Infrastructure
Design with
Multiagent
System and Virtual Reality
Application
to the PSA site
Stéphane GALLAND,
Olivier LAMOTTE, Nicolas GAUD
System and
Transportation Laboratory (SeT)
University of
Technology of Belfort-Montbéliard, Belfort, France.
e-mails:
{stephane.galland,olivier.lamotte,nicolas.gaud}@utbm.fr
Abstract
This document presents the works carried out to
ensure the immersion of a multiagent system in a virtual environment. The
researches undertaken within the framework of this work have been allowed the
development of tools dedicated to the simulation and the study of factory area,
which are generally in perpetual evolution.
This set of tools makes it possible to study various flows circulating
on these sites and to highlight the problems which it generate. They offer
solutions to refit an existing site in an optimal way or for a preliminary
study to the establishment of new
infrastructures. These tools provide a study of flows as well as a validation
of dimensioning and accessibility of the various industrial infrastructures,
this as well under normal conditions of operation as under particular
conditions such as the rush hours or the situation of panic (fire,
accident...). Simulation offers also the advantage of being able to integrate
into the models, and to evaluate the impact and the perspicacity of future
evolutions of the infrastructures. After a short presentation of the
theoretical subjacent models, this paper focus on the first application of
these works on the study of the industrial site of PSA Sochaux, one the major
French automobile manufacturer.
1. Introduction
In one hand, the virtual reality (VR) could be
considered as an new effective environment for the multiagent systems (MAS).
Indeed VR is a technology allowing the immersion of a human in a virtual world.
It also provides natural and ergonomical interaction's ways to the users, and
is an elegant mean to populate virtual worlds with entities exhibiting
realistic behaviors (humans, animats...).
In another hand, multiagent-based simulation (MABS)
in virtual reality offers tools to answer to several application's domain
problems. First of all, the validation - synchronously or asynchronously -
of the structural models of an urban environment could be done by an user via
his virtual representation, e.g. his avatar (see section 3.2.1). This checking
includes geometrical and spatial coherencies, the adequacy of road signs, and
the accessibility of infrastructures. Another kind of interesting usage is the
highlighting and the study of emergent behaviors of the real users. The
immersion of them could also be used to validate the realism of the model.
This paper focus on one of the first application of
these approach: the study of the industrial site of PSA Sochaux - one the
major French automobile manufacturer. Greater than 250 hectares, this site is
in perpetual evolution: infrastructure modifications, road system
adaptations... By the addition of a manufacture's virtual model and a MAS, we
are able to simulate day-to-day logistical flows in a realtime and continuous
way. This application allows an immersive and real time evaluation of
architectural and infrastructural modifications impacts. The bottlenecks could
thus be detected earlier than the design stage.
The implementation of this study case lifts three
major problems:
the immersion of a standard multiagent system inside
a virtual environment;
the guarantee of realistic behaviors for the entities
populated the virtual environment;
the interaction between the users and the virtual
world, i.e. with objects or agents.
In this article, for each of these problems, we
propose models and experimental results. Moreover we mainly focus on the real
application simulation of the PSA site, including 3d modeling and user
interface for navigation.
2. Context and Applications
Our problematic is bipolar : the virtual reality and
the multiagent systems. A multiagent system is composed by artificial agents.
Each of them refers to a software or a physical artefact which must be
autonomous, i.e. operationally and informationally closed to its
environment [48]. For [2] an agent is an entity which perceives its
environment with its sensors and acts on it with its effectors. It must be
autonomous (acting without external driving), reactive (ability to respond to
external events), goal-driven, situated inside an environment.
The multiagent systems permit to model the behaviors
of the agents and the interactions between them. The virtual reality offers
mechanisms for the realtime realistical rendering and means for the humans to
interact.
This duality of the problematic can also be found in
others application domains using MAS inside VR environments:
virtual teaching [3] with a virtual agent such
as Steeve [37,17],
scientifical simulation: SIAMES[1], OpenMask[2],
ethological emergent phenomena's study [7],
virtual prototyping [44,18],
special effects for the cinema industry or for the
video games [36],
tele-operation [50,12,28],
automatical scenario building for virtual
world [33,4],
urban
simulation [29,46,45,56,30,31,32,49,52,51].
All there applications have a common point of view:
the virtual reality permits to an human to interact with a logical model of a
system. It is the starting point of a new generation of user-interfaces which
present the information in natural and sometime ludical ways. Multiagent
systems are really well adapted to evolve in a virtual environment (VE), not
only thanks to their intrinsic characteristics such as autonomy and situation
but also because they are computationally efficient, robust, safety, flexible
and lower computational costly for large-scale simulation [1].
3. Contributions
In this section we explain the
problems which we want to tackle. For each of them a model is proposed and
briefly discussed. Our works are on the scope of two issues: the settlement of
a virtual world and the interaction between a human and the entities populated
this virtual world.
