An empirical study on robustness of UAV path planning algorithms considering position uncertainty

Kang, Minyang; Yang, Liu; Ren, Yongmao; Zhao, Yijing; Zhang, Zheng

doi:10.1109/iske.2017.8258825

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Field experiments show that there can be significant positioning errors from the drone's GPS receiver(s) or gimbal. The authors in [20] characterized this position noise as a white Gaussian noise and assumed the position uncertainty at each waypoint to be independent from the previous ones. That is,…”

Section: F Positioning and Rtkmentioning

confidence: 99%

See 1 more Smart Citation

Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Salahat

Asselineau

Coventry

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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Solar energy is seen as a sustainable and nondepletable source of energy supply. Worldwide, large-scale solar power infrastructure is being installed every day. Such structures can suffer from many faults and defects that degrade their energy output during their operational life. Detecting such faults and defects requires regular inspection over physically large and distributed solar infrastructure. On-site manual human inspection tends to be impractical, risky and costly. As such, replacing humans with autonomous robotic aerial inspection systems has great potential. In this work, we propose an unmanned aerial vehicle (UAV) waypoint generation system that is specifically designed for aerial inspection of solar infrastructure. Our system takes into consideration the physical structure and the dynamic nature of sun-tracking solar modules and generates waypoints with the right camera viewing pose and drone orientation. Statistical methods are used to generate a randomly selected set of modules as a representation of the entire solar farm. The set is guaranteed to satisfy a user-defined confidence level and margin of error requirements. A path is generated to visit selected modules in an optimal way by deploying the traveling-salesman shortest path algorithm, allowing the vehicle to maximize battery use. Illustrative flights and preliminary inspection results are presented and discussed.

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Section: F Positioning and Rtkmentioning

confidence: 99%

“…where G (µ, σ) is a Gaussian process with mean µ and variance σ, which are respectively calculated as µ = E ( w k ) and σ = E ( w k − µ) ( w k − µ) [20]. In ( 6), E(−) is the expectation operator.…”

Section: F Positioning and Rtkmentioning

confidence: 99%

Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Salahat

Asselineau

Coventry

et al. 2019

IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society

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“…In cell decomposition methods, the operational area is partitioned into nonoverlapping similar shaped small regions called cells. The trajectory is generated connecting by straight lines the cell centres from the starting point to the arrival point using search algorithms like A*, PSO (Particle Swarm Optimization), RRT (Rapidlyexploring Random Tree) [4][5][6][7]. The cells occupied by obstacles are excluded in the trajectory generation process.…”

Section: Introductionmentioning

confidence: 99%

A minimum-time obstacle-avoidance path planning algorithm for unmanned aerial vehicles

2021

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In this article, we present a new strategy to determine an unmanned aerial vehicle trajectory that minimizes its flight time in presence of avoidance areas and obstacles. The method combines classical results from optimal control theory, i.e. the Euler-Lagrange Theorem and the Pontryagin Minimum Principle, with a continuation technique that dynamically adapts the solution curve to the presence of obstacles. We initially consider the two-dimensional path planning problem and then move to the three-dimensional one, and include numerical illustrations for both cases to show the efficiency of our approach.

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“…Whale optimization algorithm [35] • Sine cosine algorithm [36] Neural network training, image segmentation, and numerical function optimization [37] show improved performance in real-world problems. The metaheuristic algorithm integrated with Grey Wolf Optimization [38] was extended to implement 3D path planning of multiple UAVs.…”

mentioning

confidence: 99%

Optimized Path Planning Strategy to Enhance Security under Swarm of Unmanned Aerial Vehicles

Manikandan

Sriramulu

2022

Drones

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Unmanned Aerial Vehicles (UAVs) are widely deployed in military surveillance operations, especially the quadcopter UAVs which are easy to operate and considerably quieter. However, UAVs encounter problems in secure path planning during navigation and are prone to cyber security attacks. Further, due to the UAV battery capacity, the operating time for surveillance is limited. In this paper, we propose a novel Resilient UAV Path Optimization Algorithm (RUPOA) which provides an optimal path under security attacks such as denial-of-service (DoS) and Man-in-the-Middle (MITM) attacks. The performance efficiency of the proposed path planning algorithm is compared with the existing path planning algorithms based on execution time. To achieve secure path planning in UAVs and to mitigate security attacks, a blockchain-aided security solution is proposed. To prevent security attacks, smart contracts are generated where the devices are registered with gasLimit. The blockchain consensus mechanism allows for secure and tamper-free transmission of data between the Ground Control Station (GCS) and a swarm of UAVs. The performance efficiency of the blockchain model is evaluated based on network latency which is the total execution time across the blockchain network.

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An empirical study on robustness of UAV path planning algorithms considering position uncertainty

Cited by 8 publications

References 14 publications

Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

Waypoint Planning for Autonomous Aerial Inspection of Large-Scale Solar Farms

A minimum-time obstacle-avoidance path planning algorithm for unmanned aerial vehicles

Optimized Path Planning Strategy to Enhance Security under Swarm of Unmanned Aerial Vehicles

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