Aerial-underwater systems, a new paradigm in unmanned vehicles

Mercado, Diego; Maia, Marco M.; Díez, F. J.

doi:10.1109/icuas.2017.7991362

Cited by 22 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Esse trabalho é um dos primeiros acerca de HUAUVs. Atualmente, ainda existem poucos trabalhos desenvolvidos sobre HUAUVs, sendo que a maioria foca na mecânica e estrutura do veículo, havendo, dessa forma, pouco progresso no que diz respeito ao soft-ware e inteligência para o veículo e ao problema da transic ¸ão de meio [Ravell et al 2018] [Maia et al 2017] [Mercado et al 2019. Trabalhos relacionados a navegac ¸ão sem mapa para robôs terrestres, aéreos e subaquáticos são mais comuns.…”

Section: Trabalhos Relacionadosunclassified

“…Trabalhos relacionados a navegac ¸ão sem mapa para robôs terrestres, aéreos e subaquáticos são mais comuns. Os trabalhos mais relevantes acerca de HUAUVs partem de uma abordagem utilizando uma estrutura de quadrotor [Drews et al 2014], [Ma et al 2018], [Maia et al 2015], [Mercado et al 2019]. O HU-AUV descrito em simulac ¸ão neste trabalho levou em considerac ¸ão essa convergência de propostas, sendo baseado também em uma estrutura de quadrotor.…”

Section: Trabalhos Relacionadosunclassified

See 1 more Smart Citation

Aprendizado por Reforço Profundo para Navegação sem Mapa de um Veículo Híbrido Aéreo-Aquático

Grando¹,

Drews-Jr²

2021

Anais Estendidos Do XIII Simpósio Brasileiro De Robótica E XVIII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2021

View full text Add to dashboard Cite

Realizar a navegação entre meios é uma tarefa desafiadora para robôs móveis híbridos, especialmente em cenários com obstáculos. Este trabalho apresenta uma abordagem baseada em aprendizado por reforço profundo (Deep-RL) para navegação autônoma de um tipo específico de robô móvel híbrido: um Veículo Híbrido Tipo Ar-Água (HUAUV). A abordagem proposta utiliza somente informação de sensores de distância e de informações relativas à localização do veículo para realizar a navegação. Resultados obtidos mostram que é possível realizar navegação sem mapa do início ao fim sem usar nenhum tipo de operação manual, somente utilizando os agentes baseados em Deep-RL. A navegação dos agentes treinados é comparada com a navegação sem mapa realizada por um algoritmo clássico - BUG2. A abordagem é baseada em dois métodos do estado da arte para navegação sem mapa de robôs terrestres: Política de Gradiente Determinístico Profundo (DDPG) e Soft Actor-Critic (SAC).

show abstract

Section: Trabalhos Relacionadosunclassified

Aprendizado por Reforço Profundo para Navegação sem Mapa de um Veículo Híbrido Aéreo-Aquático

Grando¹,

Drews-Jr²

2021

Anais Estendidos Do XIII Simpósio Brasileiro De Robótica E XVIII Simpósio Latino Americano De Robótica (SBR/LARS Estendido 2021

View full text Add to dashboard Cite

show abstract

“…However, due to the huge differences in the physical properties between air and water, it is a great challenge to design a vehicle capable of both air flight and underwater navigation, especially concerning the power system, configuration, dynamics, and control. According to the existing literature, fixed-wing [5][6][7] , variable-swept wing [8,9] , flapping wing [10] , and multi-rotor systems [11][12][13][14][15][16][17][18] for aerial and underwater navigation have been proposed. Paulo [11] et al evaluated the controllability, payload/vehicle volume, environment switch-over, and structure modification of aerial and underwater vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Experimental results demonstrated that LOON COPTER was capable of seamless air-water transition. Mercado [14][15][16] proposed an aerial-underwater vehicle with an octoquadcopter configuration, named NAVIATOR. The trajectory tracking and attitude control systems of the above three UAUVs were controlled by PID, and the air flight, underwater navigation, and seamless transition functions were conducted successfully.…”

Section: Introductionmentioning

confidence: 99%

Attitude and Altitude Control of Unmanned Aerial-Underwater Vehicle Based on Incremental Nonlinear Dynamic Inversion

Chen

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

A vehicle able to navigate both in the air and underwater is referred to an unmanned aerialunderwater vehicle (UAUV). In this paper, the attitude and altitude control of UAUV with a quadrotor configuration during underwater navigation is studied theoretically, and an incremental dynamic inverse control (INDI) method with second-order low-pass filters is employed in the control system. Based on the underwater motion model of an UAUV and the incremental attitude and altitude control model, the relationship between thrust increment and vertical and angular acceleration increment is obtained through Taylor series expansion and angular acceleration feedback. Subsequently, attitude and altitude controllers based on nonlinear dynamic inversion (NDI) and INDI are designed and their robustness is analyzed. By introducing second-order low-pass filters at the part of control and feedback, the accuracy of the angular acceleration estimation is improved and the time synchronization of the control system is ensured. Simulation results demonstrated that when introducing external disturbances and uncertainties, the INDI control with second-order low-pass filters proposed in this paper can track the reference signal faster and more accurately than the NDI control. INDEX TERMS Unmanned aerial-underwater vehicle (UAUV), incremental nonlinear dynamic inversion (INDI), attitude control, nonlinear dynamics, low-pass filter

show abstract

“…Alzu'bi et al in Oakland University improved traditional quadrotors with additional water pumps for buoyancy and depth control underwater [ 10,11]. Based on X-4 configuration, Maia et al in Rutgers University utilized dual air rotors in each vehicle-arm with a column gap between the top and bottom motors, which highly improved effective lift force [11][12][13][14]. Brazilian researchers Drews et al changed bottom 4 air rotors into water ones in order to obtain better underwater performance [ 15,16].…”

Section: Introductionmentioning

confidence: 99%

Global optimization for Cross-domain aircraft based on Kriging model and par-ticle swarm optimization algorithm

et al. 2019

View full text Add to dashboard Cite

In order to further extend the working range of existed aircrafts, a new type of water-air cross-domain aircraft based on fixed-wing aircrafts with the addition of extra tandem ducted coaxial rotors and tail ducted propulsion system was herein proposed, which was driven by a hybrid power system. Due to its layout situation, the fluid characteristics underwater was especially analyzed based on CFD simulation experiments, which mainly aimed at the thrust force characteristics influenced by different layout parameters of tail ducts and different rotational velocities. In order to figure out the optimal layout which can reach the best performance underwater, a global layout optimization was accomplished with the former analysis results, which employed an optimization method based on PSO algorithm with the compensation of orthogonal test and Kriging model to decrease computational complexity and improve interpolation accuracy. The global optimal layout solution was finally obtained and validated to be reasonable through extra simulation experiments, and the proposed optimization method was also proved to be effective through the utilization for this studied case.

show abstract

Aerial-underwater systems, a new paradigm in unmanned vehicles

Cited by 22 publications

References 7 publications

Aprendizado por Reforço Profundo para Navegação sem Mapa de um Veículo Híbrido Aéreo-Aquático

Aprendizado por Reforço Profundo para Navegação sem Mapa de um Veículo Híbrido Aéreo-Aquático

Attitude and Altitude Control of Unmanned Aerial-Underwater Vehicle Based on Incremental Nonlinear Dynamic Inversion

Global optimization for Cross-domain aircraft based on Kriging model and par-ticle swarm optimization algorithm

Contact Info

Product

Resources

About