Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

Li, Yang; Zhang, Fubin; Xu, Dehong; Dai, Jiguo

doi:10.1155/2017/7816263

Cited by 11 publications

(8 citation statements)

References 32 publications

(52 reference statements)

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“…EAs such as particle swarm optimization (PSO) [59], ant colony optimization (ACO) [60], genetic algorithm (GA) [61], wolf colony algorithm (WCA) [62], bio-inspired neural networks, and other algorithms have all been used and implemented for solving the path planning problem of ASVs. -Sampling-based algorithms have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness [63]. The probabilistic roadmap (PRM) and rapidly exploring random tree (RRT) [64] algorithms and their variations are some of the most often used algorithms.…”

Section: Path Planning Algorithmsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles I: a review

et al. 2021

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Autonomous surface vehicles are gaining increasing attention worldwide due to the potential benefits of improving safety and efficiency. This has raised the interest in developing methods for path planning that can reduce the risk of collisions, groundings, and stranding accidents at sea, as well as costs and time expenditure. In this paper, we review guidance, and more specifically, path planning algorithms of autonomous surface vehicles and their classification. In particular, we highlight vessel autonomy, regulatory framework, guidance, navigation and control components, advances in the industry, and previous reviews in the field. In addition, we analyse the terminology used in the literature and attempt to clarify ambiguities in commonly used terms related to path planning. Finally, we summarise and discuss our findings and highlight the potential need for new regulations for autonomous surface vehicles.

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Section: Path Planning Algorithmsmentioning

confidence: 99%

Path planning and collision avoidance for autonomous surface vehicles I: a review

et al. 2021

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“…However, both Bi-RRT and RRT encounter the problem of being easily trapped in local minimums. Li et al [125] proposed an improved algorithm for an AUV path search-Li-RRT. In contrast to the typical RRT, it utilizes the liveness of each node to guide the expanding process of the random search tree, and more efficient or useful nodes will pop out to enhance the property of exploration.…”

Section: ) Rapidly Exploring Random Tree (Rrt)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

131

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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“…Details of basic RRT algorithm are given in [1,7,13,24,25,34,37,39]. This paper modifies and uses goal-biased RRT to generate the roadmap ensuring that the tree is rapidly generated towards the goal.…”

Section: Step 2: Computing the Rrt Roadmapmentioning

confidence: 99%

“…Roadmap path planning methods are gaining popular-Information Technology and Control 2019/2/48 180 ity in addressing mobile robot path planning problems [20]. Notable among these methods include probabilistic roadmap (PRM) [33], voronoi diagram (VD) [4,5] and rapidly exploring random tree (RRT) path planning methods [1,7,9,13,14,24,25,34,37,39]. Consideration is given to RRT path planning in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…The main purpose of the RRT-Edge method was to reduce memory requirement. To enhance the expanding speed of the tree generation, Li et al [25] proposed Liveness-based RRT method for autonomous underwater vehicles (AUVs) motion planning. Although splined-based RRT* method proposed in [37] could find a path according to the authors, it requires the generation of the tree to cover the entire configuration space.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

Ayawli

Mei

Shen

et al. 2019

ITC

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This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments

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Liveness-Based RRT Algorithm for Autonomous Underwater Vehicles Motion Planning

Cited by 11 publications

References 32 publications

Path planning and collision avoidance for autonomous surface vehicles I: a review

Path planning and collision avoidance for autonomous surface vehicles I: a review

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

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