**2.4 Multi-agent systems and robotic simulation**

In the field of multi-agent systems, several works have encouraged researchers to develop models simulating robot's behaviours in order to achieve a known target. In fact, there are similarities between robot and agent. So, the mapping from a robot to an agent seems straightforward because each robot represents a physical entity, independent from other robots, with a specific task. Hence, multi-agent simulations models are widely used to solve problems in mobile robotics under dynamic environments. In this way, many approaches are using multi-agent systems to simulate and control autonomous mobile robot in order to resolve the problem of space's conflict by minimising the interaction with agents. For example, (Ros, 2005) proposes a multi-agent system for autonomous robot navigation in unknown environments. In this work, the authors use the case-based reasoning (CBR) techniques in order to solve the problem of local minimum and avoid only static obstacles. In (Ono, 2004), a multi-agent architecture is also proposed to control an intelligent wheelchair. It takes into account only three behaviours: obstacle avoidance (using distance), door passage and wall following. However, it cannot combine behaviours together and it is applied only in static environment. In (Innocenti, 2007), a multi-agent system is proposed as the robot control architecture divided into four sub-systems of agents: perception, behaviour, deliberative and actuator. This architecture is applied in static and dynamic environments and uses the distance between the robot and the moving obstacle in order to predict conflict situations. But this information gives a limited knowledge about the state of the environment and does not allow the robot to take an intelligent decision while navigating in a dynamic and unknown environment. In (Posadas et al., 2008) a multi-agent system is proposed to control the navigation of robot by using a hybrid approach (it uses a motion planning). The architecture is composed of two levels; reactive and deliberative. The communications between these levels are assured by the agents.

### **2.5 Discussion**

Reactive systems are applied in local navigation and used to deal with simple problems. Nevertheless, reactive systems are typically less affected by errors and do not require an explicit models of the environment in order to navigate inside an unknown space. Furthermore, they usually deal only with local information that may be captured at real time. However, reactive systems may fall into local traps or blocking situations because they ignore prediction and reasoning in the decision process. For this reason, the robot can be derived to a conflict situation with other actors sharing the same space. In more complex situations, they are combined according to a higher level in order to include anticipation in the decision process. In these cases, low level control operates in a reactive way (local navigation) whereas high level systems tend to be deliberative and use motion planning method to update the trajectory when there are modifications in the state of the environment. These approaches are called hybrid approaches and are well applied in uncertain and dynamic environments by producing, at each step, a motion planning method

A Reactive Anticipation for Autonomous Robot Navigation 149

Fuzzy Logic Bayesian Network

However, the majority of existing fuzzy logic methods deal only with static environments, and only use the distance between the robot and the obstacle to avoid collision and to minimise conflict with other agents sharing the same space (Bonarini et al, 2003; Selekwa et al., 2008). This kind of information gives a limited knowledge about the state of the environment, so the robot cannot take intelligent decisions. For this reason, in our work, we adopt a fuzzy logic technique to deal with uncertainty. We propose a fuzzy model for autonomous navigation in a dynamic and uncertain environment based on the nature and

5. A set of operation 'op' enabling agents 'A' to collect, produce, consume and manipulate

Basing on this definition, we define our multi-agent system as a set of objects which represent the static objects (have a fixed position) and a set of agents (have a moving position) that represent dynamic objects and the robots. In our model, the principal agent is the "robot". It has its own physical parts including sensors, processors, actuators and communication devices. So, in order to guarantee the safety of the mobile robot, it should be able to navigate by minimising interactions with the other agents navigating in the same

This section is organised as follows. We present in subsection 4.1 the perception model of the robot. It allows the robot to perceive its environment in order to take the suitable decision autonomously. In subsection 4.2, we present the kinematic model of the robot. It allows the robot to localise in its environment. In subsection 4.3, we describe the principle of the fuzzy controller technique. In subsection 4.4, we present our fuzzy controllers model. The first controller is described in subsection 4.4.1. It allows the robot to determine its angular velocity. The second controller is presented in subsection 4.4.2. It allows the robot to calculate its linear velocity. We present in subsection 4.4.3 the system rules applied in our

Complexity Simple rules NP-complex

Reasoning Fuzzy reasoning Probabilistic reasoning

Controller Fuzzy inference Bayesian inference

According to Ferber (Ferber, 1995), a multi-agent system is composed of:

space in order to minimise conflict and to achieve a known goal.

fuzzy controllers used to avoid conflict situations.

Rapidity Fast (simple rules) Slow

the velocity of obstacles.

**4. Proposed approach** 

1. An environment 'E', 2. A set of objects 'O' in 'E', 3. A set of agents 'A' in 'E',

objects of 'O'.

4. A set of relationships 'R' between agents,

Table 1. Comparison between fuzzy logic and Bayesian network.

Application Avoidance collision Localization and mapping

that minimise the conflict (Petti & Fraichard, 2005). Nevertheless, building a movement planning requires an important computation time in order to find the most appropriate way. However, if the frequency of environment's changes is high, the use of a planning method becomes not only inefficient, but also useless (if the robot generates plans without using them).

So, in this situation it is interesting to introduce prediction in reactive schemas in order to consider the environment's evolution in the future. Hence, the main concern of this paper is to propose a model that combines reactivity and anticipation in order to resolve the problem of conflict without using a motion planning. Thus, the robot should anticipate the nature and velocity of the obstacles in order to minimise interactions. We use the multi-agent system to simulate the robot's behaviour because there is a mapping between robots, while navigating in dynamic environments, and multi-agent systems.
