Decision-Theoretic Planning with Person Trajectory Prediction for Social Navigation

Robots navigating in a social way should reason about people intentions when acting. For instance, in applications like robot guidance or meeting with a person, the robot has to consider the goals of the people. Intentions are inherently nonobservable, and thus we propose Partially Observable Markov Decision Processes (POMDPs) as a decision-making tool for these applications. One of the issues with POMDPs is that the prediction models are usually handcrafted. In this paper, we use machine learning techniques to build prediction models from observations. A novel technique is employed to discover points of interest (goals) in the environment, and a variant of Growing Hidden Markov Models (GHMMs) is used to learn the transition probabilities of the POMDP. The approach is applied to an autonomous telepresence robot

Generation of rapidly-exploring random trees by using a new class of membrane systems

Methods based on Rapidly-exploring Random Trees (RRTs) have been in use in robotics to solve motion planning problems for nearly two decades. On the other hand, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers for more than six years. These controllers in real robots handle the power of motors ac- cording to motion commands usually generated by planning algorithms, but today there is a lack of planning algorithms based on membrane sys- tems for robotics. With this motivation, we provide in this paper a new variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) oriented to RRTs for planning in robotics and we illustrate it by presenting a model for generation of RRTs with holonomic limitations. We are working on the ENPS frame- work with the idea of moving towards a complete mobile robot system based on membrane systems, i.e. including controllers and planning; and we have incorporated new ingredients into the ENPS framework to meet the requirements of the RRT generation algorithm

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.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

A Formal Framework for P Systems with Dynamic Structure

This article introduces a formalism/framework able to describe different variants of P systems having a dynamic structure. This framework can be useful for the definition of new variants of P systems with dynamic structure, for the comparison of existing definitions as well as for their extension. We give a precise definition of the formalism and show how existing variants of P systems with dynamic structure can be translated to it

P-Lingua: A Programming Language for Membrane Computing

Software development for cellular computing has already been addressed, yielding a first generation of applications. In this paper, we develop a new programming language: P-Lingua. Furthermore, we present a simulator for the class of recognizing P systems with active membranes. We illustrate it by giving a solution to the SAT problem as an example.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58

Simulating Tritrophic Interactions by Means of P Systems

P systems provide a high level computational modelling framework that combines the structural and dynamical aspects of ecosystems in a compressive and relevant way. The inherent randomness and uncertainty in biological systems is captured by using probabilistic strategies. The design of efficient simulation algorithms in order to reproduce the behavior of these computational models over conventional computers is fundamental for the validation and virtual experimentation processes. In this paper, we describe the modelling framework and two different simulation algorithms. As a case study, a P system based model of an ideal ecosystem with three trophic levels is designed and simulated by both simulation algorithms, providing comparisons of efficiency between them

Formal Verification of P Systems with Active Membranes through Model Checking

Formal verification of P systems using model checking has attracted a significant amount of research in recent years. However, up to now only P systems with static structure have been considered. This paper makes significant advances in this area by considering P systems with active membranes, in particular P systems with division rules. The paper presents a theoretical framework for addressing this problem and reports on a complex case study involving a well-known NP-complete problem solved using P systems with membrane division rules. This is implemented in Promela and non trivial properties are verified using Spin.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

Comparing simulation algorithms for multienvironment probabilistic P systems over a standard virtual ecosystem

Membrane Computing has recently proved to be a suitable framework for addressing the modelling of dynamical biological systems in general, and ecosystems in particular. Due to the inherent randomness and uncertainty in biological systems, when designing a model the relevant tasks to be addressed are the validation and virtual experimentation processes, rather than the formal verification. It is therefore crucial to rely on software implementations of efficient simulation algorithms. This paper presents a simple (but realistic enough) ecosystem where a carnivore and several herbivorous species interact. The model of this ecosystem has been used to compare experimentally the performance of two different simulation algorithms.Ministerio de Ciencia e Innovación TIN2009–13192Junta de Andalucía P08-TIC-0420

P-Lingua 2.0: New Features and First Applications

P-Lingua is a programming language for membrane computing. It was rst presented in Edinburgh, during the Ninth Workshop on Membrane Computing (WMC9). In this paper, the models, simulators and formats included in P-Lingua in version 2.0 are explained. We focus on the stochastic model, associated simulators and updated features. Finally, we present two new applications based on P-Lingua 2.0: a tool for describing and simulating ecosystems and a framework (currently under development) for P systems design.Ministerio de Educación y Ciencia TIN2006–13425Junta de Andalucía P08-TIC-0420

A syntax for semantics in P-Lingua

P-Lingua is a software framework for Membrane Computing, it includes a programming language, also called P-Lingua, for writting P system de nitions using a syntax close to standard scienti c notation. The rst line of a P-Lingua le is an unique identi er de ning the variant or model of P system to be used, i.e, the semantics of the P system. Software tools based on P-Lingua use this identi er to select a simulation algorithm implementing the corresponding derivation mode. Derivation modes de ne how to obtain a con guration Ct+1 from a con guration Ct. This information is usually hard-coded in the simulation algorithm. The P system model also de nes what types or rules can be used, the P-Lingua compiler uses the identi er to select an speci c parser for the le. In this case, a set of parsers is codi ed within the compiler tool. One for each unique identi er. P-Lingua has grown during the last 12 years, including more and more P system models. From a software engineering point of view, this approximation implies a continous development of the framework, leading to a monolithic software which is hard to debug and maintain. In this paper, we propose a new software approximation for the framework, including a new syntax for de ning rule patterns and derivation modes. The P-Lingua users can now de ne custom P system models instead of hard-coding them in the software. This approximation leads to a more exible solution which is easier to maintain and debug. Moreover, users could de ne and play with new/experimental P system models