Dynamics modeling and trajectory optimization for unmanned aerial-aquatic vehicle diving into the water

Wu, Yu; Li, Leilei; Su, Xichao; Gao, Bowen

doi:10.1016/j.ast.2019.04.004

Cited by 31 publications

(9 citation statements)

References 16 publications

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“…UAS with higher LoA would also help improve the efficiency of the UTM network. For individual operation, UAS with higher LoA is capable of better allocation of the resources for its mission and able to plan for shorter path [45] and form better air route network with smoother traffic flow [46]. That can shorten travel distance, thus reduce required flight time and energy accordingly.…”

Section: A Overview Of Loamentioning

confidence: 99%

Framework of Level-of-Autonomy-based Concept of Operations: UAS Capabilities

Pang

Wang

Low

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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The utility of unmanned aircraft system (UAS) is growing fast in recent years with applications like parcel delivery and security patrol. However, most of the current applications require the involvement of human operator, posing challenges for large-scale UAS deployment due to the operator's and air traffic control officer's (ATCO) limited cognitive capability to control and monitor the traffic. Autonomous UAS operation that reduces the operator and ATCO workload could be a promising solution to meet that challenge. The main aim of this paper, therefore, is to propose a conceptual framework for Level of Autonomy (LoA) based concept of operations (ConOps). The discussion includes the definition and evaluation of LoA and the investigation opportunities of enabling UAS operation at various LoA via case studies. This could be further extended for mixed operation with vehicles of multiple LoA. The framework is presented around core factors defining levels of autonomy for UAS and character for each level in terms of operational actions. Two use cases involving scheduled parcel delivery and non-scheduled emergency operation are presented to demonstrate the evaluation of LoA for a given operation. The proposed framework could serve to identify opportunities for research and engineering development of UAS operations in urban environments.

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Section: A Overview Of Loamentioning

confidence: 99%

Framework of Level-of-Autonomy-based Concept of Operations: UAS Capabilities

Pang

Wang

Low

2021

2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC)

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“…Other hybrid robots' studies have applied different techniques to deal with the motion planning problem. Wu et al [24] applied an adaptive and global-best guided cuckoo search (CS) algorithm, called improved CS (ICS) algorithm to plan for a water diving transition. Additionally, in a work by Wu [25], a coordinated path planning between a HUAUV and an Autonomous Underwater Vehicle (AUV) was developed using a particle swarm optimization algorithm.…”

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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“…To avoid the complex switchover control strategy in the course of aerial-aquatic navigation, the process of aerial-aquatic switchover is regarded as an uncontrolled motion, during which the states of UAAV are determined entirely by its terminal state in the air, and then, the motion of UAAV is controlled after it has been entering water completely. 19 The advantage of this strategy is that complex control system is not needed, and the instability caused by switchover between different control strategies is avoided. However, with the above control strategy, the desired trajectory of UAAV may not be generated as there are only several states which can be optimized.…”

Section: Introductionmentioning

confidence: 99%

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

Su¹,

Guo³

et al. 2021

International Journal of Advanced Robotic Systems

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Unmanned aerial–aquatic vehicles are a new type of aircraft that can navigate in air and underwater. An unmanned aerial–aquatic rotorcraft (UAAR) is introduced to complete the task of navigating between air and underwater, and the trajectory optimization problem for this task is focused on in this study. The dynamics of a four-axle rotorcraft with eight rotors operating in air and underwater is described. On this basis, the trajectory optimization model is established, wherein the constraints on control variables and states in different media are included. The optimization index is denoted as the weighted sum of the terminal states. In view of the weakness of the teaching- and learning-based optimization (TLBO) algorithm, the formula for updating the individual grade in the teaching process is modified. Thus, this ensures that the algorithm avoids converging at the local optimum and improves the solution quality. Finally, an improved TLBO (ITLBO)-based trajectory optimization method for UAAR navigating between air and water is developed. The control variables are discretized with respect to height at a set of Chebyshev collocation points to reduce the terminal error of states, and the values of control variables at other heights are obtained via interpolation. In the simulation studies, the ITLBO-based method exhibits better performance in terms of optimizing the index when compared to the other two algorithms. Furthermore, the effects of the distribution and number of collocation points on the results are analyzed.

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Dynamics modeling and trajectory optimization for unmanned aerial-aquatic vehicle diving into the water

Cited by 31 publications

References 16 publications

Framework of Level-of-Autonomy-based Concept of Operations: UAS Capabilities

Framework of Level-of-Autonomy-based Concept of Operations: UAS Capabilities

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

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

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