systems. AHFE (2023) International Conference., 2023.
[BibTeX] [Abstract] [Download PDF] [DOI]
As the number of drone operations increases, so does the risk of incidents with these novel, yet sometimes dangerous unmanned systems. Research has shown that over 70% of drone incidents are caused by human error, so in order to reduce the risk of incidents, the human factors related to the operation of the drone should be studied. However, this is not a trivial exercise, because on the one hand, a realistic operational environment is required (in order to study the human behaviour in realistic conditions), while on the other hand a standardised environment is required, such that repeatable experiments can be set up in order to ensure statistical relevance. In order to remedy this, within the scope of the ALPHONSE project, a realistic simulation environment was developed that is specifically geared towards the evaluation of human factors for military drone operations. Within the ALPHONSE simulator, military (and other) drone pilots can perform missions in realistic operational conditions. At the same time, they are subjected to a range of factors that can influence operator performance. These constitute both person-induced factors like pressure to achieve the set goals in time or people talking to the pilot and environment-induced stress factors like changing weather conditions. During the flight operation, the ALPHONSE simulator continuously monitors over 65 flight parameters. After the flight, an overall performance score is calculated, based upon the achievement of the mission objectives. Throughout the ALPHONSE trials, a wide range of pilots has flown in the simulator, ranging from beginner to expert pilots. Using all the data recorded during these flights, three actions are performed:-An Artificial Intelligence (AI) – based classifier was trained to automatically recognize in real time good and bad flight behaviour. This allows for the development of a virtual co-pilot that can warn the pilot at any given moment when the pilot is starting to exhibit behaviour that is recognized by the classifier to correspond mostly to the behaviour of inexperienced pilots and not to the behaviour of good pilots.-An identification and ranking of the human factors and their impact on the flight performance, by linking the induced stress factors to the performance scores-An update of the training procedures to take into consideration the human factors that impact flight performance, such that newly trained pilots are better aware of these influences.The objective of this paper is to present the complete ALPHONSE simulator system for the evaluation of human factors for drone operations and present the results of the experiments with real military flight operators. The focus of the paper will be on the elaboration of the design choices for the development of the AI – based classifier for real-time flight performance evaluation.The proposed development is highly significant, as it presents a concrete and cost-effective methodology for developing a virtual co-pilot for drone pilots that can render drone operations safer. Indeed, while the initial training of the AI model requires considerable computing resources, the implementation of the classifier can be readily integrated in commodity flight controllers to provide real-time alerts when pilots are manifesting undesired flight behaviours.The paper will present results of tests with drone pilots from Belgian Defence and civilian Belgian Defence researchers that have flown within the ALPHONSE simulator. These pilots have first acted as data subjects to provide flight data to train the model and have later been used to validate the model. The validation shows that the virtual co-pilot achieves a very high accuracy and can in over 80% of the cases correctly identify bad flight profiles in real-time.
