path finding and collision avoidance in crowd simulation · keywords:...

Journal of Computing and Information Technology - CIT 17, 2009, 3, 217–228doi:10.2498/cit.1000873

217

Path Finding and Collision Avoidancein Crowd Simulation

Cherif Foudil1, Djedi Noureddine1, Cedric Sanza2 and Yves Duthen2

1LESIA, Department of Computer Science, University of Biskra, Algeria2VORTEX, IRIT, Toulouse, France

Motion planning for multiple entities or a crowd is achallenging problem in today’s virtual environments. Wedescribe in this paper a system designed to simulatepedestrian behaviour in crowds in real time, concentrat-ing particularly on collision avoidance. On-line planningis also referred to as the navigation problem. Additionaldifficulties in approaching navigation problem are thatsome environments are dynamic. In our model weadopted a popular methodology in computer games,namely A∗ algorithm to find the first itinerary of eachentity. The idea behind A∗ is to look for the shortestpossible routes to the destination, not through exploringexhaustively all possible combinations, but utilizing allpossible directions at any given point. In order to dealwith collision avoidance problems, priority rules aregiven to some entities as well as some social behaviour.These rules solved the problem of disorder in the crowdmovement.

Keywords: path finding, collision avoidance, behaviouralanimation, crowd simulation

1. Introduction

The autonomy of a virtual human is defined byits capacity to perceive, act and decide aboutits actions. The behaviour is usually describedthrough several simple skills that can be mixedto generate a more complex and credible be-haviour. One of the most important skills is theability to navigate inside a virtual environmentas it is part of a large number of behaviours. Re-producing this fundamental behaviour requiresto address different topics such as the topolog-ical model of the environment, path planningand collision avoidance techniques.

In order to animate a crowd of pedestrians inreal-time, each of these techniques should be

optimized without leaving out behavioral stud-ies. A crowd is not only a group of many in-dividuals: crowd modelling involves problemsarising only when we focus on crowds. For in-stance, collision avoidance among a large num-ber of individuals in the same area requires dif-ferent resolving strategies in comparison withthe methods used to avoid collisions betweenjust two individuals. Also, motion planningfor a group walking together requires more in-formation than needed to implement individualmotion planning. An important guideline forour work is that, as in real life, each virtualpedestrian should be an autonomous, intelligentindividual. More explicitly, each pedestrianshould be able to control itself across percep-tual, behavioural and cognitive levels, just likereal people, it should be an autonomous agentthat does not require any external, global coor-dination whatsoever, including control by anyreal human animators in order to cope with itshighly dynamic environment.

In this article, we propose a general model, in-spired by studies on human behaviour, to sim-ulate the navigation process in dynamic envi-ronments. We focus our work on the meth-ods of collision avoidance between pedestrians.We will demonstrate that the use of these rulessolved the problem of disorder in the pedestrianmovement.

2. Related Works

The simulation of behaviour has been studiedsince the earliest days of computer graphicsresearch. Early work concentrated on animal

218 Path Finding and Collision Avoidance in Crowd Simulation

behaviour, with birds a popular choice, but re-cently there has been a lot of work on humanbehaviour. Techniques for simulating a crowdas a single entity have been proposed, as well asthose which consider each person in the crowdseparately. In the virtual environments commu-nity, the most common approach to simulatinggroup movement is to use flocking. The con-cept of flocking was introduced by Reynolds[15]. His boids-model described behaviour ofthe units in a group using only local rules for theindividual units. Later, Reynolds extended thetechnique to include autonomous reactive be-haviour [16]. The idea is that units steer them-selves in such a way that they avoid collisionswith other units in the environment, while at thesame moment, they try to align themselves withother units and try to stay close to them.

In open areas this leads to rather natural groupbehaviour as can be observed in flocks of birdsor schools of fish. When we also give the unitsa goal, they will move toward the goal together.The big drawback of this approach is that theunits act based on local information which eas-ily gets them stuck in cluttered environments.Also, the combined steering behaviour can eas-ily lead to the group breaking up.

Another widely used technique is grid search-ing in which the environment is divided into agrid that can be searched for a free path usingA* like approaches [17]. Different units try tofind a path through the grid while avoiding colli-sions with each other. This easily leads to unitsgetting stuck in ways that can only be resolvedby rather unnatural motions (or cheating, likepenetrating the walls).

