**2.2 Global planning approach**

Hence, other approaches applied in dynamic environments are proposed with the idea of combining the reactive techniques with global planning methods (Stachniss & Burgard, 2002). At the beginning, a complete plan from present state to goal state is computed. These approaches need prior complete information about the state of the environment, so they do not take into account the uncertainty.

A Reactive Anticipation for Autonomous Robot Navigation 147

provide a safe partial path at any time. During the execution of this partial path chosen, another partial path is developed from the end of the previous path developed. This method is applied at real time in dynamic environments, but its major drawback is that it does not consider the uncertainty of the information collected. So it can produce non-executable

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

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

partial paths.

**2.5 Discussion** 

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

communications between these levels are assured by the agents.

To face this limit, deliberative approaches are appeared. They use a global world model provided by user input or sensory information to generate appropriate actions for the mobile robot to reach the target (Pruski & Rohmer, 1997). This kind of approach enables prediction and reasoning in the decision process by considering the current information and the past information (Nilsson, 1980; Sahota, 1994). The deliberative control architecture comprises three modules: sensing, planning and action modules. First, robot sense it's surrounding and creates a world model of static environment by combining sensory information. Then, it employs planning module to search an optimal path toward the goal and generate a plan for robot to follow. Finally, robot executes the desired actions to reach the target. After a successful action, robot stops and updates information to perform the next motion. Then, it repeats the process until it reaches the goal. It can coordinate multiple goals and constraints within a complex environment (Huq et al., 2008; Yang et al., 2006).

However, in deliberative navigation, accurate model of environment is needed to plan a globally feasible path. The computational complexity of such systems is generally too great to attain the cycle rates needed for the resulting action to keep pace with the changing environments (it is difficult to obtain a completely known map). To perform necessary calculations, enormous processing capabilities and memory is needed.

Therefore, these approaches are not proper in the presence of uncertainty in dynamic or real world.
