**1. Introduction**

142 Serial and Parallel Robot Manipulators – Kinematics, Dynamics, Control and Optimization

Reinoso, O.; Saltaren, R.; Aracil, R.; Almonacid, M. & Perez, C. (2001). Avances en el

Uicker, J. J.; Denavit, J. & Hartenberg, R. S. (1964). An interactive method for the

*Automática*, ISBN 84-699-4593-9, Barcelona, Spain, September, 2001

Vol.86, pp: 215–221

desarrollo de un robot trepador de estructuras cilíndricas, *XXII Jornadas de* 

displacement analysis of spatial mechanisms. *Journal of Applied Mechanics ASME,*

Nowadays, mobile robots are expected to carry out various tasks in all kinds of application fields ranging from manufacturing plants, transportation, nursing service, resource or underwater exploration. In all these applications, robots should navigate autonomously in uncertain and dynamic environments in order to achieve their goals. So, the most current challenge in the development of autonomous robot control systems is making them respond intelligently to changing environments. Navigation in such environments involves many mechanisms such as: object detection, perception, internal building model, decision making, prediction of the future state of the environment and on-line navigation. To attend its goal safely, the robot should minimise interaction with other actors in order to avoid conflict situations. Generally, this problem comes up when many robots and/or actors would have access to the same space at the same time. In this case, the control of autonomous robotic navigation for conflict resolution has been widely studied. Some researchers have been focused on navigation in dynamic environments, where either reactive systems (producing real time behaviour), deliberative systems (introduce reasoning and need much more time to calculate a suitable decision) or hybrid systems (combine deliberative and reactive approaches) have been used in order to attend a known goal.

Typically, reactive systems are used to deal with simple problems (detect an obstacle, go away from an obstacle, follow a wall, etc.). Nevertheless, reactive systems are typically less affected by errors and do not require an explicit model 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, in reactive systems, the robot can be derived to a conflict situation with other actors because they ignore prediction and reasoning in the decision process. To face this problem, global planning approaches are used. They consist of elaborating a global plan from beginning state to goal state. These approaches need prior complete information about the state of the environment, so they do not take into account the environment uncertainty. So, in more complex situations, hybrid approaches are used. They combine reactive approaches according to a higher level in order to include anticipation of the state of the environment 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. It provides, at each step, a partial moving plan to the robot. But these systems are complex because they need much more time to calculate or to update a suitable trajectory toward a predefined goal. In this situation, it is interesting to introduce prediction

A Reactive Anticipation for Autonomous Robot Navigation 145

 Dynamic Window (Fox et al., 1997) and Curvature Velocity (Simmons, 1996): they search a space of the robot's translational and rotational velocities. Obstacles near the robot are transformed into this space by eliminating all commanded velocities that would cause a collision within a certain time period. Both these methods take into account the kinematics and the dynamics of the robot. Commanded velocities are chosen based on an objective function that considers both progress towards the goal

 Nearness Diagram (Minguez & Montano, 2000): it consists in analysing a situation from two polar diagrams. One diagram is used to extract information of environmental characteristics and identify the immediate goal valley. The second diagram is used to define the safety level between the robot and the obstacles by identifying the closest

 Elastic Bands (Quinlan & Khatib, 1993): this method modifies the trajectory of the robot, originally provided by a planner, by using artificial forces which depend on the layout

 Behaviour-based methods (Jing et al., 2003; Langer et al., 1994): they define fundamental behaviour sets and establish mappings between sensors' information under different situations and reactive behaviours of the mobile robot. The reactive behaviour of the robot can be regarded as a weighted sum of these fundamental behaviours or can be chosen from the behaviour set according to an evaluation

 The Velocity Obstacles method (Fiorini & Shiller, 1998): the moving obstacle is transformed into a velocity obstacle. This approach uses the distance between robot and obstacle to minimise conflict at real time. It is considered as a mapping between sensory data and control commands without introducing reasoning in the decision

The main drawback of these strategies is that the robot gets into an infinite loop or local minimum when it is moving among multiple obstacles or in conflict situations when it shares the same resource with moving obstacles. To overcome this problem, many methods

 Memory state method that uses a state memory strategy (Zhu & Yang, 2004). The variables from which this method makes ultimate decisions are: "the distance between the robot and the target" and "the distance between the robot and the nearest obstacle". Minimum risk approach is based on trial-return behaviour phenomenon (Omid et al.,

 Virtual wall method directs the robot away from the dead-end by placing a virtual wall that bars the limit cycle path (Ordonez et al., 2008). However, these strategies do not take into account the dynamic nature of the environment and the uncertainty of

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

perception. So the robot can be driven into dangerous or conflict situations.

and robot safety.

of the obstacles in the way.

one.

function.

process.

are proposed. We mention:

2009; Wang & Liu, 2005).

**2.2 Global planning approach** 

not take into account the uncertainty.

in reactive schemas, without using planning techniques, in order to consider the environment's evolution in the future.

To deal with uncertainty, autonomous navigation involves using systems for navigation control that must be not too computationally expensive, as this would result in a sluggish response. In this case, we choose the fuzzy logic technique because it is faster when the frequency of the environment's changes is high.

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 (Posadas et al., 2008). They are more flexible and fault tolerant as several simple agents are easier to handle and cheaper to build. Indeed, the term "agent" has been defined as hardware or a software system with certain properties such as autonomy, social ability, reactivity and pro-activity (Ferber, 1995). In fact, there are similarities between robot and agent because they share the same characteristics. 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.

In this paper, we present a reactive anticipation model based on fuzzy logic that takes into account: (1) the reactive navigation in an environment composed of local minimum and moving obstacles, and (2) the anticipation of blocking situations and the prediction of the nature, the velocity and the position of obstacles in the future. This information is used to predict conflict situations without using a motion planning method. In order to validate our work, we evaluate our approach by simulating various scenarios. We also give a comparative study between results obtained by our model and those of some other approaches.

In the remainder of the article, we give, in section 2, an overview of the existing approaches for conflict resolution applied to autonomous robot navigation. In section 3, we present the most used techniques to deal with the uncertainty of perception and we justify our choice of the fuzzy logic method. In section 4, we describe our model that combines reactivity with anticipation in order to deal with the problem of conflict resolution without using a motion planning. In Section 5, we present our experimental results. Finally, we conclude in section 6 and we give perspectives for this work.