The social potential field technique [14] definespotential force fields between units of the group.Desired behaviour is then created by definingthe correct force fields. However, the sameproblem as in flocking arises because only localinformation is taken into account.

Kamphuis andOvermars [9] developed amethodfor planning the motion of a coherent group ofunits using a multiphase algorithm. First, a pathis planned for a deformable rectangle, represent-ing the group shape. Second, the internal mo-tion of the units inside this deformable rectangleis calculated using social potential fields. Third,the global and local paths are combined to givethe total motion of the units. Although the tech-nique guarantees coherence, it lacks complete-

ness. The approach also generates unnaturalbehaviour when a group enters or leaves a nar-row passage.

Bayazit, Lien and Amato [4] have combinedthe probabilistic roadmap approach (PRM) ap-proach with flocking techniques. The units usethe roadmap created by PRM to guide their mo-tion toward the goal while they use flocking toact as a group and avoid local collisions. Whilethis indeed leads to better goal finding abilities,groups still split up easily.

Li and Chou [12] developed an approach thatallows dynamic structuring of the units suchthat the centralized planning of the motions isgreatly improved. Again, this approach lacksthe ability of guaranteeing coherence.

Crowd simulation also investigates the move-ment of large numbers of units in a virtual en-vironment. This research area has received vastamounts of attention over the last few years,such as [13],[20]. Although related to our re-search, the area has a different goal. The globalidea behind crowd simulation is to have virtualunits behave in a natural way, interacting witheach other, based on (social) rules. The emer-gent behaviour of the units is then studied.

Other work in this area is by Feurtey [6], whouses a space-time approach to predict collisionswith other actors, Helbing and Molnar [8], whouse a social force model to simulate move-ment based on motivations, Blue and Adler[5], who use a cellular automata model, Gilliesand Dodgson [7], who concentrated on obstacleavoidance and the simulation of attention, andLamarche and Donikian[10], whose work onpath finding also includes ideas on behavioursimulation.

Other recent work is done by Rymill and Dodg-son [18] simulating human behaviour in crowdsin real-time, concentrating particularly on colli-sion avoidance. The algorithms used are basedheavily on psychology research and their ap-proach gives better results than conventionalmethods.

Shao and Terzopoulos [19] address the difficultopen problem of emulating the rich complex-ity of real pedestrians in urban environments.Their artificial life approach integrates motor,

Path Finding and Collision Avoidance in Crowd Simulation 219

perceptual, behavioral, and cognitive compo-nents within a model of pedestrians as individu-als. They represent the environment using hier-archical data structures, which efficiently sup-port the perceptual queries of the autonomouspedestrians that drive their behavioral responsesand sustain their ability to plan their actions onlocal and global scales.

3. The Problem of Path Finding

Path planning consists of finding an optimalpath (generally the shortest one) between astarting point and a destination point in a virtualenvironment, avoiding obstacles. Traditionally,path planning has been solved using a heuristicsearch algorithm such as A* [1],[3] directly cou-pled with the low-level animation of the agent.The use of A* for path planning is based on atwo step process. The virtual environment isfirst discretised to produce a grid of cells. Thisgrid is formally equivalent to a connectivity treeof branching factor eight, as each cell in thediscretised environment has eight neighbours.Searching this connectivity tree with A* using adistance-based heuristic (Euclidean distance orManhattan distance) produces the shortest pathto the destination point. This path is calculatedoffline, as A* is not a real-time algorithm, andthe agent is subsequently animated along thispath. As a consequence, this method cannotbe applied to dynamic environments. This di-rect integration of A* with low-level animationprimitives is faced with a number of limitations.

In order to let an object or character move insidea scene from one location to another, a path hasto be planned that guarantees a collision-freetranslation from the start to the goal position.Hence, the whole task of path planning is usu-ally broken down into three sub-problems:

• First, one has to find a suitable discretiza-tion of the ground on which one can builda graph. This can be done offline in a pre-processing step. The resulting graph shouldbe as lean as possible to allow a fast search.If the graph is too large, the search will besignificantly slowed down. One the otherhand, the discretization should be as fine aspossible so that the areas corresponding tograph nodes are not too large. This would

lead to an approximation error which endsup in suboptimal paths.