3.1.
Settlement of Virtual Environments
As introduced by Thalmann,
virtual sensors constitute a key tool to implement perception for virtual
agents [43]. These agents could be equipped with visual, auditory and
eventually tactile sensors. These sensors are at the base of the behaviors of
all the agents of the simulation.
3.1.1.
Perception Architectures
Visual Perception To evolve in a virtual world and exhibit high level
behaviors, an agent requires several semantical, symbolical and topological
data about its environments. To assure these behaviors virtual worlds should
integrate these informations in addition to the geometry. Perception is thus a
problem in the intersection between the domains of the virtual environment's
modeling and the design of architectures for virtual sensors. We argue that
vision problems couldn't be solved without the use of an adapted environment
model.
The
principles of synthetic vision is to simulate the biological vision organs. All
these methods work towards a common end : allowing a visual perception for
autonomous entities in virtual environment. They are mostly inspired from the
3d-rendering techniques. The first
synthetic vision system was introduced by Renault et al [35]. Many works
succeeded them since for in particular trying to adapt the synthetic vision to
the constraints of virtual reality [47,42,26,22].
Wen
et al. presented an adaptation of [35] in which an octree[3] was used to assure the hierarchical scene
decomposition [53]. The synthetic vision is thus reduced to a simple
intersection test between the agent's view frustum and the object's AABB[4]. But this system is still incompatible with the
simulation involving a great number of agents : they spend too much
computational time [24].
We
have avoided this incompatibility using a synthetic vision method associated to
an adapted environment model. Our environment model is inspired from the
Informed Environment developped by Farenc et al. [10] and VUEMS presented
by Donikian [6]. We define the virtual environment for a multiagent system
as an multi-layers architecture (see fig. 2):
·
3d database: this level is
considered as a database of all the objects into the scene. In most of the
case, this database is only used by the rendering engine during the simulation.
·
Metric Environment: this is
the lowest level into the MAS and the highest level into
the 3d. Indeed it structures the 3d information into
dedicated spatial data trees which could be easily used by the agents, or
called by the higher environment layers. The metric environment could be mostly
generated prior to the simulation starting.
·
Linear-Graph Environment:
this is an example of high level environment. It defines
the
space as a set of edges and nodes. Each of them are associated to an surface,
i.e. an edge could be associated with a road, a node with a crossroad.
·
Homotopic Environments: there
environments act as layers between a lower and an higher environments.
·
Semantical Environment: this
is a transversal level in which all the object's semantics are specified. It
could be based on a simple object-oriented typing or on an urban ontology. Each
object from the other environments point to one or more concepts from this
level.
Because the metric
environment must be designed to assure the fast visual perception for a great
number of agents, we focus on it (see fig. 3). In this environment, the
entities have been classified into two categories : immovable (or static) and
mobile (or dynamic). For the agents, in terms of geometry, only the bounding
boxes of the 3d-objects are really required. We store environmental entities
inside adapted spatial data structures inspired from the dynamic AABB tree
presented by Shagam [38]. The
vision process on these structures is reduced to a simple 2d-frustum and
occlusion culling. The frustum surface associated to the agent's point of view,
is successively tested against the AABB of each node to determine fully and
partially visible objects. Then we use a simple occlusion culling algorithm
(Z-buffer equivalent) to assure the exact object visibility. The semantic of
each perceived object is then extracted from the semantical environment and
returned to the requiring agent.
Auditive Perception Another key point for
designing believable agents is to allow them to perceive sounds inside their
environment. In parallel to the visual perception architecture, two models for
imobile- and mobile-sound sources are developed. These sources emit signals
transporting specifical semantics. Each signal is propagated inside a
particular spatial envelope. Some works already focussed on the simulation of
the sound physics [43,27,5]. But they are still difficult and
computationally costly to implement, especially for realtime sound sources. The
development of a simple model is
suggested by us. It is designed to respect the realtime constraints, and enough
realistic to not introduce a bias into the simulation results.
The application of a sound
perception model could be applied to the simulation of blind men or
badly-sighted people, or to the simulation of building-evacuation scenarios
without any ambient or spot light.
3.1.2.
Behavioral Definitions
The definition of efficient
perceptual mechanisms is not sufficient for a multiagent system to properly
respond to a problem. Indeed it is necessary to the agents to exhibit
realistical behaviors for an external observer. Two major approaches focus on
the modeling and the simulation of the agent's behaviors : deliberative agents
(or cognitive agents) and the reactive agents [39,11,55,15].
