Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions
The development of autonomous systems, such as Unmanned Aerial Vehicles (UAVs), has witnessed significant growth in recent years. Their deployment in emergency response missions has become increasingly crucial due to their potential benefits, such as faster response times. However, these benefits al...
- Autores:
-
Pardo González, Germán Roberto
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/67649
- Acceso en línea:
- http://hdl.handle.net/1992/67649
- Palabra clave:
- Column Generation
Q-Learning
Reinforcement Learning
OR in emergency response
Drone Deployment
Multi-objective optimisation
Ingeniería
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nd/4.0/
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Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| title |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| spellingShingle |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions Column Generation Q-Learning Reinforcement Learning OR in emergency response Drone Deployment Multi-objective optimisation Ingeniería |
| title_short |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| title_full |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| title_fullStr |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| title_full_unstemmed |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| title_sort |
Fair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missions |
| dc.creator.fl_str_mv |
Pardo González, Germán Roberto |
| dc.contributor.advisor.none.fl_str_mv |
Gómez Castro, Camilo Hernando Rodríguez Herrera, Carlos Francisco |
| dc.contributor.author.none.fl_str_mv |
Pardo González, Germán Roberto |
| dc.contributor.jury.none.fl_str_mv |
Gómez Castro, Camilo Hernando Alvarez Martínez, David Lozano, Carlos |
| dc.subject.keyword.none.fl_str_mv |
Column Generation Q-Learning Reinforcement Learning OR in emergency response Drone Deployment Multi-objective optimisation |
| topic |
Column Generation Q-Learning Reinforcement Learning OR in emergency response Drone Deployment Multi-objective optimisation Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
The development of autonomous systems, such as Unmanned Aerial Vehicles (UAVs), has witnessed significant growth in recent years. Their deployment in emergency response missions has become increasingly crucial due to their potential benefits, such as faster response times. However, these benefits also raise ethical considerations that must be addressed when utilizing them in emergency scenarios. This work aims to contribute to the fair automation of UAVs in disaster response missions. To achieve this, the Column Generation method is employed to solve the initial routing problem within a specific grid. The primary objectives of this approach are twofold: to maximise population coverage and maximise fairness in the distribution of coverage, with a focus on assisting the most vulnerable individuals. Since the data accounts for two conflicting objectives, only Pareto-optimal solutions are considered and compared based on the assigned weights for each objective. Moreover, the Q-Learning algorithm is utilised to dynamically evaluate and prioritise newly discovered survivors who were not initially expected. This algorithm enables the agent to adapt and make decisions based on the vulnerability of the survivors. In order to address uncertainties inherent in emergency situations, a random variable is incorporated, enabling the agent to learn and make decisions based on new information. |
| publishDate |
2023 |
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2023-06-16T19:54:04Z |
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2023-06-16T19:54:04Z |
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2023-06-16 |
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Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/coar/resource_type/c_7a1f |
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eng |
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| dc.relation.references.es_CO.fl_str_mv |
Aggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 270-299. doi:https://doi.org/10.1016/j.comcom.2019.10.014 Anastasiou, A., Kolios, P., Panayiotou, C., & Papadaki, K. (2020). Swarm Path Planning for the Deployment of Drones in Emergency Response Missions. 2020 International Conference on Unmanned Aircraft Systems (ICUAS), (pp. 456-465). Athens. doi:10.1109/ICUAS48674.2020.9213876 Arnold, R. D., Yamaguchi, H., & Tanaka, T. (2018). Search and rescue with autonomous flying robots through behavior-based cooperative intelligence. Journal of International Humanitarian Action, 3-18. doi:https://doi.org/10.1186/s41018-018-0045-4 Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., . . . Rahwan, I. (2018). The Moral Machine experiment. Nature, 59-64. doi:https://doi.org/10.1038/s41586-018-0637-6 Battistuzzi, L., Recchiuto, C. T., & Sgorbissa, A. (2021). Ethical concerns in rescue robotics: a scoping review. Ethics and Information Technology, 863-875. doi:https://doi.org/10.1007/s10676-021-09603-0 Brandao, M., Jirotka, M., Webb, H., & Luff, P. (2020). Fair navigation planning: A resource for characterizing and designing fairness in mobile robots. Artificial Intelligence. doi:https://doi.org/10.1016/j.artint.2020.103259 Ebina, T., & Kinjo, K. (2021). Approaching the social dilemma of autonomous vehicles with a general social welfare function. Engineering Applications of Artificial Intelligence. doi:https://doi.org/10.1016/j.engappai.2021.104390 Etzioni, A., & Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. The Journal of Ethics, 403-418. doi:DOI 10.1007/s10892-017-9252-2 Evans, K., de Moura, N., Chauvier, S., Chatila, R., & Dogan, E. (2020). Ethical Decision Making in Autonomous Vehicles: The AV Ethics project. Science and Engineering Ethics, 3285-3312. doi:https://doi.org/10.1007/s11948-020-00272-8 Feillet, D. (2010). A tutorial on column generation and branch-and-price for vehicle routing problems. A Quarterly Journal of Operations Research, 407-424. doi:https://doi.org/10.1007/s10288-010-0130-z Geisslinger, M., Poszler, F., & Lienkamp, M. (2023). An Ethical Trajectory Planning Algorithm for Autonomous Vehicles. Nature Machie Intelligence, 137-144. doi:https://doi.org/10.1038/s42256-022-00607-z Hussain, Q., Feng, H., Grzebieta, R., Brijs, T., & Olivier, J. (2019). The relationship between impact speed and the probability of pedestrian fatality during a vehicle-pedestrian crash: A systematic review and meta-analysis. Accident Analysis and Prevention, 241-249. doi:https://doi.org/10.1016/j.aap.2019.05.033 Lozano, L., Duque, D., & Medaglia, A. L. (2016). An Exact Algorithm for the Elementary Shortest Path Problem with Resource Constraints. Transportation Science, 348-357. doi:http://dx.doi.org/10.1287/trsc.2014.0582 Mavrotas, G. (2009). Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 455-465. Nie, J., Li, G., & Yang, J. (2015). A Study of Fatality Risk and Head Dynamic Response of Cyclist and Pedestrian Based on Passenger Car Accident Data Analysis and Simulations. Traffic Injury Prevention, 76-83. doi:10.1080/15389588.2014.881477 Powell, W. B. (2019). A unified framework for stochastic optimization. European Journal of Operational Research, 795-821. doi:https://doi.org/10.1016/j.ejor.2018.07.014 Powell, W. B. (2022). Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions. John Wiley & Sons. Saha, S., Vasegaard Elkjaer, A., Nielsen, I., Hapka, A., & Budzisz, H. (2021). UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities. Electronics. doi:https://doi.org/10.3390/electronics10101193 Sutton, R., & Barto, A. (2015). Reinforcement Learning: An Introduction. The MIT Press. Weidinger, L., Mckee, K. R., Everett, R., Huang, S., Zhu, T. O., Chadwick, M. J., . . . Gabriel, I. (2023). Using the Veil of Ignorance to align AI systems with principles of justice. doi:https://doi.org/10.1073/pnas.2213709120 |
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Al consultar y hacer uso de este recurso, está aceptando las condiciones de uso establecidas por los autoreshttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Castro, Camilo Hernandovirtual::17715-1Rodríguez Herrera, Carlos Franciscovirtual::17716-1Pardo González, Germán Roberto88fe26b2-f971-41d5-b6c4-df55364e9f6d600Gómez Castro, Camilo HernandoAlvarez Martínez, DavidLozano, Carlos2023-06-16T19:54:04Z2023-06-16T19:54:04Z2023-06-16http://hdl.handle.net/1992/67649instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/The development of autonomous systems, such as Unmanned Aerial Vehicles (UAVs), has witnessed significant growth in recent years. Their deployment in emergency response missions has become increasingly crucial due to their potential benefits, such as faster response times. However, these benefits also raise ethical considerations that must be addressed when utilizing them in emergency scenarios. This work aims to contribute to the fair automation of UAVs in disaster response missions. To achieve this, the Column Generation method is employed to solve the initial routing problem within a specific grid. The primary objectives of this approach are twofold: to maximise population coverage and maximise fairness in the distribution of coverage, with a focus on assisting the most vulnerable individuals. Since the data accounts for two conflicting objectives, only Pareto-optimal solutions are considered and compared based on the assigned