• Then, the graph has to be searched for a so-lution which connects the found nodes. Forstatic environments, as expected, the A* al-gorithm is commonly used.

• Afterwards, the resulting sequence of graphnodes needs to be transferred back to theoriginal environment.

4. A∗ Algorithm

The standard search algorithm for the shortestpath problem in a graph is A*. It is a directedbreadth-first search and combines the advan-tages of uniform-cost and greedy searches usinga fitness function:

f (n) = g(n) + h(n);

where g(n)denotes the accumulated cost fromthe start node to node n and h(n) is a heuris-tic estimation of the remaining cost to get fromnode n to the goal node.

During the search the A* algorithm maintainstwo lists of nodes: The open list contains thenodes that have to be considered next and theclosed list which contains the nodes already vis-ited. The algorithm itself consists of expandingthe one node from the open list, whose fitnessfunction is minimal. Expanding a node meansputting it into the closed list and inserting theneighbours into the open list and evaluating thefitness function. The algorithm stops, when thegoal node gets expanded.

The choice of a good heuristic is necessary in or-der to achieve both quality and efficiency of thesearch. As long as the heuristic underestimatesthe real cost, the shortest path is guaranteed tobe found. Nevertheless, underestimating caneasily lead to an expansion of too many nodes.But when the heuristic is allowed to overesti-mate the remaining cost, faster results can beachieved because fewer nodes get expanded.If overestimating the distance to the goal, theA* algorithm tends to expand nodes that lie onthe direct path to the goal before trying othernodes. But this can also lead to significantlyslower searches if the final path contains direc-tions that lead away from the goal [11].


5. System Overview

This section discusses various aspects of oursolution. First, we present how to create thevirtual environment and how to discretizate thescene into cells in order to form a graph. Sec-ond, we present our modified algorithm of A∗ inorder to be used in dynamic environment. Thisalgorithm is used by every individual inside itsvisible region. Then we focus our discussion oncollision avoidance types and situations. Theindividuals must avoid collisions with the envi-ronment and with each other.

Given a pedestrian’s current location and a tar-get destination, it exploits the topological mapat the top level of the environment model. Byapplying path search algorithms within the pathmaps associated with each region, the pedes-trian can plan a path from the current locationto the boundary or portal between the currentregion and the next. The process is repeated inthe next region, and so on, until it terminates atthe target location.

5.1. Scene Modelisation

In our problem setting we are given a virtualenvironment in which individuals must movefrom a given start point to a given goal position.The information to be given are:

• The number and position of the obstacles inorder to form the desired environment.

• The number of agents, the position and thegoal point for each agent. More than oneagent could have the same goal position.

5.2. Discretization

Our approach is a cell decomposition approachwhich uses a modified A∗ to find paths for aset of agents. The first task is to discretize thescene into obstacle-free regions. Each agentoccupied one cell, but an obstacle can occupyseveral cells in the environment.

5.3. Path Finding

Autonomous pedestrians are capable of auto-matically planning paths around static and dy-

namic obstacles in the virtual environment. Af-ter the discretization of the scene, each agentcomputes its path by applying the A∗ algorithmin its visible region. The boundary or portal be-tween the current region and the next becomesthe intermediate target location. At this stagethe algorithm takes into account only the staticobstacles.

The next stage of the simulation is the pedes-trian’s movement. So, for each frame of thebehavioural animation and before getting to thenext position, each agent must process the colli-sion prediction to avoid collision with the otheragents in the environment.

5.4. Collision Prediction

In each frame of the simulation, every agentneeds to check for future collisionswith all otheragents in the scene. If a collision has been pre-dicted, the type of collisionmust be determined.Every pedestrian has its itinerary saved in thedata structure. This itinerary was computed bythe path finding module using the A* algorithm.This algorithm produces for each agent a list ofnodes which formed its initial itinerary. Theseitineraries are free of static obstacles.

A vector represents the mapping from a presentagent position to a desired moving direction.The agents determine their moving direction byreferring to this vector at the present location.This strategy is well suited to design a com-plex flow pathway. The moving direction isdetermined according to the positional relationamong the agents.

