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This paper presents mathematical modeling and simulation results of a wheelchair system with motion control, which constitutes a crucial part of a research project on vision-based smart wheelchair control and navigation. Disabled people with neuromuscular or reflex disabilities have difficulties in operating standard electric wheelchairs properly. In addition to, driving wheelchair in outdoor has critical risks. The work will contribute greatly mobility problem of disabled people by visual information technology and efficient motion control design simulation. To reach this purpose, the mathematical model of wheelchair is constructed that shows main characteristics and dynamical behavior of system. Results of the study is observed by a designed computer simulation model. Simulation test outcomes provide developing control algorithm that well organized motor control. Computer simulation results include controlling the position and direction of wheelchair, linear displacement, velocity information and disturbance effect on driving. Consequencely, the mathematical model has the ability to show the representing autonomous wheelchair dynamics in open-loop and closed-loop and simulation outputs prove effeciency of the model and designed control system. Results of the study will be used in the research project that aims to solve the autonomous wheelchair control problem.

In this paper, we design and implement a human–machine interaction application, which enables a visually challenged person to locate and manipulate personal objects in her/his neighborhood. In this setting, we need to develop a tool (embedded in a mobile phone) which is capable of sensing, computing, and guiding the human arm toward the object. This involves solving the following two subproblems: (1) recognition of objects in the input images, and (2) generating control signals, to guide the human for navigation, to reach the desired destination. For the former subproblem, we adapt the bag-of-words framework for recognition and matching on mobile phones. For the latter subproblem, we have developed a moment-based human servoing algorithm which is able to generate commands that help the visually impaired human to localize his hand with respect to the object of interest. All necessary computations take place on the mobile phone. The proposed object recognition and vision-based control design are deployed on a low-/mid-end mobile phone. This can lead to a wide range of applications. With our proposed design and implementation, we demonstrate that our application is effective and accurate, with a high reliability of convergence for different experimental settings.

In this paper, we present a novel sequential visual localization algorithm in partitioned route. The algorithm utilizes Monte-Carlo for accurate visual localization. Partitioning the route into several regions produces higher accuracy along with lower computational cost. Each of the regions is represented using independent maps. We use bag-of-words for visual mapping of the environment. In addition, it shows smooth transition when the robot moves from one region to another. Experiments are carried out using data collected from crowded roads. Results show that this method is superior to previous attempts that looked into localization in crowded outdoor environments.