A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

Pérez–Hurtado, Ignacio; Amor, Miguel Ángel Martínez del; Zhang, Gexiang; Neri, Ferrante; Pérez–Jiménez, Mario J.

doi:10.3233/ica-190616

Cited by 51 publications

(22 citation statements)

References 62 publications

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“…Modern autonomous indoor robots are developed to perform some more tasks related to interact with the external environment. This robot and environmental interaction have various styles and types such as the systems where reconstruction of shapes of any three-dimensional object [212] and also a navigation system based on vision, which is mostly used in drones [213].…”

Section: E: Rapidly-explained Random Tree Algorithmsmentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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Robots that can move autonomously and can make intelligent decisions by perceiving their environments and surrounding objects are known as autonomous mobile robots. Such robots have rapidly moved from laboratories to automated industries to fill a variety of roles in our lives, homes, offices, hospitals, industries, and even on the streets. The interest in mobile robots is growing rapidly, prompting an enormous amount of research over the last 30 years, on critical factors of mobile robots such as locomotion, perception, localization, mapping, ego-motion tracking, and dynamic navigation. This article surveys these essential factors of autonomous mobile robots in terms of mathematical modeling, control issues, and challenging factors. Brief discussions are provided on the fundamentals of these technologies, popular algorithms in comprehensive mode, future challenges, and promising directions to guide the construction of an autonomous mobile robot with high accuracy and effectiveness. Since it is difficult to find complete coverage of those topics in a single location, this article provides a guideline for researchers entering the field or for innovators in the mobile robotics sector. The paper also examines open challenges in indoor mobile robots and identifies potential futures for autonomous mobile robots.

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Section: E: Rapidly-explained Random Tree Algorithmsmentioning

confidence: 99%

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

et al. 2021

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“…-RRT-ENPS-GPU: a family of enzymatic numerical P systems implementing the RRT/RRT* algorithms [28]. -CuSNP: spiking neural P systems [3,4].…”

Section: Parallel Implementationsmentioning

confidence: 99%

Simulation challenges in membrane computing

Valencia-Cabrera¹,

Pérez–Hurtado²,

Amor³

2020

J Membr Comput

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P system simulators are critical tools to enable them as formal modeling framework for real-life applications. Such simulators abstract the concept of P systems in various ways, depending on the needs of the users and the requirements of the specific application. We identify three main levels of abstraction: graphical user interfaces, simulation engines and parallel implementations. In this paper, we survey the state of the art at these levels and discuss the main challenges under consideration for future developments.

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“…RRT is a sampling-based path planning algorithm, which is characterized by randomly extracting the configuration space or state space, and searching for connectivity in it [25,26]. This allows rapid planning in semi-structured spaces, and it is not only suitable for ordinary two-dimensional planes, but also for three-dimensional spaces.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

et al. 2022

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Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.

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A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

Cited by 51 publications

References 62 publications

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Critical Design and Control Issues of Indoor Autonomous Mobile Robots: A Review

Simulation challenges in membrane computing

Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

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