UAV Trajectory Planning for Complex Open Storage Environments Based on an Improved RRT Algorithm

Zhang, Jingcheng; An, Yuqiang; Cao, Jianing; Shibo, O. Y.; Wang, Lei

doi:10.1109/access.2023.3252018

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…A variety of methods have been applied to assess the flight safety in integrated airspace [9,13]. A category of existing methods involves improving traditional path planning algorithms [14] to research the low-altitude collision avoidance problem, such as the well-known A ⋆ algorithm [15], the rapidly exploring random tree algorithm [16], and the model predictive control algorithm [17]. Another category of existing methods is based on traditional air traffic management collision models, such as the Reich model and Monte Carlo modeling [18].…”

Section: Related Workmentioning

confidence: 99%

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

Wang,

Lv,

Tai

2024

Drones

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This work presents a minimum separation calculation for the integrated operation of manned and unmanned aerial vehicles in an uncertain airspace environment. Different from traditional path-planning-based research, this study investigated the minimum safe separation distance from a novel perspective of reachability analysis. The proposed computational method made use of the Hamilton–Jacobi partial differential equation (HJPDE) to obtain the backward reachable tube. Firstly, this work modeled the integrated operation in the UAS traffic management scenario, particularly focusing on the uncertainties. Then, a probabilistic reachability tube computation method was derived. Next, this work calculated the safe separation distances based on reachability analysis for three scenarios: a deterministic environment, an environment with relative position uncertainty, and an environment with relative heading angle uncertainty. By calculating the reachable tubes for a given response time, the worst-case minimum safe distances from the UAV’s perspective were determined, and the quantitative patterns were summarized. The results in this work indicate that, with an increase in the risk level and under the premise of a 1 second response time, the minimum safe separation increases from 26.7 meters to 30.0 meters. Finally, the paper discusses the results, explaining their rationality from both mathematical and physical perspectives.

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Section: Related Workmentioning

confidence: 99%

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

Wang,

Lv,

Tai

2024

Drones

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“…Those iterative optimization algorithms are used to find a set of UAV missions that suit the defined constraints. In many papers, they are further combined with bioinspired neural network (Liu, Zhang, et al, 2021), gauss pseudo‐spectral method (Shao et al, 2021), or RRT (Liu et al, 2020; Zhang et al, 2023) to improve the trajectory, considering local obstacle avoidance.…”

Section: Related Workmentioning

confidence: 99%

Automated mission planning for aerial large‐scale power plant thermal inspection

Romanov,

Gyrichidi,

Volkova

et al. 2024

Journal of Field Robotics

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Close‐range aerial inspection of large‐scale industrial facilities such as power plants is complex. It is characterized by a few areas suitable for safe landing, many obstacles, complicated radio communication, and so forth. The paper presented a new offline approach for automated mission planning in such conditions. It is based on novel concepts of ‐shaped flight profile and safe altitude map. These concepts reduce the mission planning task to a multiple traveling salesmen problem on a two‐dimensional map, which is solved using rapidly exploring random tree and genetic algorithms. A computational fluid dynamics‐based local turbulence map concept was proposed to avoid turbulence zones during inspection flights. As an advantage, this map can be generated at least 6.8× faster than the known approaches. Our new multicopter flight time prediction model provides an error of less than 10% in most cases and significantly outperforms those implemented in commercial mission planning software. All the proposed solutions were verified during a 2400‐MW power plant inspection. Our algorithms managed to plan to visit all the inspection points in six routes, approximately 15 min long each. This mission set has a comparable overall duration but better safety and even battery usage compared with the routes planned by a qualified operator. Moreover, the designed aerial inspection system provides higher quality images than manually from the ground using a thermal camera with two times higher sensitivity and equipped with more advanced optics. To our knowledge, this research is the first to report the successful implementation of autonomous unmanned aerial vehicle‐based power equipment diagnostics on a large‐scale thermal power plant.

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“…Moreover, it is prone to nonoptimality, due to possible inefficient organization of the cells [201]. Other variants of grid-based algorithms include quadtree [202], Circle Grid Trajectory Cell (CGTC) [203], A* [203,204], Anytime Repairing A* (ARA*) [205,206], and D* [207].…”

Section: Categories Excluded From the Reviewmentioning

confidence: 99%

Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

2023

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Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.

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UAV Trajectory Planning for Complex Open Storage Environments Based on an Improved RRT Algorithm

Cited by 13 publications

References 25 publications

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

Automated mission planning for aerial large‐scale power plant thermal inspection

Conventional, Heuristic and Learning-Based Robot Motion Planning: Reviewing Frameworks of Current Practical Significance

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