weights for each objective. Moreover, the Q-Learning algorithm is utilised to dynamically evaluate and prioritise newly discovered survivors who were not initially expected. This algorithm enables the agent to adapt and make decisions based on the vulnerability of the survivors. In order to address uncertainties inherent in emergency situations, a random variable is incorporated, enabling the agent to learn and make decisions based on new information.Ingeniero MecánicoPregradoapplication/pdfengUniversidad de los AndesIngeniería MecánicaFacultad de IngenieríaDepartamento de Ingeniería MecánicaFair path planning for Unmanned Aerial Vehicles (UAVs) in emergency response missionsTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPColumn GenerationQ-LearningReinforcement LearningOR in emergency responseDrone DeploymentMulti-objective optimisationIngenieríaAggarwal, S., & Kumar, N. (2020). Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Computer Communications, 270-299. doi:https://doi.org/10.1016/j.comcom.2019.10.014Anastasiou, A., Kolios, P., Panayiotou, C., & Papadaki, K. (2020). Swarm Path Planning for the Deployment of Drones in Emergency Response Missions. 2020 International Conference on Unmanned Aircraft Systems (ICUAS), (pp. 456-465). Athens. doi:10.1109/ICUAS48674.2020.9213876Arnold, R. D., Yamaguchi, H., & Tanaka, T. (2018). Search and rescue with autonomous flying robots through behavior-based cooperative intelligence. Journal of International Humanitarian Action, 3-18. doi:https://doi.org/10.1186/s41018-018-0045-4Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., . . . Rahwan, I. (2018). The Moral Machine experiment. Nature, 59-64. doi:https://doi.org/10.1038/s41586-018-0637-6Battistuzzi, L., Recchiuto, C. T., & Sgorbissa, A. (2021). Ethical concerns in rescue robotics: a scoping review. Ethics and Information Technology, 863-875. doi:https://doi.org/10.1007/s10676-021-09603-0Brandao, M., Jirotka, M., Webb, H., & Luff, P. (2020). Fair navigation planning: A resource for characterizing and designing fairness in mobile robots. Artificial Intelligence. doi:https://doi.org/10.1016/j.artint.2020.103259Ebina, T., & Kinjo, K. (2021). Approaching the social dilemma of autonomous vehicles with a general social welfare function. Engineering Applications of Artificial Intelligence. doi:https://doi.org/10.1016/j.engappai.2021.104390Etzioni, A., & Etzioni, O. (2017). Incorporating Ethics into Artificial Intelligence. The Journal of Ethics, 403-418. doi:DOI 10.1007/s10892-017-9252-2Evans, K., de Moura, N., Chauvier, S., Chatila, R., & Dogan, E. (2020). Ethical Decision Making in Autonomous Vehicles: The AV Ethics project. Science and Engineering Ethics, 3285-3312. doi:https://doi.org/10.1007/s11948-020-00272-8Feillet, D. (2010). A tutorial on column generation and branch-and-price for vehicle routing problems. A Quarterly Journal of Operations Research, 407-424. doi:https://doi.org/10.1007/s10288-010-0130-zGeisslinger, M., Poszler, F., & Lienkamp, M. (2023). An Ethical Trajectory Planning Algorithm for Autonomous Vehicles. Nature Machie Intelligence, 137-144. doi:https://doi.org/10.1038/s42256-022-00607-zHussain, Q., Feng, H., Grzebieta, R., Brijs, T., & Olivier, J. (2019). The relationship between impact speed and the probability of pedestrian fatality during a vehicle-pedestrian crash: A systematic review and meta-analysis. Accident Analysis and Prevention, 241-249. doi:https://doi.org/10.1016/j.aap.2019.05.033Lozano, L., Duque, D., & Medaglia, A. L. (2016). An Exact Algorithm for the Elementary Shortest Path Problem with Resource Constraints. Transportation Science, 348-357. doi:http://dx.doi.org/10.1287/trsc.2014.0582Mavrotas, G. (2009). Effective implementation of the e-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 455-465.Nie, J., Li, G., & Yang, J. (2015). A Study of Fatality Risk and Head Dynamic Response of Cyclist and Pedestrian Based on Passenger Car Accident Data Analysis and Simulations. Traffic Injury Prevention, 76-83. doi:10.1080/15389588.2014.881477Powell, W. B. (2019). A unified framework for stochastic optimization. European Journal of Operational Research, 795-821. doi:https://doi.org/10.1016/j.ejor.2018.07.014Powell, W. B. (2022). Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions. John Wiley & Sons.Saha, S., Vasegaard Elkjaer, A., Nielsen, I., Hapka, A., & Budzisz, H. (2021). UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities. Electronics. doi:https://doi.org/10.3390/electronics10101193Sutton, R., & Barto, A. (2015). Reinforcement Learning: An Introduction. The MIT Press.Weidinger, L., Mckee, K. R., Everett, R., Huang, S., Zhu, T. O., Chadwick, M. J., . . . Gabriel, I. (2023). 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