Self-learning RRT* Algorithm for Mobile Robot Motion Planning in Complex Environments

Zhang, Xu; Lütteke, Felix; Ziegler, Christian; Franke, Jörg

doi:10.1007/978-3-319-08338-4_5

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…During the data extraction of the selected primary studies, was possible to identify common characteristics between various proposed biased sampling strategies. On [85], a classification of the kinds of non-uniform/informed sampling is given. Some examples of classes of sampling bias are given on [33], [84], and [86] too.…”

Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

“…The algorithm presented on [85] introduces a self-learning of the search space to adapt the sampling depending on if the region being explored it is a difficult one or an open one where goal bias is more promising. The collision check process give information about the distribution of the obstacles regions into the search space.…”

Section: B Adaptative Sampling 1) Sampling By Reduction Of the Searcmentioning

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: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Section: B Adaptative Sampling 1) Sampling By Reduction Of the Searcmentioning

confidence: 99%

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

Veras

Medeiros²,

Guimarães³

2019

IEEE Access

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“…AI can realize a reliable solution for devices to perform complex tasks effectively and efficiently, such as Artificial Neural Network (ANN). Recently, Machine Learning (ML) has been shown to provide self-learning [ 14 , 15 ], self-organizing [ 16 ], self-optimization, self-reproducing [ 17 , 18 ], and self-healing solutions for a broad range of IoT challenges. Moreover, ML and IoT are two cornerstone technologies enabling green public services at a lower cost and interacting with each other into an essential ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

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The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

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“…Robotic and AI aim is to create and understand machines capable of thinking and acting like humans. In view of this, robotics has the capability for self-learning [3,4], self-organizing [5], self-reproduce [6,7]. Nowadays, robots are becoming intelligent machines which use their artificial intelligence, abilities, and cleverness to perform tasks quickly and smartly.…”

Section: Introductionmentioning

confidence: 99%

Survey on artificial intelligence based techniques for emerging robotic communication

Alsamhi

Ansari

2019

Telecommun Syst

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This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The study of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently.

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Self-learning RRT* Algorithm for Mobile Robot Motion Planning in Complex Environments

Cited by 8 publications

References 10 publications

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

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

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Survey on artificial intelligence based techniques for emerging robotic communication

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