Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency
Resumo
This work studies the trajectory planning of an unmanned hybrid aerial-underwater vehicle (HUAUV) called Hydrone, which is being developed by the Intelligent Robotics and Automation Group (NAUTEC) of the Federal University of Rio Grande (FURG). This study presents a new trajectory planning algorithm, based on closed-loop rapidly exploring random trees (CL-RRT). This algorithm is developed for an HUAUV and introduces two heuristics to improve its energy efficiency in hybrid tasks. Simulated experiments were carried out in 135 virtual scenarios, comparing three approaches: one without heuristics and two with the proposed heuristics. Simulated results demonstrate that using the heuristics can significantly reduce energy consumption and even improve the vehicle’s average speed during missions. In particular, in 95% of the scenarios, the lowest energy consumption was achieved by one of the two heuristic-based algorithms. This article concludes by summarizing the findings and identifying potential future research opportunities.Referências
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Chen, G., Liu, A., Hu, J., Feng, J., and Ma, Z. (2020). Attitude and altitude control of unmanned aerial-underwater vehicle based on incremental nonlinear dynamic inversion. IEEE Access, 8:156129–156138.
Drews, P. L., Neto, A. A., and Campos, M. F. (2009). A survey on aerial submersible vehicles. In Conference: IEEE/OES Oceans, pages 4244–2523.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Bortoluzzi, N. P., Pinheiro, P. M., Neto, A. A., and Drews, P. L. J. (2021). Deep reinforcement learning for mapless navigation of a hybrid aerial underwater vehicle with medium transition. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 1088–1094.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Pinheiro, P. M., Guerra, R. S., and Drews, P. L. J. (2022a). Deterministic and stochastic analysis of deep reinforcement learning for low dimensional sensing-based navigation of mobile robots. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 193–198.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Pinheiro, P. M., Guerra, R. S., and Drews, P. L. J. (2022b). Mapless navigation of a hybrid aerial underwater vehicle with deep reinforcement learning through environmental generalization. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 1–6.
Grando, R. B., Pinheiro, P. M., Bortoluzzi, N. P., da Silva, C. B., Zauk, O. F., Piñeiro, M. O., Aoki, V. M., Kelbouscas, A. L., Lima, Y. B., Drews, P. L., and Neto, A. A. (2020). Visual-based autonomous unmanned aerial vehicle for inspection in indoor environments. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pages 1–6.
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Horn, A. C., Pinheiro, P. M., Silva, C. B., Neto, A. A., and Drews-Jr, P. L. (2019). A study on configuration of propellers for multirotor-like hybrid aerial-aquatic vehicles. In 2019 19th International Conference on Advanced Robotics (ICAR), pages 173–178. IEEE.
Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., and How, J. P. (2009). Real-time motion planning with applications to autonomous urban driving. IEEE Transactions on control systems technology, 17(5):1105–1118.
Liang, X., Liu, C., and Zeng, Z. (2021). Multi-domain informative coverage path planning for a hybrid aerial underwater vehicle in dynamic environments. Machines, 9(11):278.
Lu, D., Xiong, C., Zeng, Z., and Lian, L. (2019). Adaptive dynamic surface control for a hybrid aerial underwater vehicle with parametric dynamics and uncertainties. IEEE Journal of Oceanic Engineering.
Ma, Z., Feng, J., and Yang, J. (2018). Research on vertical air–water trans-media control of hybrid unmanned aerial underwater vehicles based on adaptive sliding mode dynamical surface control. International Journal of Advanced Robotic Systems, 15(2):1729881418770531.
Pedroso, A. A., da Silva, A. C., Pinheiro, P. M., and Drews, P. L. J. (2022). Prototyping and construction of a hybrid unmanned aerial underwater vehicles. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 61–66.
Pinheiro, P. M., de Oliveira Evald, P. J. D., Grando, R. B., Neto, A. A., and Drews-Jr, P. L. J. (2023). Hybrid unmanned aerial underwater vehicles: A survey on concepts and technologies. Available at SSRN 4424715.
Pinheiro, P. M., Neto, A. A., Grando, R. B., Silva, C. B. d., Aoki, V. M., Cardoso, D. S., Horn, A. C., and Drews, P. L. (2022). Trajectory planning for hybrid unmanned aerial underwater vehicles with smooth media transition. Journal of Intelligent & Robotic Systems, 104(3):1–16.
Su, X., Wu, Y., Guo, F., Cui, J., and Yang, G. (2021). Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water. International Journal of Advanced Robotic Systems, 18(2):1729881421992258.
Wu, Y., Li, L., Su, X., and Cui, J. (2020). Multi-phase trajectory optimization for an aerial-aquatic vehicle considering the influence of navigation error. Engineering Applications of Artificial Intelligence, 89:103404.
Zhu, Z., Cheng, N., and Li, Q. (2018). Real-time motion planning for quadrotor mavs in three-dimensional complex environment. In 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), pages 1–6. IEEE.
Aoki, V. M., de Oliveira Evald, P. J. D., Pinheiro, P. M., da Silva, C. B., dos Santos Cardoso, D., Cunha, M. A. B., and Drews, P. L. J. (2022a). Exploring ground effect in a hybrid aerial-aquatic unmanned vehicle. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 37–42.
Aoki, V. M., De Oliveira Evald, P. J. D., Pinheiro, P. M., Da Silva, C. B., Dos Santos Cardoso, D., Cunha, M. A. B., and Drews, P. L. J. (2022b). Volume-based transition zone assessment of hybrid unmanned aerial-underwater vehicles. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 67–72.
