A Fine Tuned Hybrid Implementation for Solving Shortest Path Problems using Bellman Ford

Hajela, Gaurav; Pandey, Manish

doi:10.5120/17347-7360

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Hpc For Path Planning1

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Cited by 4 publications

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“…Prior efforts by Surve et al [114] suggested that other path planning techniques, specifically Bellman-Ford, present more opportunities to accelerate the execution on a GPU. Hajela et al [115] improved the performance of Bellman-Ford by using hybrid execution (CPU and GPU cooperatively).…”

Section: Hpc For Path Planningmentioning

confidence: 99%

Towards real-time safe flight paths for urban air mobility

Gutierrez

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Section: Hpc For Path Planningmentioning

confidence: 99%

Towards real-time safe flight paths for urban air mobility

Gutierrez

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Analysis and implementation of the minimum route issues between some governorates of Iraq using Bellman-Ford algorithm

Hassan

2017

2017 Annual Conference on New Trends in Information &Amp; Communications Technology Applications (NTICT)

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Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

et al. 2018

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Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

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A Fine Tuned Hybrid Implementation for Solving Shortest Path Problems using Bellman Ford

Cited by 4 publications

References 9 publications

Towards real-time safe flight paths for urban air mobility

Towards real-time safe flight paths for urban air mobility

Analysis and implementation of the minimum route issues between some governorates of Iraq using Bellman-Ford algorithm

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

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