Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water

Su, Xichao; Wu, Yu; Guo, Fei; Cui, Jiapeng; Ge, Yang

doi:10.1177/1729881421992258

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…In [Su et al 2021], trajectory optimization for an HUAUV navigating between air and water is addressed. An improved teaching-and learning-based optimization (ITLBO) algorithm is proposed to minimize position and velocity errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency

Pinheiro,

A. Neto,

Drews Jr.

2023

Anais Estendidos Do XV Simpósio Brasileiro De Robótica E XX Simpósio Latino-Americano De Robótica (SBR/LARS Estendido 2023)

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency

Pinheiro,

A. Neto,

Drews Jr.

2023

Anais Estendidos Do XV Simpósio Brasileiro De Robótica E XX Simpósio Latino-Americano De Robótica (SBR/LARS Estendido 2023)

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“…7 The obtained results have shown the efficiency of the used control scheme. In the research study, 8 the trajectory optimization of a reconfigurable Unmanned Aerial Aquatic (UAA) vehicle was investigated. To achieve this goal, a Teaching and Learning Based Optimization (TLBO) algorithm was proposed.…”

Section: Related Workmentioning

confidence: 99%

Adaptive integral backstepping control of a reconfigurable quadrotor with variable parameters’ estimation

Derrouaoui

Bouzid

Guiatni

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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This work presents the control of a quadrotor capable of changing its configuration while flying. The designed controller should handle several arising issues since the drone geometric parameters are varying over time. This variation has a great influence on the quadrotor control, which prompts us to propose an efficient nonlinear adaptive integral backstepping controller. The control architecture includes an estimator of the inertia and other specific blocks to calculate the center of gravity and the mixing matrix. The performance of the proposed control approach has been compared with the conventional integral backstepping controller in two cases, with and without disturbances. The obtained results of the inertias and the center of gravity are also compared and confirmed with those calculated using Computer-Assisted Design–based model.

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“…Finally, Wu et al [26] proposed an improved teaching-learning-based optimization (ITLBO) algorithm to strengthen the influence of individual historical optimal solutions in an environment-induced multi-phase trajectory optimization problem focused on the underwater target-tracking task. Recently, Su et al, [27] improved the (ITLBO)based trajectory optimization changing the optimization function. The previous works in the field do not consider obstacles, and they are not suitable for real-time application.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition

Pinheiro¹,

Neto²,

Grando³

et al. 2021

Preprint

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In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different levels of mechanical adaptation. However, most of this literature is concentrated on physical design, practical issues of construction, and, more recently, low-level control strategies. Little has been done in the context of high-level intelligence, such as motion planning and interactions with the real world. Therefore, we proposed in this paper a trajectory planning approach that allows collision avoidance against unknown obstacles and smooth transitions between aerial and aquatic media. Our method is based on a variant of the classic Rapidly-exploring Random Tree, whose main advantages are the capability to deal with obstacles, complex nonlinear dynamics, model uncertainties, and external disturbances. The approach uses the dynamic model of the HyDrone, a hybrid vehicle proposed with high underwater performance, but we believe it can be easily generalized to other types of aerial/aquatic platforms. In the experimental section, we present simulated results in environments filled with obstacles, where the robot is commanded to perform different media movements, demonstrating the applicability of our strategy.

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Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water

Cited by 8 publications

References 22 publications

Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency

Trajectory Planning of an Aerial-Underwater Hybrid Vehicle Based on Heuristics for Energy Efficiency

Adaptive integral backstepping control of a reconfigurable quadrotor with variable parameters’ estimation

Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition

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