Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.

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COMPUTING WITH MEMBRANES (P SYSTEMS): A VARIANT

International Journal of Foundations of Computer Science ◽

10.1142/s0129054100000090 ◽

2000 ◽

Vol 11 (01) ◽

pp. 167-181 ◽

Cited By ~ 42

Author(s):

GHEORGHE PĂUN

Keyword(s):

Membrane Computing ◽

P Systems ◽

Open Problems ◽

Recursively Enumerable ◽

Pass Through ◽

The One ◽

Priority Relation ◽

Recursively Enumerable Languages ◽

Biochemical Features ◽

Computing Models

Membrane Computing is a recently introduced area of Molecular Computing, where a computation takes place in a membrane structure where multisets of objects evolve according to given rules (they can also pass through membranes). The obtained computing models were called P systems. In basic variants of P systems, the use of objects evolution rules is regulated by a given priority relation; moreover, each membrane has a label and one can send objects to precise membranes, identified by their labels. We propose here a variant where we get rid of both there rather artificial (non-biochemical) features. Instead, we add to membranes and to objects an "electrical charge" and the objects are passed through membranes according to their charge. We prove that such systems are able to characterize the one-letter recursively enumerable languages (equivalently, the recursively enumerable sets of natural numbers), providing that an extra feature is considered: the membranes can be made thicker or thinner (also dissolved) and the communication through a membrane is possible only when its thickness is equal to 1. Several open problems are formulated.

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Membrane Computing

Advances in Computational Intelligence and Robotics - Membrane Computing for Distributed Control of Robotic Swarms ◽

10.4018/978-1-5225-2280-5.ch002 ◽

2017 ◽

pp. 15-34

Keyword(s):

Robot Control ◽

Membrane Computing ◽

P Systems ◽

System Model ◽

P System ◽

Input Output ◽

Control Models ◽

P Colonies ◽

Theoretical Computing ◽

Computing Models

The theoretical computing models that are used throughout this book are described in this chapter. These models are based on the initial P system model and include: Numerical P systems, Enzymatic Numerical P systems, P colonies and P swarms. Detailed examples and execution diagrams help the reader allow the reader to understand the functioning principle of each model and also its potential in various applications. The similarity between P systems (and their variants) and robot control models is also addressed. This analysis is presented to the reader in a side-by-side manner using a table where each row represents an analysis topic. Among others we mention: (1) Architectural structure, (2) Modularity and hierarchy, (3) Input-output relationships, (4) Parallelism.

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Simple Neural-Like P Systems for Maximal Independent Set Selection

Neural Computation ◽

10.1162/neco_a_00443 ◽

2013 ◽

Vol 25 (6) ◽

pp. 1642-1659 ◽

Cited By ~ 7

Author(s):

Lei Xu ◽

Peter Jeavons

Keyword(s):

Distributed Computing ◽

Membrane Computing ◽

Independent Set ◽

Living Cells ◽

P Systems ◽

Maximal Independent Set ◽

Membrane Systems ◽

New Class ◽

The Individual ◽

Computing Models

Membrane systems (P systems) are distributed computing models inspired by living cells where a collection of processors jointly achieves a computing task. The problem of maximal independent set (MIS) selection in a graph is to choose a set of nonadjacent nodes to which no further nodes can be added. In this letter, we design a class of simple neural-like P systems to solve the MIS selection problem efficiently in a distributed way. This new class of systems possesses two features that are attractive for both distributed computing and membrane computing: first, the individual processors do not need any information about the overall size of the graph; second, they communicate using only one-bit messages.

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A Review of Membrane Computing Models for Complex Ecosystems and a Case Study on a Complex Giant Panda System

Complexity ◽

10.1155/2020/1312824 ◽

2020 ◽

Vol 2020 ◽

pp. 1-26

Author(s):

Yingying Duan ◽

Haina Rong ◽

Dunwu Qi ◽

Luis Valencia-Cabrera ◽

Gexiang Zhang ◽

...

Keyword(s):

Giant Panda ◽

Membrane Computing ◽

Model Performance ◽

Reproductive Rate ◽

P Systems ◽

Ecosystem Modelling ◽

Biological Phenomena ◽

The Rich ◽

Computing Models

Ecosystem modelling based on membrane computing is emerging as a powerful way to study the dynamics of (real) ecological populations. These models, providing distributed parallel devices, have shown a great potential to imitate the rich features observed in the behaviour of species and their interactions and key elements to understand and model ecosystems. Compared with differential equations, membrane computing models, also known as P systems, can model more complex biological phenomena due to their modularity and their ability to enclose the evolution of different environments and simulate, in parallel, different interrelated processes. In this paper, a comprehensive survey of membrane computing models for ecosystems is given, taking a giant panda ecosystem as an example to assess the model performance. This work aims at modelling a number of species using P systems with different membrane structure types to predict the number of individuals depending on parameters such as reproductive rate, mortality rate, and involving processes as rescue or release. Firstly, the computing models are introduced conceptually, describing the main elements constituting the syntax of these systems and explaining the semantics of the rules involved. Next, various modelled species (including endangered animals, plants, and bacteria) are summarized, and some computer tools are presented. Then, a discussion follows on the use of P systems for ecosystem modelling. Finally, a case study on giant pandas in Chengdu Base is analysed, concluding that the study in this field by using PDP systems can provide a valuable tool to deepen into the knowledge about the evolution of the population. This could ultimately help in the decision-making processes of the managers of the ecosystem to increase the species diversity and modify the adaptability. Besides, the impacts of natural disasters on the population dynamics of the species should also be considered. The analysis performed throughout the paper has taken into consideration this fact in order to increase the reliability of the prospects making use of the models designed.

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Robot path planning using rapidly-exploring random trees: A membrane computing approach

2018 7th International Conference on Computers Communications and Control (ICCCC) ◽

10.1109/icccc.2018.8390434 ◽

2018 ◽

Cited By ~ 1

Author(s):

Ignacio Perez-Hurtado ◽

Mario J. Perez-Jimenez ◽

Gexiang Zhang ◽

David Orellana-Martin

Keyword(s):

Path Planning ◽

Membrane Computing ◽

Random Trees ◽

Robot Path Planning ◽

Rapidly Exploring Random Trees ◽

Computing Approach ◽

Robot Path

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Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽

10.3390/s21062244 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2244

Author(s):

S. M. Yang ◽

Y. A. Lin

Keyword(s):

Path Planning ◽

Obstacle Avoidance ◽

Autonomous Vehicles ◽

Autonomous Vehicle ◽

Optimal Path ◽

Random Trees ◽

Average Deviation ◽

Lane Changes ◽

Path Smoothing ◽

Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

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