The trajectory of each pedestrian will be usedin the collision avoiding process. The pedestri-ans have to deal with two situations of collisionprediction: the first situation when two agentsreach the same cell and the second one, whenthe agents reach the cell of each other (Figure1).

After an agent has successfully avoided a col-lision, it should return to its original path. Thepath is represented by an order list of nodes, butit should also not suddenly change direction todo so, as this would look unnatural. If the agentgoes away from its initial itinerary, we proposethat the agent calculates a new path using theA* algorithm.


If a collision has been predicted, then the type ofcollision must be determined. There are threepossible types of collision, which will be calledToward, Away and Glancing.

The behaviour in the next frame of each agentdepends on the type of collision prediction andthe type of collision avoidance. The detail willbe described in Figure 3.

In real life, there are three possible types of col-lision, called Toward, Away and Glancing; theyare shown in Figure 2.

• Toward collision : or face-to-face, occurs ifthe agents are walking toward each other;

• Away collision: or rear, when the agent isbehind the collidee (another agent or an ob-stacle);

• Glancing collision: is a side-on collisionbetween two agents walking in roughly thesame direction;

a) Two agents reach the same cell.

b) The agents reach the cell of each other.

Figure 1. Collision prediction types.

5.5. Collision Avoidance

Collision avoidance between agents can involvesome problems that only appear when we dealwith many agents. A method to avoid col-lision between individuals can be inefficientwhen we have several ones. There are more

constraints and variables when a complex en-vironment (with fixed obstacles, mobile obsta-cles, and small regions to walk) includes manyvirtual human agents. Yet, if the structure ofthe group has to be preserved, this adds anotherparameter in the complexity of crowd collisionavoidance.

First, consider how we avoid collisions in ourhuman life for collision avoidance. It is verycomplex, but it can be defined by some sim-ple rules. In general, one is reluctant to be faraway from one’s path. Therefore, one just goesahead if the other goes out of the way to avoidahead-on collision. In case of overtaking, oneprefers a wide side or follows the other if obsta-cles (or other characters) exist somewhere near.In case a collision occurs while proceeding ina different direction, people pass on the back-side or speed up; otherwise speed down or waitgenerally [2].

Figure 2. The three collision types.

5.5.1. Toward Collisions

The first stage is to determine whether the col-lidee is to the left or right of the agent. Peoplewill prefer to pass on the side with least devi-ation from their path. Observations show thatthe agent has three different ways of avoidingthe collision:

• Changing direction only;

• Changing speed only;

• Changing direction and speed;

If no behaviour has been found that will avoidcollision, the agent simply stops walking. This


will allow the other agent involved in the colli-sion to avoid the subject, who can then resumewalking.

5.5.2. Away Collisions

An away collision is one where the collidee isin front of the agent, but the agent is walkingfaster than the collidee, so it will bump into therear of the collidee. To deal with this situation,the agent has two choices:

• Slow down to the same speed as the collideeand walk behind it.

• Walk faster and overtake the collidee bychoosing the appropriate side.

5.5.3. Glancing Collisions

This type of collision is dealt with in a similarway as with toward collisions.

Each agent has a goal trying to reach it by fol-lowing its initial path. After avoiding collision,the agent should return to its path smoothly inorder to look natural, or change the path com-pletely.

6. System Avoidance Behaviours

As we have seen before, each collision avoid-ance needs different behaviour and differenttreatment. The list of behaviours that can beused in collision avoidance are:

• moving forward,

• changing directions (left or right)

• waiting

• speeding up

• slowing down

• moving back.

Each agent has a priority and several ones couldhave the same priority. These priorities aretaken from the sociology and psychology ofthe human society (the priority is given to oldhumans, the handicapped, pregnant women,etc. . . ) [21].

For each agent apply A* to find its itinerary.

For each frame of animation do

For each agent do

Collision prediction

If not collision then

the agent goes on its way.

Else

Apply the collision

avoidance agent to

agent algorithm.

End if

End for

End for

Figure 3. Overview of the system.

A second level of priority is given to the be-haviours. These are complex phenomena be-cause they depend on the type of collision andthe avoidance collision situations: (two indi-viduals, crossing groups, queuing in an exit,queuing in two directions, crowded environ-ment, etc. . . ). In order to validate our system,we have chosen these situations. Some of themhave been described by Winnie and Serge [22].