@inproceedings{ahfe20203doroftei, title={Human factors assessment for drone operations: towards a virtual drone co-pilot}, author={Doroftei, D. and De Cubber, G. and De Smet, H.}, booktitle={Human Factors in Robots, Drones and Unmanned Systems. AHFE (2023) International Conference.}, editors ={Tareq Ahram and Waldemar Karwowski}, publisher = {AHFE Open Access, AHFE International, USA}, year = {2023}, vol = {93}, project = {Alphonse}, location = {San Francisco, USA}, unit= {meca-ras}, doi = {http://doi.org/10.54941/ahfe1003747}, url={https://openaccess.cms-conferences.org/publications/book/978-1-958651-69-8/article/978-1-958651-69-8_6}, abstract = {As the number of drone operations increases, so does the risk of incidents with these novel, yet sometimes dangerous unmanned systems. Research has shown that over 70% of drone incidents are caused by human error, so in order to reduce the risk of incidents, the human factors related to the operation of the drone should be studied. However, this is not a trivial exercise, because on the one hand, a realistic operational environment is required (in order to study the human behaviour in realistic conditions), while on the other hand a standardised environment is required, such that repeatable experiments can be set up in order to ensure statistical relevance. In order to remedy this, within the scope of the ALPHONSE project, a realistic simulation environment was developed that is specifically geared towards the evaluation of human factors for military drone operations. Within the ALPHONSE simulator, military (and other) drone pilots can perform missions in realistic operational conditions. At the same time, they are subjected to a range of factors that can influence operator performance. These constitute both person-induced factors like pressure to achieve the set goals in time or people talking to the pilot and environment-induced stress factors like changing weather conditions. During the flight operation, the ALPHONSE simulator continuously monitors over 65 flight parameters. After the flight, an overall performance score is calculated, based upon the achievement of the mission objectives. Throughout the ALPHONSE trials, a wide range of pilots has flown in the simulator, ranging from beginner to expert pilots. Using all the data recorded during these flights, three actions are performed:-An Artificial Intelligence (AI) - based classifier was trained to automatically recognize in real time good and bad flight behaviour. This allows for the development of a virtual co-pilot that can warn the pilot at any given moment when the pilot is starting to exhibit behaviour that is recognized by the classifier to correspond mostly to the behaviour of inexperienced pilots and not to the behaviour of good pilots.-An identification and ranking of the human factors and their impact on the flight performance, by linking the induced stress factors to the performance scores-An update of the training procedures to take into consideration the human factors that impact flight performance, such that newly trained pilots are better aware of these influences.The objective of this paper is to present the complete ALPHONSE simulator system for the evaluation of human factors for drone operations and present the results of the experiments with real military flight operators. The focus of the paper will be on the elaboration of the design choices for the development of the AI - based classifier for real-time flight performance evaluation.The proposed development is highly significant, as it presents a concrete and cost-effective methodology for developing a virtual co-pilot for drone pilots that can render drone operations safer. Indeed, while the initial training of the AI model requires considerable computing resources, the implementation of the classifier can be readily integrated in commodity flight controllers to provide real-time alerts when pilots are manifesting undesired flight behaviours.The paper will present results of tests with drone pilots from Belgian Defence and civilian Belgian Defence researchers that have flown within the ALPHONSE simulator. These pilots have first acted as data subjects to provide flight data to train the model and have later been used to validate the model. The validation shows that the virtual co-pilot achieves a very high accuracy and can in over 80% of the cases correctly identify bad flight profiles in real-time.} }
2022
- D. Doroftei, G. De Cubber, and H. De Smet, “A quantitative measure for the evaluation of drone-based video quality on a target," in Eighteenth International Conference on Autonomic and Autonomous Systems (ICAS), Venice, Italy, 2022.
[BibTeX] [Abstract] [Download PDF] [DOI]
This paper presents a methodology to assess video quality and based on that automatically calculate drone trajectories that optimize the video quality.
@InProceedings{doroftei2022alphonse2, author = {Doroftei, Daniela and De Cubber, Geert and De Smet, Hans}, booktitle = {Eighteenth International Conference on Autonomic and Autonomous Systems (ICAS)}, title = {A quantitative measure for the evaluation of drone-based video quality on a target}, year = {2022}, month = jun, organization = {IARIA}, publisher = {ThinkMind}, address = {Venice, Italy}, url = {https://www.thinkmind.org/articles/icas_2022_1_40_20018.pdf}, isbn={978-1-61208-966-9}, doi = {https://www.thinkmind.org/index.php?view=article&articleid=icas_2022_1_40_20018}, abstract = {This paper presents a methodology to assess video quality and based on that automatically calculate drone trajectories that optimize the video quality.}, project = {Alphonse}, unit= {meca-ras} } - D. Doroftei, G. De Cubber, and H. De Smet, “Assessing Human Factors for Drone Operations in a Simulation Environment," in Human Factors in Robots, Drones and Unmanned Systems – AHFE (2022) International Conference, New York, USA, 2022.
[BibTeX] [Abstract] [Download PDF] [DOI]
This paper presents an overview of the Alphonse methodology for Assessing Human Factors for Drone Operations in a Simulation Environment.
