UAV Swarm Path Planning

James, Scott; Raheb, Robert; Hudak, Allison

doi:10.1109/icns50378.2020.9223005

Cited by 11 publications

(5 citation statements)

References 13 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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“…The current airspace management system requires a solution that integrates the high-density UAV traffic into the regular airspace traffic. Article [8] extended the aforementioned work by taking into consideration large groups of autonomous UAVs flying close together, i.e., swarms of UAVs. Each swarm is represented by a unique member called "locomotive".…”

Section: Related Workmentioning

confidence: 99%

Generalized Path Planning for UTM Systems With a Space-Time Graph

Papa

Cardei

2022

IEEE Open J. Intell. Transp. Syst.

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Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of generalized path planning for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner's quality of service and the efficiency of airspace usage.

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“…In [9] the study presented an algorithm for computing the control of swarm and modeling their distributed behavior. The examination and simulations have shown the communication latency effects on different scenarios.…”

Section: Related Workmentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

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The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

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UAV Swarm Path Planning

Cited by 11 publications

References 13 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

Generalized Path Planning for UTM Systems With a Space-Time Graph

Motion Planning of UAV Swarm: Recent Challenges and Approaches

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