The process of collision avoidance is similar forall these situations by applying the above algo-rithm for each pair of agents in collisions. Theprocess is described in Figure 4. The overviewof the system is described in Figure 3.

We can summarize the treatment of collisionavoidance by using the priority rules in the Ta-ble 1. For example, if we are in a toward col-lision, the two agents reach the same cell, thefirst agent goes forward and the second agentmoves to the right. The priority one is given tothis situation.

We pass to the priority two if there is no spacefor the second agent to move, and so on.

If our environment is crowded, we can arriveat a deadlock situation in the actual frame. Sothe two agents have to wait until there is a freespace to move. This situation occurs only whena group of agents are closer to each other.


For each pair of agents do

If toward collisions, then

If agents have the same priorities, then choose one agent in random way

If collision 1, then (two agents reach same cell Figure 1)

- Agent 1 moves forward, agent 2 changes direction to the right if there is space to move

else changes direction to the left

- Agent 1 changes direction to the right, agent 2 moves forward

- Agent 1 changes direction to the left, agent 2 moves forward

- Agent 1 moves forward, agent 2 waits.

Else (collision 2 agents reach the cell of each other Figure 1)

- Agent 1 moves forward, agent 2 change direction to the right if there is space to

move, else changes direction to the left



- Agent 1 waits, agent 2 waits (these agents are blocked)

End if

Else begin with the agent which has the higher priority and process the treatment as in

the same priorities.

End if

Else if away collisions then (agent 2 behind agent 1)

- agent 1 moves forward, agent 2 changes directions right, left to overtake, or slows down

else (glancing collisions)

- Agent 1 moves forward, agent 2 changes direction to the right if there is space to move

else changes direction to the left



- Agent 1 moves forward, agent 2 waits.

End if

End if

End for

Figure 4. Agent to agent collision avoidance algorithm.

6.1. Crossing Groups

This situation is found in a crowded environ-ment, when two groups of agents moving inopposite directions try to avoid each other. Inreal life, they form opposite lines consisting ofpedestrians with the same direction. (Figure5.1)

In case of collision, the agent-to-agent colli-sion avoidance algorithm is applied. The col-lision avoidance behaviours used are: movingforward, changing directions, waiting and mov-

ing back. The last behaviour is used in the caseof blocking when there is no space to move for-ward.

6.2. Bottlenecks (Queuing in an Exit)

Bottlenecks or passing direction of pedestriansis found in applications such as the entrance intocorridors, staircases, subways, or doors. In reallife, the priority is given to the nearest one tothe centre of the bottleneck. The A* algorithmresolves directly this situation and in case of


Collision type Agent1 Agent2 Priority Remarks

forward right 1

forward left 2

Collision1 right forward 3

Toward left forward 4

collision forward wait 5

forward right 1

forward left 2

Collision2 right forward 3

left forward 4

wait wait 5 Deadlock for this frame

Away forward Overtake by right 1

collision Collision2 forward Overtake by left 2

forward slowing down 3

forward right 1

Glancing Collision1 forward left 2

collision right forward 3

left forward 4

forward wait 5

Table 1. Priority rules in collision avoidance.

collision the agent-to-agent collision avoidancealgorithm is applied. The collision avoidancebehaviours used are: moving forward, chang-ing directions, and waiting (Figure 5.2).

6.3. Queuing in Two Directions

Pedestrians form queues in front of exits ordoors of vehicles. When a vehicle arrives,pedestrians wait for the inside passengers to getoff the vehicle and then get on it. This typeof movement can be seen in elevator halls, onplatforms of railway stations, at bus stops, andso on (Figure 5.3).

There will be two types of situations: pedes-trians gather in front of the entrance withoutleaving the way for inside passengers to get offthe vehicle and pedestrians leave the way forinside passengers to get off the vehicle.

In our system a priority is given to the agents ofone direction, the others wait until there is a freespace to move. In case of collision the agent-to-agent collision avoidance algorithm is applied.The collision avoidance behaviours used are:moving forward, changing directions, waiting

and moving back. The last behaviour is usedwhen the passage is blocked; there is no spaceto move. The agents of less priority have tochoose between two possibilities: moving backto leave passage to the other agents or applyingthe A* to find a new path if it is possible.

