Hybrid rule-based motion planner for mobile robot in cluttered workspace

Abbadi, Ahmad; Matoušek, Radomil

doi:10.1007/s00500-016-2103-4

Cited by 6 publications

(6 citation statements)

References 71 publications

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“…The use of sampling probabilities and fuzzy logic to obtain collision information and guide the expansion of RRT is considered in [112]. In the method, the search space is cluttered by cell decomposition and each cell are associated with probabilities and, according to their classification, some regions of the search space becomes more important than others.…”

Section: ) Region-biased Samplingmentioning

confidence: 99%

“…In the method, the search space is cluttered by cell decomposition and each cell are associated with probabilities and, according to their classification, some regions of the search space becomes more important than others. Based on this, two planners, where probabilities information are used as RRT advisors on the expansion process, are proposed by [112]: a boundary bias planner; and a fuzzy bias planner. In the boundary bias, regions classes are defined given the neighborhood relation of the regions/cells of the search space with the nodes of the RRT.…”

Section: ) Region-biased Samplingmentioning

confidence: 99%

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Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees

Veras

Medeiros²,

Guimarães³

2019

IEEE Access

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Path planning is one of the most important process on applications such as navigating autonomous vehicles, computer graphics, game development, robotics, and protein folding. It ensures that a path is planned between an initial and final position on the collision-free region of a search space if one exists. One of the most wide algorithms used for this purpose is the rapidly-exploring random tree (RRT), in which each node of a tree data structure is generated from a search space by a random sampling process, which originally follows a uniform spatial distribution. However, some authors claim that the addition of a non-uniform/informed approach into the sampling process of the RRT could accelerate the planning time of the algorithm. Actually, many works on literature propose different strategies to include non-uniform/informed behavior on RRT-based algorithms. However, the large number of studies on path planning subject impose difficulties on the identification of new solutions on a review process. The aim of this paper is to structure a review process to deal with the massive volume of works on this subject, by presenting the planning, development, and results of a systematic literature review (SLR), to investigate non-uniform/informed sampling solutions applied to RRT-based algorithms on path planning literature. A review protocol with two scientific questions was developed to guide the investigation. As a result, 1136 studies were selected in the path planning literature, of which 53 were identified as claiming to contain a solution with non-uniform/informed sampling on RRT-based algorithms. As a specific work is considered a scientific contribution only when it has not yet been explored in scientific circles, the results of the SLR can be used as a tool to search for what has not yet been proposed, helping to identify opportunities to contribute with new sampling processes of RRT-based algorithms. To the best knowledge of the authors, this paper presents the first development of an SLR of a topic related to the RRT algorithm. INDEX TERMS Non-uniform sampling, informed sampling, path planning, RRT, systematic literature review.

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Section: ) Region-biased Samplingmentioning

confidence: 99%

Section: ) Region-biased Samplingmentioning

confidence: 99%

Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees

Veras

Medeiros²,

Guimarães³

2019

IEEE Access

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“…Parhi and Singh [10][11][12] have presented mobile robot navigational path analysis using adaptive neurofuzzy and neural networks in static and dynamic environments. Abbadi and Matousek 13 and Muni et al 14 have explained the rule-based system with the hybridization of cell decomposition method and rapidly exploring random tree algorithm (RRT). Alitappeh et al 15 have given Varonoi portioning technique to deploy multi robot for multi-objective problems.…”

Section: Introductionmentioning

confidence: 99%

Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

Kumar

Parhi

Muni

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Mobile robots have wide applications in challenging real-world scenarios. Therefore, it is necessary to have an advanced controller to control the robotic systems smoothly. An artificial bee colony optimization algorithm and recurrent neural network are combined to develop a hybrid controller and implemented for multi-robotic navigational problems in unknown static and dynamic environments. The designed controller is validated through MATLAB simulations coupled with real-time experiments. Results obtained via both the testing platforms are analysed, and found a good agreement between them as the deviation is less than 5.5%. Further, the developed controller is compared with existing controllers, and improvements of 20%, 10.19%, 13.53% is noted in terms of path length.

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“…In these works, heuristics/ strategies are used to bias the sampling process of RRT and RRT*, prioritizing sampling of new nodes in certain regions of the navigation environment. Among these works, we can mention Multi-Sample (MS-RRT), 14 RTT Voronoi Diagram based, 15 Goal-oriented test generation RRT based, 16 RRT*-Smart, 17 Selective Retraction-based RRT, 18 Poisson RRT, 19 Potential Guided Directional-RRT* (P-RRT*), 20 Sampling probabilities and fuzzy logic RRT based, 21 Informed-RRT*. 22 The contribution of this work is the study of the use of two different sampling strategies in the RRT*: the spatial distributions of samples based on Sukharev grids 9 ; and the application of samples defined by the convex vertices of the safety hulls of navigation environment obstacles.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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Hybrid rule-based motion planner for mobile robot in cluttered workspace

Cited by 6 publications

References 71 publications

Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees

Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees

Path planning and obstacle avoidance of multi-robotic system in static and dynamic environments

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

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