@InProceedings{doroftei2022a, author = {Doroftei, Daniela and De Cubber, Geert and De Smet, Hans}, booktitle = {Human Factors in Robots, Drones and Unmanned Systems - AHFE (2022) International Conference}, title = {Assessing Human Factors for Drone Operations in a Simulation Environment}, year = {2022}, month = jul, volume = {57}, editor = {Tareq Ahram and Waldemar Karwowski}, publisher = {AHFE International}, address = {New York, USA}, url = {https://openaccess-api.cms-conferences.org/articles/download/978-1-958651-33-9_16}, abstract = {This paper presents an overview of the Alphonse methodology for Assessing Human Factors for Drone Operations in a Simulation Environment.}, doi = {http://doi.org/10.54941/ahfe1002319}, project = {Alphonse}, unit= {meca-ras} }
2021
- D. Doroftei, T. De Vleeschauwer, S. L. Bue, M. Dewyn, F. Vanderstraeten, and G. De Cubber, “Human-Agent Trust Evaluation in a Digital Twin Context," in 2021 30th IEEE International Conference on Robot Human Interactive Communication (RO-MAN), Vancouver, BC, Canada, 2021, pp. 203-207.
[BibTeX] [Download PDF] [DOI]@INPROCEEDINGS{9515445, author={Doroftei, Daniela and De Vleeschauwer, Tom and Bue, Salvatore Lo and Dewyn, Michaël and Vanderstraeten, Frik and De Cubber, Geert}, booktitle={2021 30th IEEE International Conference on Robot Human Interactive Communication (RO-MAN)}, title={Human-Agent Trust Evaluation in a Digital Twin Context}, year={2021}, volume={}, number={}, pages={203-207}, url={https://www.researchgate.net/profile/Geert-De-Cubber/publication/354078858_Human-Agent_Trust_Evaluation_in_a_Digital_Twin_Context/links/61430bd22bfbd83a46cf2b8c/Human-Agent-Trust-Evaluation-in-a-Digital-Twin-Context.pdf?_sg%5B0%5D=BdEPB9AGDUV3sOwnEQKCr-DgWRA7uDNeMlvyQYNaMPGSO2bhCDbyG4AENXXxH3j323ypYTq9nMftVbDr2fsCSA.ePETOgrc5VHnE0GK_yjBK1XVVfdQ9S6g2UKVfg8Z8miIkGlMPXpzaYKlB0JPDSiroGp9QoFbmcY2egYAXbL1ZQ&_sg%5B1%5D=ykQnQS2LN8fUQXAYx5Fpiy2NXqIwqO1UyVCENkpSUUWZn8Qqgrelh1bb4ry9Q9XPgCts7lVXU1_68YLjqnCPh4seSzWfG5BpKHc3MuFwsK6l.ePETOgrc5VHnE0GK_yjBK1XVVfdQ9S6g2UKVfg8Z8miIkGlMPXpzaYKlB0JPDSiroGp9QoFbmcY2egYAXbL1ZQ&_iepl=}, project={Alphonse}, publisher={IEEE}, address={Vancouver, BC, Canada}, month=aug, doi={10.1109/RO-MAN50785.2021.9515445}, unit= {meca-ras}} - Y. Baudoin, G. De Cubber, and E. Cepolina, “Mobile Robots Supporting Risky Interventions, Humanitarian actions and Demining, in particular the promising DISARMADILLO Tool," in Proceedings of TC17-VRISE2021 – A VIRTUAL Topical Event of Technical Committee on Measurement and Control of Robotics (TC17), International Measurement Confederation (IMEKO), Theme: “Robotics for Risky Interventions and Environmental Surveillance", Houston, TX, USA, 2021, pp. 5-6.
