Model Predictive Static Programming Rendezvous Trajectory Generation of Unmanned Aerial Vehicles

Zheng, Huixin; Hong, Haichao; Tang, Shijie

doi:10.1109/icus48101.2019.8996047

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…An unmanned autonomous intelligent system (UAIS) [80][81][82][83][84][85][86][87][88] is defined as a mechatronic system capable of fulfilling universal tasks without or with limited human involvement. Typical UAIS include connected autonomous vehicles [89], swarm robot systems [90], unmanned aerial vehicles [91], unmanned ground vehicles, unmanned maritime vehicles [92], smart manufacturering systems [93][94][95][96], agricultural robots [97][98][99][100][101], service robots, and medical robots [102][103][104].…”

Section: Application Of Uav-aided Non-terrestrial Network In Uaismentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

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Section: Application Of Uav-aided Non-terrestrial Network In Uaismentioning

confidence: 99%

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Wang,

Guo,

Gao

2024

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“…Since there are an infinite number of solutions for (6), it is reasonable to formulate an optimal control problem for finding an feasible solution. Moreover, by designing the cost function, terminal control constraints can be achieved in a soft manner [13]:…”

Section: A Computation Of the Newton Stepmentioning

confidence: 99%

Smooth Sub-Optimal Trajectory Generation for Transition Maneuvers

et al. 2020

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In this paper, a sub-optimal trajectory optimization method is developed to generate trajectories for transition phases connecting two steady flights. Both control histories as well as the time step length that specify the trajectory for a transition maneuver are calculated as a root of an underdetermined system. Here, the unknowns are computed by a series of Newton iterations for finding the root, where explicit closed-form expressions are utilized within the algorithm to ensure computational efficiency. Moreover, a line-search strategy is incorporated to enhance the robustness. The terminal hard output constraints are guaranteed by the root finding, and the terminal control constraints are realized by using a time-varying weighting matrix in the cost function. Numerical simulations investigate multiple scenarios of transition phases, which show the effectiveness of the proposed method.

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“…The MPSP method has been used in many applications, such as in the trajectory control of launch vehicles, reentry guidance, cooperative control, etc. [18][19][20]. Typically, the MPSP method aims to improve the terminal accuracy by iteratively discretizing and updating.…”

Section: Introductionmentioning

confidence: 99%

“…(1) To our best knowledge, no existing methods have applied the EKF with the T-MPSP to solve the trajectory problems of aerospace vehicles. (2) Compared with the MPSP method in [18][19][20], the proposed method has a fast computing speed and high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Algorithm for the Aerospace Vehicle Based on Improved T-MPSP

Shan

Cheng

et al. 2023

Mathematics

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To deal with the uncertainty and disturbance that exist in the tracking system of an aerospace vehicle, an adaptive trajectory-tracking method based on a novel tracking model predictive static programming (T-MPSP) is proposed. Firstly, to make the proposed method more adaptive to uncertain parameter deviations, an extended Kalman filter (EKF) parameter correction strategy is designed. Then, the control constraints are considered to form a novel T-MPSP algorithm. By combining the parameter correction strategy with the improved T-MPSP algorithm, a novel adaptive tracking guidance scheme is presented. Finally, simulations are carried out to demonstrate the effectiveness of the proposed method.

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Model Predictive Static Programming Rendezvous Trajectory Generation of Unmanned Aerial Vehicles

Cited by 4 publications

References 18 publications

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Smooth Sub-Optimal Trajectory Generation for Transition Maneuvers

Adaptive Trajectory Tracking Algorithm for the Aerospace Vehicle Based on Improved T-MPSP

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