A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Gul, Faiza; Mir, Imran; Abualigah, Laith; Sumari, Putra; Forestiero, Agostino

doi:10.3390/electronics10182250

Cited by 88 publications

(30 citation statements)

References 217 publications

(227 reference statements)

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“…Developing such control systems require well-characterized dynamic system models. Similar work [25,26] has been done in the field of ground robotics [8][9][10][11][12][13]27] as well. Wind tunnel testing (commonly used method for determining the parameters of these dynamic systems) is time-consuming and costly.…”

Section: Introductionmentioning

confidence: 76%

“…The rapidly increasing fleet of UAVs, along with the widening sphere of their utility, therefore presents a serious challenge for the designers to formulate unique optimal control strategies. However, technological advancements in the aviation sector [4][5][6][7] and ground control vehicles [8][9][10][11][12][13][14][15][16][17][18][19] paved the way for the development of hi-fidelity systems. These UAVs help researchers by providing means to collect multi-spectral information with limited resources and data collection times which is critical for time sensitive dynamic data [20].…”

Section: Introductionmentioning

confidence: 99%

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Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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Unmanned Aerial Vehicles (UAVs) are important tool for various applications, including enhancing target detection accuracy in various surface-to-air and air-to-air missions. To ensure mission success of these UAVs, a robust control system is needed, which further requires well-characterized dynamic system model. This paper aims to present a consolidated framework for the estimation of an experimental UAV utilizing flight data. An elaborate estimation mechanism is proposed utilizing various model structures, such as Autoregressive Exogenous (ARX), Autoregressive Moving Average exogenous (ARMAX), Box Jenkin’s (BJ), Output Error (OE), and state-space and non-linear Autoregressive Exogenous. A perspective analysis and comparison are made to identify the salient aspects of each model structure. Model configuration with best characteristics is then identified based upon model quality parameters such as residual analysis, final prediction error, and fit percentages. Extensive validation to evaluate the performance of the developed model is then performed utilizing the flight dynamics data collected. Results indicate the model’s viability as the model can accurately predict the system performance at a wide range of operating conditions. Through this, to the best of our knowledge, we present for the first time a model prediction analysis, which utilizes comprehensive flight dynamics data instead of simulation work.

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Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

et al. 2022

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“…IoT technology is receiving a lot of attention, and many technologies are being developed [10] and widely used such as UAV [11]. The Smart Mirror is one of those Internet-of-Things (IoT) devices made by inserting a board such as a mini PC into the basic structure of a mirror.…”

Section: Use Of Smart Mirrormentioning

confidence: 99%

Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

et al. 2022

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As the elderly population increases globally, the demand for systems and algorithms that target the elderly is increasing. Focusing on the extendibility of smart mirrors, our purpose is to create a motion detection system based on video input by an attached device (an RGB camera). The motion detection system presented in this paper is based on an algorithm that returns a Boolean value indicating the detection of motion based on skeletal information. We analyzed the problems that occur when the adjacent frame subtraction method (AFSM) is used in the motion detection algorithm based on the skeleton-related output of the pose estimation model. We compared and tested the motion recognition rate for slow-motion with the previously used AFSM and the vector sum method (VSM) proposed in this paper. As an experimental result, the slow-motion detection rate showed an increase of 30–70%.

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“…Many critical characteristics of UAVs have been classified, including top-level configuration, restricting altitude, mean take-off weight, autonomous level, and even ownership. Similar nature optimization can be traced for ground applications [12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

et al. 2022

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In this research, an intelligent control architecture for an experimental Unmanned Aerial Vehicle (UAV) bearing unconventional inverted V-tail design, is presented. To handle UAV’s inherent control complexities, while keeping them computationally acceptable, a variant of distinct Deep Reinforcement Learning (DRL) algorithm, namely Deep Deterministic Policy Gradient (DDPG) is proposed. Conventional DDPG algorithm after being modified in its learning architecture becomes capable of intelligently handling the continuous state and control space domains besides controlling the platform in its entire flight regime. Nonlinear simulations were then performed to analyze UAV performance under different environmental and launch conditions. The effectiveness of the proposed strategy is further demonstrated by comparing the results with the linear controller for the same UAV whose feedback loop gains are optimized by employing technique of optimal control theory. Results indicate the significance of the proposed control architecture and its inherent capability to adapt dynamically to the changing environment, thereby making it of significant utility to airborne UAV applications.

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A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

Cited by 88 publications

References 217 publications

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Data Driven Model Estimation for Aerial Vehicles: A Perspective Analysis

Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror

Deep Reinforcement Learning for Integrated Non-Linear Control of Autonomous UAVs

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