Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance

Janson, Lucas; Ichter, Brian; Pavone, Marco

doi:10.1007/978-3-319-60916-4_29

Cited by 41 publications

(75 citation statements)

References 27 publications

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“…This approach usually samples the environment as a set of nodes or other forms and then maps the environment or randomly searches to find a path. Although the search speed is fast, the search path is usually suboptimal, and finding the path in a narrow channel is difficult [111].…”

Section: B Probabilistic Sampling-based Algorithmsmentioning

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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Section: B Probabilistic Sampling-based Algorithmsmentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

131

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“…The use of Sukharev grids in sampling-based algorithms has already been explored experimentally and theoretically in previous studies. [35][36][37] On the context of RRT*-SV, the Sukharev grid sampling process is also useful in situations where it is not possible to connect q near to a convex vertex of Q vertices . This occurs when it is not possible to connect a collision-free segment from q near to any convex vertices available for addition to the tree.…”

Section: Role Of Sukharev Grids In the Rrt*-sv Algorithmmentioning

confidence: 99%

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

Véras

Medeiros²,

Guimarães³

2019

International Journal of Advanced Robotic Systems

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The path planning for an Unmanned Aerial Vehicles ensures that a dynamically feasible and collision-free path is planned between a start and an end point within a navigation environment. One of the most used algorithms for path planning is the Rapidly exploring Random Tree, where each one of its nodes is randomly collected from the navigation environment until the start and end navigation points are connected through them. The Rapidly exploring Random Tree algorithm is probabilistically complete, which ensures that a path, if one exists, will be found if the quantity of sampled nodes increases infinitely. However, there is no guarantee that the shortest path to a navigation environment will be planned by Rapidly exploring Random Tree algorithm. The Rapidly exploring Random Tree* algorithm is a path planning method that guarantees the shorter path length to the UAV but at a high computational cost. Some authors state that by informing sample collection to specific positions on the navigation environment, it would be possible to improve the planning time of this algorithm, as example of the Rapidly exploring Random Tree*-Smart algorithm, that utilize intelligent sampling and path optimization procedures to this purpose. This article introduces a novel Rapidly exploring Random Tree*-based algorithm, where a new sampling process based on Sukharev grids and convex vertices of the security hulls of obstacles is proposed. Computational tests are performed to verify that the new sampling strategy improves the planning time of Rapidly exploring Random Tree*, which can be applied to real-time navigation of Unmanned Aerial Vehicles. The results presented indicate that the use of convex vertices and grid of Sukharev accelerate the planning time of the Rapidly exploring Random Tree* and show better performance than the Rapidly exploring Random Tree*-Smart algorithm in several navigation environments with different quantities and spatial distributions of polygonal obstacles.

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“…According to the different stages of the path planning algorithm development, the algorithms can be divided into two categories: fast-exploring random tree method [ 8 ], artificial potential field method [ 9 ], the visible method [ 10 ], A* algorithm [ 11 ] as representative traditional algorithms. Intelligent algorithms represented by genetic algorithm [ 12 ], ant colony algorithm [ 13 ], particle swarm algorithm [ 14 ], immune cloning algorithm [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Intelligent algorithms represented by genetic algorithm [ 12 ], ant colony algorithm [ 13 ], particle swarm algorithm [ 14 ], immune cloning algorithm [ 15 ]. In [ 8 ], Janson theoretically proved that the use of deterministic low-dispersion sampling plan usually makes the RRT algorithm display superior performance. In [ 9 ], Yu proposed an improved artificial potential field method.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

Hao

Zhao

et al. 2020

Sensors

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In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.

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Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance

Cited by 41 publications

References 27 publications

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Rapidly exploring Random Tree* with a sampling method based on Sukharev grids and convex vertices of safety hulls of obstacles

Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm

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