Developing a Configurable Air Traffic Controller Agent for Fast-Time Simulation

Bodoh, David; Britan, Clark; Coats, Paden

doi:10.1109/icns50378.2020.9222972

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…Different research studies explained the importance of x applying advanced control systems and Artificial Intelligence (AI) algorithms to enhance the variety of capabilities of UAM systems [16][17][18]. Several proposals have been developed related to trajectory conflict detection and avoidance mechanisms, smart path planning, and configurable air traffic control approaches [19][20][21]. Many research contributions are using image processing and integrated computer vision to strengthen the landing safety of drones.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Prognostic Malfunction Based Processor for Autonomous Landing Guidance Assistance System Using FPGA

Ahmed,

Wyatt

2024

IEEE Access

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The demand for more developed and agile urban taxi drones is increasing rapidly nowadays to sustain crowded cities and their traffic issues. The critical factor for spreading such technology could be related to the safety criteria that must be considered. One of the most critical safety aspects for such vertical and/or Short Take-Off and Landing (V/STOL) drones is related to safety during the landing stage, in which most of the recent flight accidents have occurred. This paper focused on solving this issue by proposing decentralized processing cores that could improve the landing failure rate by depending on a Fuzzy Logic System (FLS) and additional Digital Signal Processing (DSP) elements. Also, the proposed system will enhance the safety factor during the landing stages by adding a self-awareness feature in case a certain sensor malfunction occurs using the proposed Adaptive Prognostic Malfunction Unit (APMU). This proposed coarse-grained Autonomous Landing Guidance Assistance System (ALGAS4) processing architecture has been optimized using different optimization techniques. The ALGAS4 architecture has been designed completely using VHDL, and the targeted FPGA was the INTEL Cyclone V 5CGXFC9D6F27C7 chip. According to the synthesis findings of the INTEL Quartus Prime software, the maximum working frequency of the ALGAS4 system is 278.24 MHz. In addition, the proposed ALGAS4 system could maintain a maximum computing performance of approximately 74.85 GOPS while using just 166.56 mW for dynamic and I/O power dissipation.

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Section: Related Workmentioning

confidence: 99%

Adaptive Prognostic Malfunction Based Processor for Autonomous Landing Guidance Assistance System Using FPGA

Ahmed,

Wyatt

2024

IEEE Access

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“…Different research studies explained the significant rule of applying advanced control systems and Artificial Intelligence (AI) algorithms to enhance variety of capabilities of UAM systems [10][11][12]. This includes propositions related to trajectory conflict detection and avoidance mechanisms, smart path planning, and configurable air traffic techniques [13][14][15]. However, one of the most critical safety concerns for the next UAM systems will be related to the efficiency of the autonomous landing process.…”

Section: Related Workmentioning

confidence: 99%

“…Different research studies explained the importance of applying advanced control systems and Artificial Intelligence (AI) algorithms to enhance the variety of capabilities of UAM systems [16][17][18]. Several proposals have been developed related to trajectory conflict detection and avoidance mechanisms, smart path planning, and configurable air traffic control approaches [19][20][21]. Many research contributions are using image processing and integrated computer vision to strengthen the landing safety of drones.…”

Section: Related Workmentioning

confidence: 99%

Segregated FLS Processing Cores for V/STOL Autonomous Landing Guidance Assistant System Using FPGA

Ahmed

2021

2021 Integrated Communications Navigation and Surveillance Conference (ICNS)

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To keep up with today's dense metropolitan areas and their accompanying traffic problems, a growing number of towns are looking for more advanced and swift urban taxi drones. The safety parameters that must be taken into consideration may be the most important element in the widespread use of such technology. Most recent aviation mishaps have happened during the landing phase, making this a particularly important safety consideration for Vertical and/or Short Take-Off and Landing (V/STOL) drones. In this study, we focused on improving the fault tolerance of the processor architectures used by the predecessors of Autonomous Landing Guidance Assistance Systems (ALGAS), which in turn improves their decision-making capabilities. Furthermore, this is achieved by proposing a fault-tolerant processing architecture that depends on the Gamma Distribution Sliding Window Unit (GDSWU). This proposed GDSWU has been designed completely using VHDL, and the targeted FPFA was the Intel Cyclone V 5CGXFC9D6F27C7 chip. The GDSWU could operate at a maximum frequency of 369.96 MHz, as calculated by the synthesis results of the INTEL Quartus Prime program. The suggested GDSWU core only requires 20.36 mW for dynamic core and I/O power consumption.

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Developing a Configurable Air Traffic Controller Agent for Fast-Time Simulation

Cited by 2 publications

References 4 publications

Adaptive Prognostic Malfunction Based Processor for Autonomous Landing Guidance Assistance System Using FPGA

Adaptive Prognostic Malfunction Based Processor for Autonomous Landing Guidance Assistance System Using FPGA

Segregated FLS Processing Cores for V/STOL Autonomous Landing Guidance Assistant System Using FPGA

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