Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation

Singh, Victor; Willcox, Karen

doi:10.2514/6.2016-4124

Cited by 10 publications

(11 citation statements)

References 26 publications

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“…The closely related field of non-destructive evaluation also benefited from standard ANN techniques [82], though this field is not treated herein. Further, these methods were used for more than two decades in applications such as active structures control [83][84][85], and even for present-day flight control of drones [86]. Today, DL is bringing entirely new capabilities to structures and mechanics analysis.…”

Section: Mechanics Mechanical Properties and Structures Analysismentioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

267

126

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The fields of machining learning and artificial intelligence are rapidly expanding, impacting nearly every technological aspect of society. Many thousands of published manuscripts report advances over the last 5 years or less. Yet materials and structures engineering practitioners are slow to engage with these advancements. Perhaps the recent advances that are driving other technical fields are not sufficiently distinguished from long-known informatics methods for materials, thereby masking their likely impact to the materials, processes, and structures engineering (MPSE). Alternatively, the diverse nature and limited availability of relevant materials data pose obstacles to machine-learning implementation. The glimpse captured in this overview is intended to draw focus to selected distinguishing advances, and to show that there are opportunities for these new technologies to have transformational impacts on MPSE. Further, there are opportunities for the MPSE fields to contribute understanding to the emerging machine-learning tools from a physics basis. We suggest that there is an immediate need to expand the use of these new tools throughout MPSE, and to begin the transformation of engineering education that is necessary for ongoing adoption of the methods.

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Section: Mechanics Mechanical Properties and Structures Analysismentioning

confidence: 99%

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Dimiduk

Holm

Niezgoda

2018

Integr Mater Manuf Innov

267

126

View full text Add to dashboard Cite

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“…Zhao used the simulated annealing algorithm intelligent algorithm as the guiding ideology of the path planning [19]. Singh and Willcox introduced an unmanned aerial vehicle path planning method using dynamic data to drive flight capability estimation [20]. Croce et al used the VRP and the PCP as two sequential decision levels adopted by the same decision-maker [21].…”

Section: Related Workmentioning

confidence: 99%

Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

Zhang

Meng

et al. 2022

Journal of Advanced Transportation

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In civil aviation flight path planning, in order to effectively reduce the safety threat caused by the volcanic ash area to the civil aviation flight, factors such as the speed and acceleration of the aircraft in the volcanic ash area must be considered. In this paper, we propose an improved A-star algorithm by adopting the concept of potential collision set and using the velocity vector idea of optimal reciprocal collision avoidance (ORCA). The improved A-star algorithm selects the optimal speed range of the aircraft from the limited elements, obtains the speed and acceleration set of the aircraft in the volcanic ash area, calculates the flight path through the displacement increment, continuously refreshes the global starting point to the target point, and finally obtains the final path planning results by calculating the total cost value. The improved A-star algorithm is used to plan two flight paths from Madrid to Cairo and Algiers to Rome in volcanic ash areas. The verification results show that the improved A-star algorithm optimizes the flight path planning in the volcanic ash area and has the advantages of less search nodes, a small search range, and short computing time.

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“…Partially Observable Markov Decision Processes (POMDPs) have also been used for a variety of simultaneous localization and planning applications of ground or aerial vehicles where the focus is local closed-loop control [31], [32]. Building on top of the MDP framework, reinforcement learning (RL) algorithms [33], [34], [35], [36], [37], [38], [39] have also been used for path planning. GPU-accelerated RL libraries have also been developed recently [40], [41].…”

Section: A Previous Progress In Optimal Path Planningmentioning

confidence: 99%

Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows using a GPU-Accelerated Algorithm

Chowdhury,

Subramani

2021

Preprint

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Autonomous marine vehicles play an essential role in many ocean science and engineering applications. Planning time and energy optimal paths for these vehicles to navigate in stochastic dynamic ocean environments is essential to reduce operational costs. In some missions, they must also harvest solar, wind, or wave energy (modeled as a stochastic scalar field) and move in optimal paths that minimize net energy consumption. Markov Decision Processes (MDPs) provide a natural framework for sequential decision making for robotic agents in such environments. However, building a realistic model and solving the modeled MDP becomes computationally expensive in large-scale real-time applications, warranting the need of parallel algorithms and efficient implementation. In the present work, we introduce an efficient end-to-end GPU-accelerated algorithm that (i) builds the MDP model (computing transition probabilities and expected one-step rewards); and (ii) solves the MDP to compute an optimal policy. We develop methodical and algorithmic solutions to overcome the limited global memory of GPUs by (i) using a dynamic reduced-order representation of the ocean flows, (ii) leveraging the sparse nature of the state transition probability matrix, (iii) introducing a neighbouring sub-grid concept and (iv) proving that it is sufficient to use only the stochastic scalar field's mean to compute the expected one-step rewards for missions involving energy harvesting from the environment; thereby saving memory and reducing the computational effort. We demonstrate the algorithm on a simulated stochastic dynamic environment and highlight that it builds the MDP model and computes the optimal policy 600-1000x faster than conventional CPU implementations, making it suitable for real-time use.

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Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation

Cited by 10 publications

References 26 publications

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Perspectives on the Impact of Machine Learning, Deep Learning, and Artificial Intelligence on Materials, Processes, and Structures Engineering

Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

Optimal Path Planning of Autonomous Marine Vehicles in Stochastic Dynamic Ocean Flows using a GPU-Accelerated Algorithm

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