6.4. Narrow Passage

This situation is observed inside corridors, inpavements, or pedestrian’s passages. Gener-ally, the pedestrians form line segregation, eachgroup takes a direction and the choice of thedirection is taken from the sociological be-haviours of the pedestrian. In almost all so-cieties the pedestrians take their right (right orleft). We have given the priority one to this sit-

Figure 5.1. Crossing groups.


Figure 5.2. Bottlenecks.

Figure 5.3. Queuing in two directions.

Figure 5.4. Narrow passage.

uation. The agent will first check on its right.The A* algorithm provides the path to be fol-lowed by just changing direction to overtake, incase of away collision. (Figure 5.4)

7. System Results and Discussion

The behavioural algorithms described abovehave been implemented in C++ using theOpenGL library. The system is a 3D softwaredesigned to be used in real time, so differentideas and situations can be simulated. A crowdof about 640 agents can be simulated at an ac-ceptable time on a Pentium IV, 3 GHz with 256Mo of main memory.

The user interface has been designed to alloweasy testing: the simulation can be paused atany time, and replayed at any speed, or frameby frame, meaning that a collision can be viewedfrom a variety of angles. The user can create hisvirtual scene in different ways, by placing theobstacles and the agents wherever he wants; Hecan also choose the destination of each agent.The system can simulate any type of collisionsituations.

An environment with agents and obstacles orwithout obstacles, a populated environment, anarrow space to produce a large number of po-tential collisions, agents acting as bottleneck,crossing groups, queuing in twodirections, etc. . .

Figure 5 shows some results of our system. Thecolored cells are the goals of the agents.

Our algorithm is used for all the collision avoid-ance situations, with minor changes in the pri-orities of the agents and the behaviors. We haveseparated them in order to better understand andto compare the results of the system with the reallife Figure 6.

The combination of A∗ with the application ofthe behaviors of collision avoidance gave goodresults, similar to the everyday life. The agentsmoving back could use the A∗ to compute thenew optimum path from the actual position tothe destination target. After avoiding colli-sion, the agent has two choices: to return toits itinerary or compute a new path to its goalfrom that position.

The use of priority rules solved the problem ofdisorder in the crowd movement. The traversedtime depends on several parameters, amongst

Agents Withpriority rules

Withoutpriority rules

1 39 39

2 51 54

3 27 47

. . .

10 49 47

11 37 44

. . .

21 23 24

22 34 36

23 24 28

. . .

30 24 38

31 27 31

. . .

48 15 20

49 26 30

50 34 34

Table 2. Traversed time in seconds in narrow passage.


AgentsWith

priority rulesWithout

priority rules

1 45 55

2 39 59

3 49 60

4 56 50

5 33 43

6 32 34

7 44 59

11 40 54

12 34 55

13 15 18

15 20 35

16 25 25

Table 3. Traversed time in seconds in queuing in twodirections.

other things, the density of crowd and the meth-ods of collision avoidance. We have tested thesystem with these priority rules and withoutthem. And in almost all situations the traversedtime is better with the use of these rules. Table2 and Table 3 show this difference in two situa-tions, for example narrow passage and queuingin two directions. We used in the first simu-lation about 50 individuals and in the secondsimulation about 15 individuals.

The traversed time depends on many parame-ters of the environment: such as density of thecrowd, initial position, obstacles, collision situ-ations and finally the process of collision avoid-ance. We can see in the simulation tables thatsome individuals have the same traversed time(2%) and sometimes have better time withoutpriority rules (1%). But in most situations thetraversed time is better with the use of theserules (97%).

To validate the performance of our system wemust consider the realism of simulation but notthe traversed time.

8. Conclusion and Future Works

This paper has presented useful ideas toward asystem that can simulate human behavior basedon theories from sociology and psychology [21]of a human being. We concentrate our dis-cussion on techniques for collision avoidance

as well as the path finding in dynamic envi-ronment. These two techniques add realismto the simulation. The system can simulate alarge number of agents in real time (about 640agents).The use of priority rules solved the problem ofdisorder in the crowd movement and in mostsituations the traversed time is better with theuse of these rules.

Our model could be used as a framework to sim-ulate real situations such as: the rise and descentof a subway or a bus, the walk in pavement, theentry or the exit of a supermarket. . .

