Path planning and Obstacle avoidance approaches for Mobile robot

Nguyen, Hoc Thai; Le, Hai Xuan

doi:10.48550/arxiv.1609.01935

Cited by 4 publications

(4 citation statements)

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“…The plethora of existing works in the literature on path planning and obstacle avoidance [ 1 , 2 , 3 , 4 , 5 ] indicates that the issues surrounding autonomous navigation of mobile robots are far from being resolved and highlights the growing interest in addressing these problems. Most of the proposed approaches suffer from either realism (real-time execution is not feasible due to high computational times) or exhaustiveness (scenario-dependent applications that do not consider certain aspects), only partially addressing the problem.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Optimal Navigation in Situations of Localization Uncertainty

Orou Mousse,

Benrabah,

Marmoiton

et al. 2023

Sensors

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The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties in its localization? All its capabilities, whether path following or obstacle avoidance, are affected by this uncertainty, and stopping the vehicle becomes the safest solution. In this work, we propose a framework that optimally combines path following and obstacle avoidance while keeping these two objectives independent, ensuring that the limitations of one do not affect the other. Absolute localization uncertainty only has an impact on path following, and in no way affects obstacle avoidance, which is performed in the robot’s local reference frame. Therefore, it is possible to navigate with or without prior information, without being affected by position uncertainty during obstacle avoidance maneuvers. We conducted tests on an EZ10 shuttle in the PAVIN experimental platform to validate our approach. These experimental results show that our approach achieves satisfactory performance, making it a promising solution for collision-free navigation applications for mobile robots even when localization is not accurate.

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Section: Introductionmentioning

confidence: 99%

A Framework for Optimal Navigation in Situations of Localization Uncertainty

Orou Mousse,

Benrabah,

Marmoiton

et al. 2023

Sensors

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“…In this work, we address the problem of clustering in the presence of obstacles by integrating k-means with the A* algorithm, which is used to find routes and distances between two positions in a scenario considering obstacles. The A* algorithm was chosen as the metric because it does not result in deadlocks, such as bug algorithms [15], and it is more efficient than Dijkstra [16]. As a proof of concept, we applied the proposed approach to a simple scenario formed by a grid of 20×20 grid where 25 users have been arbitrarily scattered.…”

Section: Introductionmentioning

confidence: 99%

Integration of A* and k-means clustering for star network design in environments considering obstacles

Aldaya¹,

Cardinal²,

Menzinger³

et al. 2022

Anais Do XL Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Multi-level star is a widely adopted topology in many telecommunication systems, including wireless and wired networks. The optimization of this network topology, however, is not trivial, especially in the presence of obstacles. In this paper, we employ the well-known k-means clustering method but, instead of using a distance metric that neglects obstacles, we use the A* algorithm to find trajectories that allow circumventing obstacles. To assess the feasibility of the proposed approach, we apply it to 25 users arbitrarily located in a 20×20 uniform grid. This example indicates that by adopting A* to find obstacleaware trajectories, the mean distance between the users and their associated centroids is sensibly reduced.

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“…It is necessary to use the sensor to establish an environmental map in real time, avoid obstacles, and find a suitable path. This kind of path planning is called local path planning [6]. The global path planning method is applied to a static environment in the paper.…”

Section: Introductionmentioning

confidence: 99%

A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

Tao

Ren

et al. 2021

Applied Sciences

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A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

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Path planning and Obstacle avoidance approaches for Mobile robot

Cited by 4 publications

References 0 publications

A Framework for Optimal Navigation in Situations of Localization Uncertainty

A Framework for Optimal Navigation in Situations of Localization Uncertainty

Integration of A* and k-means clustering for star network design in environments considering obstacles

A Mobile Service Robot Global Path Planning Method Based on Ant Colony Optimization and Fuzzy Control

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