Indoor Navigation with Human Assistance for Service Robots Using D*Lite

Alves, Raulcezar M. F.; Morais, Josué Silva de

doi:10.1109/smc.2018.00696

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…D * Lite algorithm is an improved version of the D * algorithm, which combines the advantages of the D * algorithm and the LPA * algorithm. Unlike the forward search used by LPA * , D * Lite uses the reverse search (the target point is fixed and the starting point changes with time) [33], which makes the path planning easier and faster. Due to the dynamic characteristics of the underwater environment and the uncertainty and incompleteness of environmental information, it is necessary to replan the path in time when the AUV encounters unknown static or dynamic obstacles.…”

Section: 22mentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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Path planning is a key technology for autonomous underwater vehicle (AUV) navigation. With the emphasis and research on AUV, AUV path planning technology is continuously developing. Path planning techniques generally include environment modelling methods and path planning algorithms. Based on a brief description of the environment modelling methods, this paper focuses on the path planning algorithms commonly used by AUV. According to the basic principles of the algorithm, the AUV path planning algorithms are divided into four categories: artificial potential field methods, geometric model search methods, random sampling methods, and intelligent bionic methods. In this review, we summarize in detail the development and application of various path planning algorithms in recent years. Meanwhile, we analyse the advantages and disadvantages of various algorithms and their improvement methods. Obstacles, ocean currents, and undersea terrain have an impact on AUV path planning. Therefore, how to deal with the complex underwater environment adds some limits to AUV path planning algorithms. In addition to the external environment, path planning algorithms also need to consider AUV’s physical constraints, such as energy constraints and motion constraints. Then, we analyse the motion constraints in AUV path planning. Finally, we discuss the development direction of AUV path planning algorithm. Time-varying ocean currents, special obstacles, multiobjective constraints, and practicability will be the problems that AUV path planning algorithms need to solve.

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Section: 22mentioning

confidence: 99%

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Guo

Liu

Fan

et al. 2021

Mathematical Problems in Engineering

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“…The presence or absence of the ability to walk along the edges in both directions distinguishes between a directed graph and an undirected graph. There are several different algorithms designed to solve the issue of the shortest path, including but not limited to Dijkstra's algorithm (P. , the Bellman-Ford algorithm (Parimala et al, 2021), the A* algorithm (Rachmawati & Gustin, 2020), the D* algorithm (Alves et al, 2019), and the Floyd-Warshall algorithm (Ramadiani et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

Sariman,

Othman,

Mat Akir

et al. 2024

JST

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The term “indoor navigation system” pertains to a technological or practical approach that facilitates the navigation and orientation of individuals within indoor settings, such as museums, airports, shopping malls, or buildings. Over several years, significant advancements have been made in indoor navigation. Numerous studies have been conducted on the issue. However, a fair evaluation and comparison of indoor navigation algorithms have not been discussed further. This paper presents a comprehensive review of collective algorithms developed for indoor navigation. The in-depth analysis of these articles concentrates on both advantages and disadvantages, as well as the different types of algorithms used in each article. A systematic literature review (SLR) methodology guided our article-finding, vetting, and grading processes. Finally, we narrowed the pool down to 75 articles using SLR. We organized them into several groups according to their topics. In these quick analyses, we pull out the most important concepts, article types, rating criteria, and the positives and negatives of each piece. Based on the findings of this review, we can conclude that an efficient solution for indoor navigation that uses the capabilities of embedded data and technological advances in immersive technologies can be achieved by training the shortest path algorithm with a deep learning algorithm to enhance the indoor navigation system.

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A Procedure for Taking a Remotely Controlled Elevator with an Autonomous Mobile Robot Based on 2D LIDAR

Palacín

Bitriá

Rubies

et al. 2023

Sensors

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Navigating between the different floors of a multistory building is a task that requires walking up or down stairs or taking an elevator or lift. This work proposes a procedure to take a remotely controlled elevator with an autonomous mobile robot based on 2D LIDAR. The application of the procedure requires ICP matching for mobile robot self-localization, a building with remotely controlled elevators, and a 2D map of the floors of the building detailing the position of the elevators. The results show that the application of the procedure enables an autonomous mobile robot to take a remotely controlled elevator and to navigate between floors based on 2D LIDAR information.

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Indoor Navigation with Human Assistance for Service Robots Using D*Lite

Cited by 3 publications

References 9 publications

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

Research Progress of Path Planning Methods for Autonomous Underwater Vehicle

A Review: Current Trend of Immersive Technologies for Indoor Navigation and the Algorithms

A Procedure for Taking a Remotely Controlled Elevator with an Autonomous Mobile Robot Based on 2D LIDAR

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