UAV-based Receding Horizon Control for 3D Inspection Planning

Papaioannou, Savvas; Kolios, Panayiotis; Theocharides, Theocharis; Panayiotou, Christos G.; Polycarpou, Marios M.

doi:10.1109/icuas54217.2022.9836051

Cited by 13 publications

(4 citation statements)

References 76 publications

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“…There are a lot of results in this field and applications in different fields, such as computer wiring [44], wallpaper cutting [45], hole punching [46], dartboard design [47], crystallography [48], and vehicle routing [44]. The most recent applications are finding the best route for the inspection of transmission infrastructure using Unmanned Aerial Vehicles (UAV) [49,50]. When the TSP is considered for UAVs, naturally, the classical discrete optimization techniques can be altered to continuous ones.…”

Section: Discussionmentioning

confidence: 99%

Best Proximity Points for p–Cyclic Infimum Summing Contractions

Hristov

Ilchev

Kopanov

et al. 2023

Axioms

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We investigate fixed points for p cyclic maps by introducing a new notion of p–cyclic infimum summing maps and a generalized best proximity point for p–cyclic maps. The idea generalizes some results about best proximity points in order to widen the class of sets and maps for which we can ensure the existence and uniqueness of best proximity points. The replacement of the classical notions of best proximity points and distance between the consecutive set arises from the well-known group traveling salesman problem and presents a different approach to solving it. We illustrate the new result with a map that does not satisfy the known results about best proximity maps for p–cyclic maps.

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Section: Discussionmentioning

confidence: 99%

Best Proximity Points for p–Cyclic Infimum Summing Contractions

Hristov

Ilchev

Kopanov

et al. 2023

Axioms

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“…Mixed-Integer Programming (MIP) is also a popular method in this field. Papaioannou et al [9] proposed a receding horizon coverage control approach for multiple autonomous aerial agents to cooperatively cover the total surface area of a 3D object of interest. They demonstrated the transformation of non-linear and non-convex visibility determination constraints into logical constraints embedded within a mixed-integer optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Method for Collaborative Coverage Monitoring in Urban Scenarios

Xu,

Zhou,

Liu

et al. 2024

Remote Sensing

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In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios.

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“…Additionally, robust planning based on LAC is sensitive to initial conditions since it reshapes around a reference trajectory, often resulting in local optima that prematurely terminate the algorithm [17]. Given the limitations of LAC, scholars studying robust planning problems for Unmanned Aerial Vehicles (UAVs) have adopted methods based on nonlinear models such as Polynomial Chaos Expansion (PCE) [18][19][20][21][22] and Unscented Transform [23][24][25][26][27], enhancing the precision and efficiency of problem-solving.…”

Section: Introductionmentioning

confidence: 99%

Robust Trajectory Planning Of Gliding-Guided Projectiles With Weak Maneuverability

Yin,

Chen,

Wang

et al. 2024

Preprint

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Due to constraints in launch platform and cost, the maneuverability of gliding-guided projectiles is limited, necessitating a rational design of their trajectory schemes. To reduce the sensitivity of trajectory schemes to uncertainties while ensuring compatibility between flight schemes and guidance control systems, and fully exploiting the control capability of the projectile, a closed-loop robust trajectory planning method is proposed. Models of major uncertain factors and states deviation at the control-start point are established. Based on the NIPCE method, the stochastic dynamic model is transformed into a high-dimensional deterministic model with PCE coefficients as state variables, and the uncertainty propagation law is obtained. A PID algorithm is employed to design a tracking guidance law based on position error feedback, and open-loop and closed-loop robust trajectory planning models are established accordingly. The optimal control problem is solved by transforming it into a nonlinear programming problem using the direct shooting method. Simulation results indicate that the NIPCE method can significantly improve the computational efficiency of uncertainty propagation while ensuring accuracy. Open-loop robust planning can effectively mitigate the sensitivity of gliding trajectories to uncertainties but cannot completely eliminate terminal dispersion. Closed-loop robust planning effectively improves control effort consumption on the basis of open-loop planning.

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UAV-based Receding Horizon Control for 3D Inspection Planning

Cited by 13 publications

References 76 publications

Best Proximity Points for p–Cyclic Infimum Summing Contractions

Best Proximity Points for p–Cyclic Infimum Summing Contractions

A Path Planning Method for Collaborative Coverage Monitoring in Urban Scenarios

Robust Trajectory Planning Of Gliding-Guided Projectiles With Weak Maneuverability

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