Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Vo, Anh Hoa; Yoon, Hyeon Kyu; Ryu, Jae-Kwan; Jin, Taekseong

doi:10.26748/ksoe.2019.067

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Hybrid representation combines the two (metric and topological) representations to perform a simultaneous location and mapping of the environment [9]. Other types of environment processing techniques such as the Otsu algorithm are used when an image of the environment is used as an input to an obstacle avoidance algorithm [19]. The Otsu algorithm for instance converts the input image to a binary representation of the environment [12].…”

Section: Related Work On Obstacle Avoidancementioning

confidence: 99%

“…Since 2D obstacle avoidance algorithms are widely used in both the military and civilian applications, it is crucial to understand them through numerical simulations. The capacity to avoid obstacles is a key component for ground mobile robots, allowing them to effectively and safely undertake a wide range of duties and tasks such as exploration and transportation [19].…”

Section: Introductionmentioning

confidence: 99%

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Mobile Robots with Dynamic Obstacle Avoidance

Ali,

Das

2023

2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET)

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Robots with autonomous navigation capabilities have become increasingly popular after the advances in robotic automation, particularly in the area of pathfinding algorithms. These algorithms enable robots to safely traverse through complex environments with both stationary and moving obstacles. Applications of this field range from data acquisition to surveys of hazardous situations and transportation by industrial robots. The most commonly utilized approach for two-dimensional obstacle avoidance is grid-based pathfinding algorithms. These methods function by initially generating a grid consisting of nodes and edges based on the environment. In this paper, we explore an implementation of a variation of the A* pathfinding algorithm on a 15x15 grid. The A* algorithm was chosen because it guarantees finding the optimal route between starting and ending points. A* is a grid-based algorithm that falls under the category of search-based algorithms. The Maximum Velocity Obstacle (MVO) algorithm undergoes rigorous testing to evaluate its performance, and we examine how the simulation input parameters influence the algorithm's effectiveness. The experimental results indicate that the MVO algorithm is an efficient and reliable solution for dynamic obstacle avoidance in a grid-based setting. Moreover, this study demonstrates that the algorithm can be further optimized by using more advanced techniques such as combining it with existing pathfinding algorithms, like artificial neural networks. This would enable the robot to adapt to unpredictable environments in future research.

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Section: Related Work On Obstacle Avoidancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%