A Reactive Type-2 Fuzzy Logic Control Architecture for Mobile Robot Navigation





Mouloud Ider

Electrical Engineering Department, LTII Laboratory, A/Mira University, Targa Ouzemour Street,

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Boubekeur Mendil

Electrical Engineering Department, LTII Laboratory, A/Mira University, Targa Ouzemour Street,

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Abstract—This paper presents a type-2 fuzzy reactive architecture for mobile robot navigation in cluttered environments. The proposed control scheme allows to the robot to avoid obstacles and to reach the target. Robot control actions are generated by  different  behaviors: attraction to the  target, obstacle avoidance and fusion block.


Since the robot evolve in unstructured and unknown environments and they need to cope with large amounts of uncertainties, fuzzy logic, especially type-2 fuzzy logic, seems to be the most convenient solution to design different parts of robot control system. Simulation results show the effectiveness and the robustness of the proposed architecture.


Keywords-Behavioral control architecture; mobile robot navigation; type-2 fuzzy logic; uncertainty and modeling




Nowadays, mobile robots are used in many industrial situations such as transport, security or cleaning tasks. However, their design requires many engineering and science disciplines, from mechanical, electrical and electronics engineering to artificial vision, computer and cognitive.

The control of mobile robot navigation in cluttered environments  is   a   fundamental   problem   that   has   been receiving a large amount of attention. The main issue in this

field   is   how   to   obtain   accurate,   flexible   and   reliable navigation? One part of the literature in this domain considers that the robot is fully actuated with no control bound and

focuses the attention on path planning. Voronoï diagrams and

visibility graphs [1] or navigation functions [2] are among these roadmap-based methods. However, the other part of the literature  considers  that  to  control  a  robot  with  safety, flexibility and reliability, it is essential to accurately take into account:  robot’s  structural  constraints (e.g.,  nonholonomy); avoid command discontinuities and set-point jerk, etc. Nevertheless, even in this method, there are two schools of thought, one uses the notion of planning and re-planning to reach the target, e.g., [3] and [4] and the other more reactive (without planning) like in [5], [6] or [7].


Our proposed control architecture is linked to this last approach. Therefore, where the stability of robot control is rigorously demonstrated and the overall robot behavior is constructed  with  modular and  bottom-up approach [8].  To guarantee multi-objective criteria, control architectures can be elaborated in a modular and bottom-up way as introduced in [9]   and   so-called   behavioral   architectures   [8].   These techniques are based on the concept that a robot can achieve a complex  global  task  while  using  only  the  coordination of several   elementary   behaviors.   In   fact,   to   tackle   this complexity, behavioral control architecture decompose the global controller into a set of elementary behavior/controller (e.g., attraction to the objective, obstacle avoidance, trajectory following, etc.) to master better the overall robot behavior. In this kind of control, it exists two major principles for behavior coordination: action selection and fusion of actions which lead respectively to competitive and cooperative architectures of control. In competitive architectures (action selection), the set- points sent to the robot actuators at each sample time are given by a unique behavior which has been selected among a set of active behaviors. The principle of competition can be defined by a set of fixed priorities like in the subsumption architecture [9] where a hierarchy is defined between the behaviors. The action selection can also be dynamic without any hierarchy between behaviors [10], [11]. In cooperative architectures (fusion of actions), the set-points sent to the robot actuators are the result of a compromise or a fusion between controls generated by different active behaviors. These mechanisms include fuzzy control [12] via the process of defuzzification, or the multi-objective techniques to merge the controls [13].


Our  work deals  with the  problem of  robots evolving in unstructured and  unknown environments that need to cope with large amounts of uncertainties. Many stochastic techniques, such as Kalman filtering [14], are used to get the best  estimate  of  the  measured  variables,  especially, in  the presence of sensor noise. However, in this work, we use the powerful of fuzzy logic to design the different parts of the robot navigation system.

Fuzzy  logic  is  an  adequate  methodology  for  designing robust   controllers   that   are   able   to   deliver   satisfactory


performance in the presence of disturbances and uncertainties.     

The general framework of fuzzy reasoning allows handling much of the uncertainty, using fuzzy sets characterized by type-1 membership functions. However, in many situations, the   designer   has   no   information   about   the   adequate membership function shapes. Thus, the use of type-2 fuzzy sets becomes natural [15], [16]. Type-2 fuzzy logic is  a  more general formulation using fuzzy membership functions with additional dimension [17]. This provides additional degree of freedom to handle uncertainties and the lack of information.

In this work, fuzzy logic is used to design the controllers for the basic tasks (behaviors) for robot navigation: attraction to the target and obstacles avoidance. However, the coordination and the fusion of the elementary behaviors are more difficult, since, we have no valuable online information to safely avoid obstacles and  to  guarantee the  optimal convergence to  the target. Hence, type-2 fuzzy logic is used to design the fusion controller, to compensate our lack of information. This is the core stone of our design. The results are compared to those of type-1 fuzzy logic controller.

 The remainder of the paper is organized as follows. In the next section (II), the proposed control architecture is given.

Section III details the design of the different control parts (attraction to a the target, obstacle avoidance and fusion controller). Section IV discusses simulations results. Finally, section V concludes this paper.



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