An Efficient Hardware Architecture of the A-star Algorithm for the Shortest Path Search Engine

Seo, Woo-Jin; Ok, Seung-Ho; Ahn, Jin-Ho; Kang, Sungho; Moon, Byungin

doi:10.1109/ncm.2009.371

Cited by 21 publications

(4 citation statements)

References 2 publications

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“…For the testing location a starting point and an endpoint can be defined. We integrated a path planning algorithm [ 26 , 27 ], which calculates a path between these two points. For the AR navigation scenario, arrows were placed along the path.…”

Section: Methodsmentioning

confidence: 99%

HyMoTrack: A Mobile AR Navigation System for Complex Indoor Environments

Gerstweiler

Vonach

Kaufmann

2015

Sensors

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Navigating in unknown big indoor environments with static 2D maps is a challenge, especially when time is a critical factor. In order to provide a mobile assistant, capable of supporting people while navigating in indoor locations, an accurate and reliable localization system is required in almost every corner of the building. We present a solution to this problem through a hybrid tracking system specifically designed for complex indoor spaces, which runs on mobile devices like smartphones or tablets. The developed algorithm only uses the available sensors built into standard mobile devices, especially the inertial sensors and the RGB camera. The combination of multiple optical tracking technologies, such as 2D natural features and features of more complex three-dimensional structures guarantees the robustness of the system. All processing is done locally and no network connection is needed. State-of-the-art indoor tracking approaches use mainly radio-frequency signals like Wi-Fi or Bluetooth for localizing a user. In contrast to these approaches, the main advantage of the developed system is the capability of delivering a continuous 3D position and orientation of the mobile device with centimeter accuracy. This makes it usable for localization and 3D augmentation purposes, e.g. navigation tasks or location-based information visualization.

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Section: Methodsmentioning

confidence: 99%

HyMoTrack: A Mobile AR Navigation System for Complex Indoor Environments

Gerstweiler

Vonach

Kaufmann

2015

Sensors

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“…To summarize, see Table 3 for a list of A* and its variants used in path planning within static and dynamic contexts. Importantly, the A* algorithm is computationally efficient [43,44]. This makes it suitable for applications deployed in static environments.…”

Section: Function Equationmentioning

confidence: 99%

A Survey of Path Planning Algorithms for Mobile Robots

et al. 2021

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Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

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“…[8], a time window method based task assignment and the A‐star algorithm Ref. [9] based path planning are jointly proposed, the conflict problem of AGVs has been solved, but, the path environment is just formulated through using a very simply way, which is unrealistic and reduces the capability of the work; in Ref. [10], an algebraic algorithm is used to plan the path for the AGVs, however, in this paper, task assignment has not been considered and the path environment is just presented by using an acyclic graph; in Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

Wen

Daichuan

Liu

et al. 2021

Chinese J of Electronics

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We propose an integrated path planning method for multiple automated guided vehicles performing logistics delivery within a real‐world warehouse environment considering obstacles. By applying it on each vehicle, this proposed method enables the vehicle the vehicles have the capabilities for autonomous path planning. The path planning consists of three parts, K‐means algorithm based task points clustering, genetic algorithm based task points ordering, and the probabilistic road map based best path search. Vehicle conflict resolution is depending on implementing the probabilistic road map construction considering the realistic map with obstacles. The simulations result validate that the clustering and ordering are necessary for the path planning, both the path planning time and the Automated guided vehicles (AGVs) running time can be dramatically reduced.

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An Efficient Hardware Architecture of the A-star Algorithm for the Shortest Path Search Engine

Cited by 21 publications

References 2 publications

HyMoTrack: A Mobile AR Navigation System for Complex Indoor Environments

HyMoTrack: A Mobile AR Navigation System for Complex Indoor Environments

A Survey of Path Planning Algorithms for Mobile Robots

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

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