Electric wheelchair is one of the many engines used for the movement of aged and disabled people. This paper introduces an obstacle avoidance using deformable virtual zone (DVZ), particle filter to improve localization and fuzzy controller to join desired target. This controller is developed to increase the independence of disabled and aged people, specifically those who suffer not only disability in the lower limbs but also visual disturbances. To overcome these problems, different perceptive abilities or sensors were introduced. In this context, we developed a control system composed by fuzzy controller to join a target, DVZ method for obstacle avoidance, and particle filter for localization. Also, we present the simulation results of the wheelchair navigation system. 1. Introduction Several works have been developed in this context. The publication of Lapierre et al. [1] threatened the DVZ method combined with the path followed, and the work of Gil Pinto et al. [2, 3] provides an active approach to planning based on the DVZ method. This control strategy is based on the GPS sensor and ultrasonic sensors for generating an optimal trajectory to ensure cooperation between nonholonomic robots in an unknown environment. The particulate filter is a probabilistic method allowing the multisensor data fusion and localization. The coordination of these strategies results in an automatic obstacle avoidance navigation to reach a target point [4, 5]. This paper presents control system based on the DVZ method for obstacle avoidance. The principle of this method is to define the robot/environment interaction as a risk zone surrounding the wheelchair (Figure 2). The DVZ characterizes the deformable zone geometry and depends on the wheelchair speed. When the risk zone is disturbed by an obstacle, it can be reformed by modifying the velocities of the wheelchair [1, 6]. In addition we will detail the use of the particle filter method for localization. This method uses a probabilistic approach [4, 7]. It generates a set of possible positions in each sampling period. This data fusion method gives the estimated position of the chair using distance between wheelchair and obstacles. Also, we use a fuzzy controller already developed in previous work that allows the wheelchair to reach the desired position [8, 9]. In the following we will detail the used methods and the simulation of the control system. 2. Using Fuzzy Controller for Electric Wheelchair to Join Target In this paper the fuzzy controller permits reaching the target position from any position . The considered
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