The deliberative agents have
the essential property to choose the next action they must realise. This choice
is based on internal critera (perceptions, mental state...) and could be very
simple (such as a stochastic choice) or based on more complex theories (social
or acts of language theories...). For instance the agents could exhibit a
Believe-Desir-Intension architecture [54,34,25]. With it, an agent has a
set of goals and computes several action plans to rich them - eventually
by initiating communications with other agents to ask some kind of help on the
task realization. Several other works aim to use natural human communication
channels to allow the agents to interact with their environment [37,8], or
use ontologies as conceptual and vocabulary basis between the agents [21,9].
But all their approaches need computational resources which is proportional to
the cognitive architecture or to the ontologie complexity for instance. In most
of the cases, the cognitive agents are not compatible with large-scale realtime
systems.
The property of large
quantity of agents is, from our point of view, one of the major characteristics
of a multiagent system immersed in a virtual environment. To support this
constraint, we propose to use reactive agents with simple or instinctive
behaviors. This approach will permit to develop more complex agents, i.e.
cognitive agent based on reactives behaviors such as spatial moves. This last
is indeed one of the major problem of situated agents. [36] proposes to use
potential-fields for the simulation of flocks. [20,16,24] propose models for
the simulation of crowds. Several works on the mobile robotics and on the
usability of multiagent systems to drive autonomous robots use the
potential-field approaches too. They associate to each object of the system
repulsive or attractive vectors. The moving decision is simply the addition of
all the vectors inside the influence area of the agent. This reactive approach
has already proved its computatonial efficience and its realism in most of the
cases [40].
But several spatial
configurations are not truly supported and generate blocking or infinite-loop
situations (see fig. 1 - cul-de-sac...). To solve this problem, [40]
introduces an indicator which express if a agent was satisfied or not by the
status of its task. Comparing the different agent's satisfactions permits to
determine the most unsatified agent. Then all the other agents inside the
influence area of this agent switch to an altruism behavior which inhibit the
agent's tasks in profit to the unsatisfy agent.
This model of reactive agent
initially proposed for autonomous robots has successfully transposed to virtual
agents. Inded, this model is only dependent of the perception mechanisms which
are already developed for VE. Moreover Simonin
indicates that, if the perception method respects the realtime constraint, then
the satisfaction-altruism model is also compliant with this contraint, i.e. the
model satisfaction-altruism is a compatible model with a virtual reality
environment [40].
Several approaches aim to
move a part of the intelligence from the agents to the environment [10].
This is due to the fact that the environment objects contain their attributes
but also the means to interact with them. In such way [19] propose the
concept of Smart Object.
The agents could not have any
knowledge about how an object must work. They only store the mean to interact
through a generic interface. We translate this approach into a more holonic
point of view[5] [14].
This « agentification » of the environment permits to develop more
complex and large models without any restrive constraint on the agent's
architectures.
3.1.3.
Immersing a MAS into a VE
Modeling of the VE On the user level, the
virtual representation of the study area is very significant, especially if
this environment is modelled starting from real data, e.g. from a Geographical
Information System (GIS). It should correspond accurately to the reality, as
well for the geometry as for the texture mapping and the decorations... Based
on traditional techniques of realtime computer graphics sciences, the virtual
environment, in which the agents evolve, is a 3d-model. It is made up from
various objects modelled in three dimensions, and each of them are made up from
triangular facets. However, the study area is generally very large and the
number of objects in the scene (agent representations, buildings,
decorations...) can be significant, so various computer graphics techniques
must be implemented in order to preserve a fluidity in displacement, i.e.
places and portals [23,41], BSP Tree[6]...
Multi-level simulation The multiagent-based
simulation (MABS) differs from many other kinds of computer-based simulation in
that the simulated entities are designed and implemented in terms of agents.
MABS models are usually regarded as microscopic simulation models, in
opposition to the macroscopic models of simulation based on flows, Markovian
processes, queueing systems, Petri networks...
But it is utopian to simulate at a microscopical level a whole city or a large industrial area where the number of agents - or entities - could easily exceeded 20.000 entities on a traditional IT-platform, i.e. without a cluster of calculators. We argue that the solution is inevitably in hybrid solutions: simulations including intrinsically different levels of simulation. We have already developed an application integrating three levels of simulation: micro (e.g. vehicles), meso (e.g. roads and streets), macro (e.g. factory). Today we work on a more general model where the quantity of simulation levels depends on the available IT-resources and on the complexity of the organizations in which the agents are implied. These models are integrated on a model of holonic environment and holonic agents' organizations[7].
Interface between the MAS and the VR platform Always
with the aim to guarantee a realtime system, calculations are shared between
several units to allowing charge balancing. It permits also to physically
separate the different treatments and to specialize the computers according to
their tasks. For example a computer could be dedicated and optimized for the
graphical part and the other machines for the MAS... This way imposes however
the development of communication techniques between the calculators. It must
being subjected to the constraint of realtime. We must thus prevent the
bottlenecks and optimize the transmissions of messages. A first development
uses only the traditional socket messages. However, in order to increase the
possibilities of the architecture, we currently work on a CORBA realtime core
version.
