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This paper presents a study that focuses on sidewalk following problem of an autonomous wheelchair. The main goal is to propose a solution to the urban mobility problem of people with walking disabilities. The study offers an efficient control system design for this task. A linearized wheelchair model is constructed and image-based visual servoing is introduced to evaluate the performance of tracking yellow tactile pavement on sidewalk with optimal control. Reference trajectory sets are created using robust vanishing point for sidewalk following by employing the Hough Lines method. These reference paths are tested with two control methods of Linear Quadratic Regulator (LQR) control and Pole Placement (PP) control. Both control methods are applied through simulation on the autonomous wheelchair model, and efficacy of sidewalk following under these control methods is discussed comparatively. Disturbance attenuation results of the given optimal control methods and simulation outputs prove the efficacy of the model and the designed control systems. LQR control method proves to have better performance in system response in comparison to PP control method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]