1,368 research outputs found
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
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-
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
An extension of GHMMs for environments with occlusions and automatic goal discovery for person trajectory prediction
This work is partially funded by the EC-FP7 under grant agreement no.
611153 (TERESA) and the project PAIS-MultiRobot, funded by the Junta de
Andalucía (TIC-7390). I. Perez-Hurtado is also supported by the Postdoctoral
Junior Grant 2013 co-funded by the Spanish Ministry of Economy and
Competitiveness and the Pablo de Olavide University.Robots navigating in a social way should use some knowledge about common motion patterns of people in the environment. Moreover, it is known that people move intending to reach certain points of interest, and machine learning techniques have been widely used for acquiring this knowledge by observation. Learning algorithms such as Growing Hidden Markov Models (GHMMs) usually assume that points of interest are located at the end of human trajectories, but complete trajectories cannot always be observed by a mobile robot due to occlusions and people going out of sensor range. This paper extends GHMMs to deal with partial observed trajectories where people's goals are not known a priori. A novel technique based on hypothesis testing is also used to discover the points of interest (goals) in the environment. The approach is validated by predicting people's motion in three different datasets.Universidad Pablo de Olavide. Departamento de Deporte e InformáticaPostprin
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
New applications for an old tool
First, the dependency graph technique, not so far from its current application,
was developed trying to nd the shortest computations for membrane systems
solving instances of SAT. Certain families of membrane systems have been demonstrated
to be non-effcient by means of the reduction of nding an accepting computation (respectively,
rejecting computation) to the problem of reaching from a node of the dependency
graph to another one.
In this paper, a novel application to this technique is explained. Supposing that a
problem can be solved by means of a kind of membrane systems leads to a contradiction
by means of using the dependency graph as a reasoning method. In this case, it is demonstrated
that a single system without dissolution, polarizations and cooperation cannot
distinguish a single object from more than one object.
An extended version of this work will be presented in the 20th International Conference
on Membrane Computing.Ministerio de Industria, Economía y Competitividad TIN2017-89842-
A P-Lingua based simulator for tissue P systems
AbstractInvestigations within the field of tissue-like P systems are being conducted, on one hand studying their computational efficiency, and on the other hand exploring the possibilities to use them as a computational modelling framework to biological phenomena.In both cases it is necessary to develop software that provides simulation tools (simulators) for the existing variety of tissue P systems. Such simulators allow us to carry on computations of solutions to computationally hard problems on certain (small) instances. Moreover, they also provide a way to verify tissue-like models for real biological processes, by means of experimental data.The paper presents an extension of P-Lingua (a specification language intended to become a standard for software devoted to P systems), in order to cover the class of tissue-like P systems, that were not considered in the previous release. This extension involves on one hand defining the syntax to be used, and on the other hand introducing a new built-in simulation algorithm that has been added to the core library of P-Lingua
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
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