A similar work has been done by Feurtey [6 ]using an agent-to-agent algorithm. He has useda critical density to differentiate the high andthe low density of the crowd. The reaction ofthe agent depends on the distance ranges; close,near, and far. So the agent’s reaction is not thesame according to this range. The second prob-lem is in the way that his virtual environmenthas been represented.

Our agent-to agent model can be used in differ-ent types of crowd; high or low density, just byapplying a small set of rules depending on thecollision types and the priority rules. A secondadvantage of our model resides in the way thatwe have calculated the path of each agent freeof obstacles.

The system implements a number of featuresthat make it more realistic than existing sim-ulations [6],[18]: we have included almost allthe behaviours of avoiding collisions, concen-trating our research on the real life behaviours.We have translated these behaviours to simplecollision avoidance rules.

There are many ideas for future work to improvethe realism of the simulation:

• Improve the individualmodelisation andmove-ment by using professional 3D animationsoftware such as 3DMAX or others.

• Actually, all agents move through the sceneon their own, furtherworkwould allowagentsto move in small groups.

• Currently each agent is assigned a goal tomove toward; a better system would involvethe agent being given a plan rather than asimple goal. For example, an agent could betold to visit all the sites in a museum, get outof the scene etc...


• The notion of groups should improve the re-alism of the simulation by performing groupavoidance rather than individual avoidanceand it will allow modelling both group andindividual behaviours.

• More time would have enabled work on

Figure 6a. Open area with obstacles.

Figure 6b. Narrow passage; the path is shown by lines.

Figure 6c. Queuing in two directions before startinganimation.

Figure 6d. Queuing in two directions: during theanimation.

some other aspects of natural human inter-action. We are interested actually in inte-grating sociological and psychological rulesto improve the realism of the simulation.

References

[1] N. BADLER, C. PHILLIPS, B. WEBBER, SimulatingHumans: Computer Graphics Animation and Con-trol. Oxford University Press, New York, NY, 1993.

[2] S.BAEK, I. K. J. INHO, LeeMotionGenerationUsingMotion Mining System. International Conferenceon Artificial Reality and Telexistence ICAT2003,Japan, 2003.

[3] S. BANDI, D. THALMANN, Space Discretization forEfficient Human Navigation. Computer GraphicsForum, 17(3), pp. 195–206, 1998.

[4] O. B. BAYAZIT, J. M. LIEN, N. M. AMATO, Betterflocking behaviors using rule-based roadmaps. InAlgorithmic Foundations of Robotics V, SpringerTracts in Advanced Robotics 7, Springer-VerlagBerlin Heidelberg, pp. 95–111, 2004.

[5] V. BLUE, J. ADLER, Cellular automata model ofemergent collective bi-directional pedestrian dy-namics. In Artificial Life VI, the Seventh Interna-tional Conference on the Simulation and Synthesisof Living Systems, 2000.

[6] F. FEURTEY, Simulating theCollisionAvoidanceBe-havior of Pedestrians. Master’s thesis, Departmentof Electronic Engineering, University of Tokyo,2000.

[7] M. F. P.GILLIES, N. A. DODGSON, Attention basedobstacle avoidance for animated characters. In Vir-tual Reality. To appear.

[8] D. HELBING, P. MOLNR, Social force model forpedestrian dynamics. Physical Review, 51, pp.4282–4286, 1995.


[9] A. KAMPHUIS, M. H. OVERMARS, Motion planningfor coherent groups of entities. In IEEE Int. Conf. onRobotics and Automation. IEEE Press, San Diego,CA, 2004.

[10] F. LAMARCHE, S. DONIKIAN, Crowd of virtual hu-mans: a new approach for real time navigation incomplex and structured environments. In ComputerGraphics Forum, vol. 23, pp. 509–518, 2004.

[11] C. NIEDERBERGER, D. RADOVIC, M. GROSS,Generic Path Planning for Real-Time Applications,Computer Graphics International (CGI’04), pp.299–306, 2004.

[12] T. Y. LI, H. C. CHOU, Motion planning for a crowdof robots. In International Conference on Roboticsand Automation (ICRA). IEEE Press, San Diego,CA, 2003.