Aoki, V. M., Pinheiro, P. M., Drews-Jr, P. L., Cunha, M. A., and Tuchtenhagen, L. G. (2021). Analysis of a hybrid unmanned aerial underwater vehicle considering the environment transition. In 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), pages 90–95. IEEE.
Arslan, O., Berntorp, K., and Tsiotras, P. (2017). Sampling-based algorithms for optimal motion planning using closed-loop prediction. In 2017 IEEE International Conference on Robotics and Automation (ICRA), pages 4991–4996. IEEE.
Chen, G., Liu, A., Hu, J., Feng, J., and Ma, Z. (2020). Attitude and altitude control of unmanned aerial-underwater vehicle based on incremental nonlinear dynamic inversion. IEEE Access, 8:156129–156138.
Drews, P. L., Neto, A. A., and Campos, M. F. (2009). A survey on aerial submersible vehicles. In Conference: IEEE/OES Oceans, pages 4244–2523.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Bortoluzzi, N. P., Pinheiro, P. M., Neto, A. A., and Drews, P. L. J. (2021). Deep reinforcement learning for mapless navigation of a hybrid aerial underwater vehicle with medium transition. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 1088–1094.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Pinheiro, P. M., Guerra, R. S., and Drews, P. L. J. (2022a). Deterministic and stochastic analysis of deep reinforcement learning for low dimensional sensing-based navigation of mobile robots. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 193–198.
Grando, R. B., de Jesus, J. C., Kich, V. A., Kolling, A. H., Pinheiro, P. M., Guerra, R. S., and Drews, P. L. J. (2022b). Mapless navigation of a hybrid aerial underwater vehicle with deep reinforcement learning through environmental generalization. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 1–6.
Grando, R. B., Pinheiro, P. M., Bortoluzzi, N. P., da Silva, C. B., Zauk, O. F., Piñeiro, M. O., Aoki, V. M., Kelbouscas, A. L., Lima, Y. B., Drews, P. L., and Neto, A. A. (2020). Visual-based autonomous unmanned aerial vehicle for inspection in indoor environments. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pages 1–6.
Horn, A. C., Pinheiro, P. M., Grando, R. B., da Silva, C. B., Neto, A. A., and Drews, P. L. (2020). A novel concept for hybrid unmanned aerial underwater vehicles focused on aquatic performance. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pages 1–6. IEEE.
Horn, A. C., Pinheiro, P. M., Silva, C. B., Neto, A. A., and Drews-Jr, P. L. (2019). A study on configuration of propellers for multirotor-like hybrid aerial-aquatic vehicles. In 2019 19th International Conference on Advanced Robotics (ICAR), pages 173–178. IEEE.
Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., and How, J. P. (2009). Real-time motion planning with applications to autonomous urban driving. IEEE Transactions on control systems technology, 17(5):1105–1118.
Liang, X., Liu, C., and Zeng, Z. (2021). Multi-domain informative coverage path planning for a hybrid aerial underwater vehicle in dynamic environments. Machines, 9(11):278.
Lu, D., Xiong, C., Zeng, Z., and Lian, L. (2019). Adaptive dynamic surface control for a hybrid aerial underwater vehicle with parametric dynamics and uncertainties. IEEE Journal of Oceanic Engineering.
Ma, Z., Feng, J., and Yang, J. (2018). Research on vertical air–water trans-media control of hybrid unmanned aerial underwater vehicles based on adaptive sliding mode dynamical surface control. International Journal of Advanced Robotic Systems, 15(2):1729881418770531.
Pedroso, A. A., da Silva, A. C., Pinheiro, P. M., and Drews, P. L. J. (2022). Prototyping and construction of a hybrid unmanned aerial underwater vehicles. In 2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE), pages 61–66.
Pinheiro, P. M., de Oliveira Evald, P. J. D., Grando, R. B., Neto, A. A., and Drews-Jr, P. L. J. (2023). Hybrid unmanned aerial underwater vehicles: A survey on concepts and technologies. Available at SSRN 4424715.
Pinheiro, P. M., Neto, A. A., Grando, R. B., Silva, C. B. d., Aoki, V. M., Cardoso, D. S., Horn, A. C., and Drews, P. L. (2022). Trajectory planning for hybrid unmanned aerial underwater vehicles with smooth media transition. Journal of Intelligent & Robotic Systems, 104(3):1–16.
Su, X., Wu, Y., Guo, F., Cui, J., and Yang, G. (2021). Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water. International Journal of Advanced Robotic Systems, 18(2):1729881421992258.
Wu, Y., Li, L., Su, X., and Cui, J. (2020). Multi-phase trajectory optimization for an aerial-aquatic vehicle considering the influence of navigation error. Engineering Applications of Artificial Intelligence, 89:103404.
Zhu, Z., Cheng, N., and Li, Q. (2018). Real-time motion planning for quadrotor mavs in three-dimensional complex environment. In 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), pages 1–6. IEEE.
Publicado
09/10/2023
Como Citar
PINHEIRO, Pedro M.; A. NETO, Armando; DREWS JR., Paulo L. J..
Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency. In: CONCURSO DE TESES E DISSERTAÇÕES EM ROBÓTICA - CTDR (MESTRADO) - SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO-AMERICANO DE ROBÓTICA (SBR/LARS), 15. , 2023, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 25-36.
DOI: https://doi.org/10.5753/sbrlars_estendido.2023.234779.
