Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Jones, Michael; Djahel, Soufiene; Welsh, Kristopher

doi:10.1145/3570723

Cited by 56 publications

(19 citation statements)

References 120 publications

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“…As such, each autonomous flight is characterized by an objective, either being the arrival to a certain target point, or the aim of maximizing the information coverage [32]. With regards to UAS navigation, different path-planning alternatives can be used depending on the specific requirements of the application [33]. These methods can be classified into two categories: those that are based on a sampling of the search space, and those that employ artificial intelligence (AI) to find a solution with respect to the representation of the environment [34].…”

Section: Autonomous Navigationmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed.

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Section: Autonomous Navigationmentioning

confidence: 99%

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Politi,

Purucker,

Larsen

et al. 2024

Electronics

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“…The path planning technology for mobile robots has emerged as a focal point in both industrial and academic research in recent years [1] . The objective of path planning is to enable robots to autonomously devise the optimal trajectory from a starting point to a destination in an unknown environment, involving collision detection and corresponding motion planning during movement.…”

Section: Introductionmentioning

confidence: 99%

CFEAC:A Contrastive Learning Approach for Feature- Enhanced Actor-Critic in Robot Path Planning

Gu,

Chu,

Meng

et al. 2024

Preprint

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In the field of robot path planning, Deep Reinforcement Learning (DRL) has demonstrated considerable potential as a cutting-edge artificial intelligence technology. However, the effective utilization of representation learning in path planning tasks, which is pivotal for successful DRL performance, has remained elusive. This challenge arises from the predominant use of compact vectors derived directly from low-level sensors as the state representation in the task. Learning meaningful representations on such low-level states often proves to be challenging. To address this issue, a novel approach named Contrastive Learning Regularized Feature-Enhanced Actor-Critic (CFEAC) is proposed in this paper. This method adopts a contrastive learning perspective to handle features in neural networks and incorporates cross-layer connections and deep networks to achieve feature enhancement. In a constructed 3D point cloud simulation environment, the CFEAC algorithm outperforms DDPG, TD3, SAC, and SALE algorithms in terms of higher cumulative reward and lower collision rates. Experimental results validate that this approach exhibits superior path planning performance in complex static and dynamic scenarios.

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“…Generally, path planning can be divided into three categories based on the mission of UAV: global path planning, local path planning, and distributed path planning. Global path planning divides the entire flight area into grids or graphs and then finds the best path based on the UAV's start and end points, Local path planning focuses on the obstacles around the UAV to avoid collisions and danger, Distributed path planning, in order to achieve collaborative missions, requires simultaneous consideration of the positions, speeds, and mission requirements of multiple UAVs, divides the mission area into several sub-mission areas, and performs separate path planning for each sub-mission area [3].…”

Section: Introductionmentioning

confidence: 99%

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

Zheng,

He,

2024

Preprint

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Unmanned Aerial Vehicle (UAV) route planning is an intricate issue that requires the comprehensive consideration of multiple factors and the combination of suitable strategies to achieve efficient and safe flight paths. Its purpose is to plan a suitable navigational path for the drone to achieve a specific task or avoid obstacles. In practical applications, UAVs are usually required to accomplish tasks in a variety of complex environments, resulting in the feasible flight paths will be reduced and path planning for UAVs becomes difficult. Accordingly, we design a simple yet useful planner, called Self-adaptive Golden Equilibrium Optimization algorithm (SGEO). The proposed algorithm combines the new self-adaptive approach, the acceleration control factor and the Golden-SA strategy in order to balance the exploitation and exploration capabilities. The novel self-adaptive strategy is used to expand the global search range of the algorithm and enhance its ability for global exploration; the acceleration control factor is introduced to control the parameters a1 and a2 and improve the convergence ability of the algorithm; the Golden-SA strategy helps the candidate particles achieve better balance between different dimensions based on the golden ratio and sine function, this enables the particles to explore the search space more comprehensively. The limitations in UAV route planning are translated into the objective function, and four more algorithms are introduced to compare with SGEO in two different circumstances. The simulation findings reveal that SGEO excels at three-dimensional path planning for UAV in complicated environments.

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Path-Planning for Unmanned Aerial Vehicles with Environment Complexity Considerations: A Survey

Cited by 56 publications

References 120 publications

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

Enabling Technologies for the Navigation and Communication of UAS Operating in the Context of BVLOS

CFEAC:A Contrastive Learning Approach for Feature- Enhanced Actor-Critic in Robot Path Planning

SGEO: Equalization Optimizer with Hybrid Learning Strategies for UAV Path Planning in Complex 3D Environments

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