[13] S. R. MUSSE, D. THALMANN, Hierarchical modelfor real time simulation of virtual human crowds.IEEE Transactions on Visualization and ComputerGraphics, 7(2), pp. 152–164, 2001.

[14] J. REIF, H. WANG, Social potential fields: A dis-tributed behavioral control for autonomous robots.In K. Goldberg, D. Halperin, J. C. Latombe, andR. Wilson, editors, International Workshop on Al-gorithmic Foundations of Robotics (WAFR), A. K.Peters, Wellesley, MA, pp. 431–459, 1995.

[15] C. W. REYNOLDS, Flocks, herds, and schools: Adistributed behavioral model. Computer Graphics,21(4), pp. 25–34, 1987.

[16] C. W. REYNOLDS, Steering behaviors for au-tonomous characters. In Game Developers Con-ference, 1999.

[17] S. RUSSELL, P. NORVIG, Artificial Intelligence: AModern Approach. Prentice Hall, 1994.

[18] S. J. RYMILL, N. A. DODGSON, A Psychological-based Simulation of Human Behaviour. Theoryand Practice of Computer Graphics. EG UK, pp.229–236, 2005.

[19] W. SHAO, D. TERZOPOULOS, Autonomous Pedestri-ans. Eurographics / ACM SIGGRAPH Symposiumon Computer Animation. USA. pp. 19–28, 2005.

[20] B. ULICNY, D. THALMANN, Crowd simulation forinteractive virtual environments and vr training sys-tems.

[21] M. WOLFF, Crowd Notes on the behaviour of pedes-trians. In People in Places: The Sociology ofthe Familiar, Birenbaum A., Sagar E., New York:Praeger, pp. 35–48, 1973.

[22] W. DAAMEN, SP. HOOGENDOORN, Experimental re-search on pedestrian walking behavior. In Trans-portation Research Board annual meeting 2003.Washington DC: National Academy Press, pp.1–16, 2003.

Received: May, 2006Revised: May, 2009

Accepted: June, 2009

Contact addresses:

Cherif FoudilLESIA LaboratoryBiskra University

BP 145, RPBiskra

Algeriae-mail: foud [email protected]

Djedi NouredddineLESIA LaboratoryBiskra University

BP 145, RPBiskra

Algeriae-mail: djedi [email protected]

Cedric SanzaVORTEX, IRIT

ToulouseFrance

e-mail: [email protected]

Yves DuthenVORTEX, IRIT

ToulouseFrance

e-mail: [email protected]

DR. CHERIF FOUDIL is currently working as an Associate Professor ofcomputer science at Computer Science Department, Biskra University,Algeria. Dr. Cherif holds PhD degree in computer science. The topicof his doctoral dissertation was Behavioral Animation: simulation ofa crowd of virtual humans. He also possesses B. Sc. (engineer) incomputer science from Constantine University 1985, M. Sc. in com-puter science from Bristol University, UK 1989. He is currently thehead of “animation and artificial life” team in LESIA laboratory. Hiscurrent research interest is in artificial intelligence, artificial life, crowdsimulation, behavioural animation.

DR. DJEDI NOUREDDINE is currently working as a Professor of computerscience at Computer Science Department, Biskra University, Algeria.Dr. Djedi holds Ph. D. degree in computer science. He also possesses B.Sc. (engineer) in computer science from USTHB University 1986. Heis currently the head of LESIA laboratory. His current research interestis in artificial intelligence, artificial life, crowd simulation, behaviouralanimation.

DR. CEDRIC SANZA has been an Assistant Professor in computer graph-ics at the University of Toulouse since 2002. He works in the Vortexteam at the IRIT laboratory. His current research interests includelearning classifier systems, behavioural simulation of characters andanimation of crowds.

PROF. DR. YVES DUTHEN is a Research Professor of artificial life andvirtual reality at the University of Toulouse 1 Capitole, IRIT lab. Hereceived his Ph. D. degree from the University Paul Sabatier in 1983and the “French Habilitation” post-doctorate degree in 1993 to becomefull Professor. He has pioneered research in artificial life for buildingadaptive artificial creatures and focuses now on organism developmentwith an embedded metabolism. He has directed or co-directed about 20PhD theses and has published more than 80 research articles.

path finding and collision avoidance in crowd simulation · keywords:...

Documents