[BibTeX] [Download PDF]@INPROCEEDINGS{knvrise, author={Baudoin, Yvan and De Cubber, Geert and Cepolina, Emanuela}, booktitle={Proceedings of TC17-VRISE2021 - A VIRTUAL Topical Event of Technical Committee on Measurement and Control of Robotics (TC17), International Measurement Confederation (IMEKO), Theme: "Robotics for Risky Interventions and Environmental Surveillance"}, title={Mobile Robots Supporting Risky Interventions, Humanitarian actions and Demining, in particular the promising DISARMADILLO Tool}, year={2021}, volume={}, number={}, pages={5-6}, url={https://mecatron.rma.ac.be/pub/2021/TC17-VRISE2021-Abstract%20Proceedings.pdf}, project={AIDED, Alphonse, MarSur, SSAVE, MarLand, iMUGs, ICARUS, TIRAMISU}, publisher={IMEKO}, address={Houston, TX, USA}, month=oct, unit= {meca-ras} }
2020
- D. Doroftei, G. De Cubber, and H. De Smet, “Reducing drone incidents by incorporating human factors in the drone and drone pilot accreditation process," in Advances in Human Factors in Robots, Drones and Unmanned Systems, San Diego, USA, 2020, p. 71–77.
[BibTeX] [Abstract] [Download PDF] [DOI]
Considering the ever-increasing use of drones in a plentitude of application areas, the risk is that also an ever-increasing number of drone incidents would be ob-served. Research has shown that a large majority of all incidents with drones is due not to technological, but to human error. An advanced risk-reduction meth-odology, focusing on the human element, is thus required in order to allow for the safe use of drones. In this paper, we therefore introduce a novel concept to pro-vide a qualitative and quantitative assessment of the performance of the drone op-erator. The proposed methodology is based on one hand upon the development of standardized test methodologies and on the other hand on human performance modeling of the drone operators in a highly realistic simulation environment.
@InProceedings{doroftei2020alphonse, author = {Doroftei, Daniela and De Cubber, Geert and De Smet, Hans}, booktitle = {Advances in Human Factors in Robots, Drones and Unmanned Systems}, title = {Reducing drone incidents by incorporating human factors in the drone and drone pilot accreditation process}, year = {2020}, month = jul, editor = {Zallio, Matteo}, publisher = {Springer International Publishing}, pages = {71--77}, isbn = {978-3-030-51758-8}, organization = {AHFE}, address = {San Diego, USA}, abstract = {Considering the ever-increasing use of drones in a plentitude of application areas, the risk is that also an ever-increasing number of drone incidents would be ob-served. Research has shown that a large majority of all incidents with drones is due not to technological, but to human error. An advanced risk-reduction meth-odology, focusing on the human element, is thus required in order to allow for the safe use of drones. In this paper, we therefore introduce a novel concept to pro-vide a qualitative and quantitative assessment of the performance of the drone op-erator. The proposed methodology is based on one hand upon the development of standardized test methodologies and on the other hand on human performance modeling of the drone operators in a highly realistic simulation environment.}, doi = {10.1007/978-3-030-51758-8_10}, unit= {meca-ras}, project = {Alphonse}, url = {http://mecatron.rma.ac.be/pub/2020/Reducing%20drone%20incidents%20by%20incorporating%20human%20factors%20in%20the%20drone%20and%20drone%20pilot%20accreditation%20process.pdf}, }
2019
- D. Doroftei and H. De Smet, “Evaluating Human Factors for Drone Operations using Simulations and Standardized Tests," in 10th International Conference on Applied Human Factors and Ergonomics (AHFE 2019), Washington DC, USA, 2019.
[BibTeX] [Abstract] [Download PDF] [DOI]
This poster publication presents an overview of the Alphonse project on the development of new training curricula to reduce the number of drone incidents due to human error.
@InProceedings{doroftei2019alphonse, author = {Doroftei, Daniela and De Smet, Han}, booktitle = {10th International Conference on Applied Human Factors and Ergonomics (AHFE 2019)}, title = {Evaluating Human Factors for Drone Operations using Simulations and Standardized Tests}, year = {2019}, month = jul, organization = {AHFE}, publisher = {Springer}, address = {Washington DC, USA}, abstract = {This poster publication presents an overview of the Alphonse project on the development of new training curricula to reduce the number of drone incidents due to human error.}, doi = {10.5281/zenodo.3742199}, project = {Alphonse}, url = {http://mecatron.rma.ac.be/pub/2019/Poster_Alphonse_Print.pdf}, unit= {meca-ras} }