The visual perception component
previously presented could also be seen as an interaction mean between a MAS
and a VE. Indeed its high-level functionst, i.e. methods for requesting a
perception inside a space, are perceptual primitives from the agent's point of
view. By this fact, the visual perception component is a part of the MAS
environment and allow the agents to directly and transparently interact with a
virtual environment.
3.2. Realtime
Human Interactions with the Agents
3.2.1. Avatar
Definition
The interaction and the
immersion are the two fundamental characteristics of all virtual reality
application [12]. It is essential for the user, since his real universe,
to immerse himself in this virtual environment in order to interact with it or
with the entities which populate it. With this intention, the user needs an
existence in the virtual universe: the avatar - his graphical
representation and his interactional vehicle inside the virtual world.
Within the framework of our
developments, we conceive an avatar allowing the user to freely move inside the
virtual universe. It is concretely realized with the devices of our
laboratory's VR platform, namely a system of optical motion capture[8].
The user drives his avatar in an instinctive way with a Flystick[9]
device. However, the core of the development is sufficiently modular to allow
the use of other devices.
3.2.2.
Interactions Avatar-Agent
In the multiagent system the avatar is regarded as an agent. Its interface is similar to that of the other agents. Its behavior on the other hand is the responsibility of the user, who controls it via the interaction means described in the above section. The avatar is regarded as an agent identical to the others. Consequently it can communicate with the whole of the multiagent system. One of the main problems is to provide to the user several intuitive ways of interaction which enable him to maintain, via its avatar, a simple level of communication with the other agents. This communication constitutes the support for a possible cooperation between the avatar and the agents.
4. Application to the PSA Site
We detail in this part a range
of tools at the disposal of the companies to manage the establishment of a new
industrial site or the refitting of an existing one. These tools are located inside the scope of the study of
logistic, production or people flows in order to determine the optimal
adjustments of the site.
4.1.
Congestion of Infrastructures
Within the
framework of the general study of the PSA Sochaux site (see fig. 4) flows,
the simulation highlights a certain number of malfunctions, mainly in the
exchanges between the site buildings. Indeed, we reveal the location of the
main production cells and the exchanges generated throughout one day with different
time scales. Their localizations specified on the virtual site enable us to
study various flows and their frequencies in order to clarify the significant
hotspots of the road structure. We detect the bottlenecks in logistic flows and
propose an adapted dimensioning of the concerned infrastructures. Our tools
also allow as well as possible to position the buildings' production functions
according to the volume of their mutual exchanges.
4.2.
Accessibility of Infrastructures
One of the main application of our tools is the validation of the infracstructures' accessibility in panic or day-to-day situation. We can check the efficiency and the good location of the road signs inside the site. Our tools also provide an average time of access to the infrastructures for each of the categories of entities evolving on the factory site: pedestrian, vehicles, trucks... By this way we detect the bad dimensioning of the infrastructures for each type of them. For example we validate if the signs intended for the emergency evacuation of an administrative building allow the people under panic to evacuate under the best conditions. This constitutes a complementary tool to the flow study presented in section 4.1. Using these two approaches we can draw up a complete panorama of the factory site in term of bottlenecks, dimensioning (road, corridor...), and infrastructures' accessibility.
4.3. Modification and Validation of
Infrastructures
An interesting application of our models is the
ability to change the spatial location of the infrastructural buildings and
elements. The user could moves a building for studying its influence on its
environment. Moreover we plan to propose interactive tools which allow to
update or change the infrastructural models (roads, streets, buildings...).
With the introduction of a temporal parameter, this set of tools could be
associated to scenarios for simulating the past, present or future evolutions
of an area. In such a way, we already simulate the PSA site exchanges between
the buildings and show the results with chronological or chronomorphical maps[10],
i.e. the buildings are colored in adequacy to their time-dependent functions
(see figure 4).
5. Conclusion
This paper briefly introduces our works on the
immersion of a multiagent system inside a virtual environment. We describe the
different steps to achieve this immersion : perception architectures, agent
behaviors, virtual environment modeling...We also present a first framework to
provide to the user intuitive ways of interaction which allow him to maintain a
simple communication with the whole of the multiagent system. The undertaken
research works result on the development of tools intended for the companies
owning large factory sites. These tools enable us to draw up a complete
diagnostic on accessibility, the problems of logistic and people's flows inside
a factory. We propose adapted solutions in term of adjustment and refitting of
an industrial or an urban area.
The simulation enables us to
integrate future modifications in terms of infrastructure's development as well
as in terms of logistic and entity
flows. The possibility of testing these
future modifications under normal conditions of use as much as in exceptional
conditions (i.e. panic) enables us to finely apprehend the possible consequences of a set of
policies.
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