Vision-Based Navigation of Omnidirectional Mobile Robots

Ferro, Marco; Paolillo, Antonio; Cherubini, Andrea; Vendittelli, Marilena

doi:10.1109/lra.2019.2913077

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…Another obstacle-avoidance algorithm based on the data from a camera, motor encoders, and an inertial measurement unit was proposed. 14 The visual servo controller and velocity estimation algorithm were fused to generate obstacle-free path. The effectiveness of the proposed algorithm was tested for both wheeled and humanoid robots.…”

Section: Image Processing-based Obstacle Identificationmentioning

confidence: 99%

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

Singh

Bera

Chatti³

2022

SIMULATION

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In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.

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Section: Image Processing-based Obstacle Identificationmentioning

confidence: 99%

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

Singh

Bera

Chatti³

2022

SIMULATION

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“…These senses have also been used for robots, especially for visual and tactile perception. Robot vision is a fast-advancing field that enables robots to obtain vision information (including size, shape, color, and brightness) for various tasks, such as Visual Simultaneous Localization and Mapping (VSLAM) [5], [6], visual servo grasping [7], and visual navigation [8], [9]. Robot tactile perception is indispensable for tasks such as stable grasping [10], item classification [11], [12], and contact force control [13], [14], using interaction information of contact texture, object weight, material compliance and interface temperature.…”

Section: Introductionmentioning

confidence: 99%

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Miao

Zhou

et al. 2022

IEEE Trans. Med. Robot. Bionics

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Humanoid robots are designed and expected to resemble humans in structure and behavior, showing increasing application potentials in various fields. Like their biological counterparts, their environmental perception ability is fundamental. In particular, the visual and tactile perception are the two main sensory modes that humanoids use to understand and interact with the environment. Vision-Tactile Fusion Perception (VTFP) has shown multiple possibilities for better sensing understanding in challenging conditions, causing new research interests and questions. The overlap between visual and tactile perception in humanoids is continually growing. This work has reviewed the current state of the art of VTFP. It starts with the physiological basis of biological vision and tactile systems as well as the VTFP mechanisms as inspirations for humanoid perception. Then, the bioinspired visual-tactile fusion systems for humanoids are reviewed as the emphasis. After the survey on the vision and tactile sensors of robots, seven currently publicly available VTFP datasets are introduced. They are the data sources for several studies on neural network-inspired fusion algorithms. Furthermore, the applications of VTFP on humanoids are summarized. Finally, the challenges and future work are discussed. This review aims to provide several references for further exploitation of VTFP and its applications on humanoids.

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“…Non-linear control of omnidirectional four-wheel mobile robots is an open topic which is still attracting much research interest [22][23][24][25]. Non-linear control of proven global stability is a challenging objective which can improve the performance of omnidirectional four-wheel mobile robots, in terms of precise path tracking and operational capacity [18,[26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Non-linear optimal control for four-wheel omnidirectional mobile robots

Rigatos

Busawon

Abbaszadeh

et al. 2020

Cyber-Physical Systems

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The article proposes a non-linear optimal control approach for four-wheel omnidirectional mobile robots. The method has been successfully tested so-far on the control problem of several types of autonomous ground vehicles and the present article shows that it can also provide the only optimal solution to the control problem of four-wheel omnidirectional robotic vehicles. To implement this control scheme, the state-space model of the robotic vehicle undergoes first approximate linearisation around a temporary operating point, through first-order Taylor series expansion and through the computation of the associated Jacobian matrices. To select the feedback gains of the H-infinity controller an algebraic Riccati equation is repetitively solved at each time-step of the control method. The global stability properties of the control loop are proven through Lyapunov analysis. Finally, to implement state estimation-based feedback control, the H-infinity Kalman Filter is used as a robust state estimator.

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Vision-Based Navigation of Omnidirectional Mobile Robots

Cited by 17 publications

References 18 publications

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

Review of Bioinspired Vision-Tactile Fusion Perception (VTFP): From Humans to Humanoids

Non-linear optimal control for four-wheel omnidirectional mobile robots

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