UAV Path Planning Using Optimization Approaches: A Survey

Saadi, Amylia Ait; Soukane, Assia; Meraihi, Yassine; Gabis, Asma Benmessaoud; Mirjalili, Seyedali; Ramdane-Chérif, Amar

doi:10.1007/s11831-022-09742-7

Cited by 78 publications

(32 citation statements)

References 240 publications

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“…Path planning is the most critical research area focusing on determining an optimal path between UAVs' source and destination. UAVs may come across several obstacles during their operations in a dynamic environment, the path should be determined in such a way that there should be no collisions between the UAV and its surrounding obstacle [65]. The major challenges for an optimal path planning of UAVs are listed in [66].…”

Section: B Path Plannig Optimsationmentioning

confidence: 99%

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

2022

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Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs' trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

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Section: B Path Plannig Optimsationmentioning

confidence: 99%

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

2022

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“…There are a lot of UAV path planning works in these surveys. One of the new surveys on UAV path planning is in [31]. In this survey, Ait et al proposed an overview of UAV path planning approaches.…”

Section: Introductionmentioning

confidence: 99%

RCS: a fast path planning algorithm for Unmanned Aerial Vehicles

Divkoti

Nouri-Baygi

2023

Preprint

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Path planning is a major problem in autonomous vehicles. In recent years, with the increase in applications of Unmanned Aerial Vehicles (UAVs), one of the main challenges is path planning, particularly in adversarial environments. In this paper, we consider the problem of planning a collision-free path for a UAV in a polygonal domain from a source point to a target point. Based on the characteristics of UAVs, we assume two basic limitations on the generated paths: an upper bound on the turning angle at each turning point (maximum turning angle) and a lower bound on the distance between two consecutive turns (minimum route leg length). We describe an algorithm that runs in O(n2 log n) time and finds a feasible path in accordance with the above limitations, where $n$ is the number of obstacle vertices. As shown by experiments, the output of the algorithm is much close to the shortest path with this requirement (always below about 10% of the shortest path), in a much smaller time (up to 10000 quicker, in our experiments). We further demonstrate how to decompose the algorithm into two phases, a preprocessing and a query phase. In this way, given a fixed start point and a set of obstacles, we can preprocess a data structure of size O(n2) in O(n2 log n) time, such that for any query target point we can find a path with the given requirements in O(n log n) time. Finally, we modify the algorithm to find a feasible (almost shortest) path that reaches the target point within a given range of directions.

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“…With the advancement of UAV technology and its increasing application in fields such as intelligent transportation, 1 path planning, 2 engineering geology, 3 collaborative control, 4 communication, 5 and other fields, quadrotor UAV has become a hot field of current research and application and is one of the important directions of future aircraft development. With the continuous expansion of the quadrotor UAV operation field and the continuous improvement of operation requirements, higher performance standards are required.…”

Section: Introductionmentioning

confidence: 99%

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

Li,

Wang,

Luo

et al. 2023

Intl J Robust & Nonlinear

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This paper investigates the robust optimal formation control for the position subsystem of quadrotor unmanned aerial vehicles (UAVs) subject to external disturbances and collision constraints. To prevent collision with both members of the formation and external obstacles, a collision avoidance potential function is constructed using relative position and velocity information. The basic bounded control input can ensure the stable flight and collision avoidance of a quadrotor UAV system. Based on the approximate dynamic programming (ADP) framework and two‐player zero‐sum differential game theory, the optimal controller is designed to further enhance the control performance of the system. The optimal value function is approximated by a single layer neural network, which avoids solving complex nonlinear Hamilton‐JacobiIsaac (HJI) equation. The stability of the closed loop system is proved. The effectiveness of the robust optimal formation controller based on learning is validated through MATLAB simulation in two distinct scenarios.

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UAV Path Planning Using Optimization Approaches: A Survey

Cited by 78 publications

References 240 publications

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

RCS: a fast path planning algorithm for Unmanned Aerial Vehicles

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems

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UAV Path Planning Using Optimization Approaches: A Survey

Cited by 78 publications

References 240 publications

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

RCS: a fast path planning algorithm for Unmanned Aerial Vehicles

Learning‐based collision avoidance and robust H∞ optimal formation control for uncertain quadrotor UAV systems

Contact Info

Product

Resources

About

Learning‐based collision avoidance and robust H_∞ optimal formation control for uncertain quadrotor UAV systems