Wheelchair control and navigation based on kinematic model and iris movement

Șolea, Răzvan; Filipescu, Adrian; Minca, Eugénia; Filipescu, Silviu

doi:10.1109/iccis.2015.7274600

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Using the rotation matrix ( ) (1), Equations ( 2) and ( 3), three constraint Equations can be obtained and these Equations are written in the matrix form below (Solea et al, 2015):…”

Section: Kinematics Of the Differential Drive Mobile Robotmentioning

confidence: 99%

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Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Demirbaş¹,

Kalyoncu²

2017

Scitech

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ABSTRACT:In this study, the mathematical model of a nonholonomic vehicle was derived. A PID and kinematic based backstepping controller (KBBC) was designed for a differential drive mobile robot to be able to track a desired trajectory. The KBBC was used to overcome the nonlinearity of the trajectory tracking and the PID controllers was used for the DC motors' speeds adjustments. Responses of the vehicle's controller in the square shaped trajectory had obtained and results were graphically presented. The effectiveness of the designed controller has been discussed.

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“…Using the rotation matrix ( ) (1), Equations ( 2) and ( 3), three constraint Equations can be obtained and these Equations are written in the matrix form below (Solea et al, 2015):…”

Section: Kinematics Of the Differential Drive Mobile Robotmentioning

confidence: 99%

“…For the differentially drive mobile robot, generalized coordinates can be defined as [ ] . Using kinematic Equations ( 9), transformation matrix can be redefined according to the point D (Solea et al, 2015);…”

Section: Dynamics Of the Differential Drive Mobile Robotmentioning

confidence: 99%

Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Demirbaş¹,

Kalyoncu²

2017

Scitech

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“…A wheelchair is a good substitute, for example Reference [44]. It's orientation commands are created by a wheelchair mounted eye movement tracking system [45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

Dirik

Castillo

Kocamaz

2019

ASI

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Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot’s motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye movements supply significant features of nature task to realization. This paper presents an intelligent vision-based gaze guided robot control (GGC) platform that uses a user-computer interface based on gaze tracking enables a user to control the motion of a mobile robot using eyes gaze coordinate as inputs to the system. In this paper, an overhead camera, eyes tracking device, a differential drive mobile robot, vision and interval type-2 fuzzy inference (IT2FIS) tools are utilized. The methodology incorporates two basic behaviors; map generation and go-to-goal behavior. Go-to-goal behavior based on an IT2FIS is more soft and steady progress in data processing with uncertainties to generate better performance. The algorithms are implemented in the indoor environment with the presence of obstacles. Experiments and simulation results indicated that intelligent vision-based gaze guided robot control (GGC) system can be successfully applied and the IT2FIS can successfully make operator intention, modulate speed and direction accordingly.

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“…Vision-based navigation has received intensive attention as an alternative solution to sensor-based navigation [13,14,15,16,17,18,19,20,21], and these methods are further categorized into stereovision-based methods and monocular vision-based ones. The methods using stereovision techniques discern obstacles using three-dimensional depth information.…”

Section: Introductionmentioning

confidence: 99%

Wheelchair Navigation System for Disabled and Elderly People

Kim

2016

Sensors

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An intelligent wheelchair (IW) system is developed in order to support safe mobility for disabled or elderly people with various impairments. The proposed IW offers two main functions: obstacle detection and avoidance, and situation recognition. First, through a combination of a vision sensor and eight ultrasonic ones, it detects diverse obstacles and produces occupancy grid maps (OGMs) that describe environmental information, including the positions and sizes of obstacles, which is then given to the learning-based algorithm. By learning the common patterns among OGMs assigned to the same directions, the IW can automatically find paths to prevent collisions with obstacles. Second, it distinguishes a situation whereby the user is standing on a sidewalk, traffic intersection, or roadway through analyzing the texture and shape of the images, which aids in preventing any accidents that would result in fatal injuries to the user, such as collisions with vehicles. From the experiments that were performed in various environments, we can prove the following: (1) the proposed system can recognize different types of outdoor places with 98.3% accuracy; and (2) it can produce paths that avoid obstacles with 92.0% accuracy.

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Wheelchair control and navigation based on kinematic model and iris movement

Cited by 10 publications

References 4 publications

Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Differential Drive Mobile Robot Trajectory Tracking With Using Pid and Kinematic Based Backstepping Controller

Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic

Wheelchair Navigation System for Disabled and Elderly People

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