A 3D Benchmark for UAV Path Planning Algorithms: Missions Complexity, Evaluation and Performance

Rocha, Lídia; Vivaldini, Kelen Cristiane Teixeira

doi:10.1109/icuas54217.2022.9836190

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…Related research in the field of drone operations and data acquisition for photogrammetric processes includes benchmark data that are openly accessible, which allows easy comparisons of results (Rocha and Vivaldini, 2022). This could be another worthwhile addition to the literature on the topic of scan planning and the VPP for static laser scanning.…”

Section: Discussionmentioning

confidence: 99%

Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods

Noichl,

Stuecke,

Thielen

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The preparation of laser scanning missions is important for efficiency and data quality. Furthermore, it is a prerequisite for automated data acquisition, which has numerous applications in the built environment, including autonomous inspections and monitoring of construction progress and quality criteria. The scene and potential scanning locations can be discretized to facilitate the analysis of visibility and quality aspects. The remaining mathematical problem to generate an economic scan strategy is the Viewpoint Planning Problem (VPP), which asks for a minimum number of scanning locations within the given scene to cover the scene under pre-defined requirements. Solutions for this problem are most commonly found using heuristics. While these efficient methods scale well, they cannot generally return globally optimal solutions. This paper investigates the VPP based on a problem description that considers quality-constrained visibility in 3D scenes and suitable overlaps between individual viewpoints for targetless registration of acquired point clouds. The methodology includes the introduction of a preprocessing method designed to simplify the input data without losing information about the problem. The paper details various solution methods for the VPP, encompassing conventional heuristics and a mixed-integer linear programming formulation, which is solved using Benders decomposition. Experiments are carried out on two case study datasets, varying in specifications and sizes, to evaluate these methods. The results show the actual quality of the obtained solutions and their deviation from optimality (in terms of the estimated optimality gap) for instances where exact solutions can not be achieved.

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Section: Discussionmentioning

confidence: 99%

Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods

Noichl,

Stuecke,

Thielen

et al. 2024

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Thus, a comprehensive complexity formula has been developed, incorporating several factors, including the size of the environment, the number of obstacles that the drone identiőed until the moment, the distance to the goal node, and the spatial distribution factor (SDF), as shown in Equation 8. This equation is based in the results of this study [55], which describes the complexity of several path planning algorithms. complexity = ( num_obstacles grid_size 2 )×(…”

Section: Dynamic Iterationsmentioning

confidence: 99%

Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments

Rocha,

Caldas,

Terra

et al. 2024

Preprint

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Unmanned Aerial Vehicles need an online path planning capability to move in high-risk missions in unknown and complex environments to complete them safely. However, many algorithms reported in the literature may not return reliable trajectories to solve online problems in these scenarios. The Q-Learning algorithm, a Reinforcement Learning Technique, can generate trajectories in real-time and has demonstrated fast and reliable results. This technique, however, has the disadvantage of defining the iteration number. If this value is not well defined, it will take a long time or not return an optimal trajectory. Therefore, we propose a method to dynamically choose the number of iterations to obtain the best performance of Q-Learning. The proposed method is compared to the Q-Learning algorithm with a fixed number of iterations, A*, Rapid-Exploring Random Tree, and Particle Swarm Optimization. As a result, the proposed Q-learning algorithm demonstrates the efficacy and reliability of online path planning with a dynamic number of iterations to carry out online missions in unknown and complex environments.

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“…All used algorithms were optimized by adjusting the hyperparameters as shown in a previous work [Rocha and Vivaldini 2022] and using them correctly with the other UAV modules, except for APF, which was implemented to improve the algorithm that differs from the conceptual model. Three improvements were implemented in APF.…”

Section: Path Planning Improvementsmentioning

confidence: 99%

“…There are several path planning techniques and each of them has different results in different environments [Rocha and Vivaldini 2022]. The path planning techniques to be evaluated fall into three main categories: classical techniques (exact and approximate), metaheuristic, and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Algorithms in Unknown and Unstructured Environments for UAVs

Rocha

Vivaldini

2022

Anais Estendidos Do XIV Simpósio Brasileiro De Robótica E XIX Simpósio Latino-Americano De Robótica (SBR/LARS Estendido 2022)

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For an Unmanned Aerial Vehicle to become autonomous, it must perform actions without human interference. Regardless of its application area, path planning is required to carry out a mission. Nowadays, several applications require the UAV to operate in an unknown, 3D, and unstructured environment. Another essential point is considering the movement restrictions in the execution of the movements, where achieving smooth curves reduces the number of stops on 90 degrees curves. One observable aspect among the existing and most used techniques is ”which would be the best technique to work in each of these environments”. This work aims to answer this question with a deeper analysis of all path planning categories: classic, metaheuristic, and machine learning. We develop our planner to analyze these techniques considering completeness, distance, time, CPU usage, memory usage, collision prevention, and robustness. This planner is modular, so it is possible to add new techniques and scenarios to be studied. We also performed tests in simulated and real environments.

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A 3D Benchmark for UAV Path Planning Algorithms: Missions Complexity, Evaluation and Performance

Cited by 4 publications

References 35 publications

Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods

Assessing and Improving Automated Viewpoint Planning for Static Laser Scanning Using Optimization Methods

Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments

Path Planning Algorithms in Unknown and Unstructured Environments for UAVs

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