Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment

Shang, Wanfeng; Cao, Xiangang; Ma, Hongwei; Zang, Hailong; Wei, Pengkang

doi:10.1155/2016/8252015

Cited by 12 publications

(13 citation statements)

References 18 publications

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“…A coal mine rescue robot (CMRR) can bring an appropriate amount of emergency supplies and various sensors to the accident scene, and the rescue command center can then formulate an efficient rescue strategy based on the information provided by the robot; this guarantees the smooth progress of various rescue operations as well as possible. The application of CMRRs not only reduces the threat to rescue worker safety but also improves the rescue efficiency [8], [9]. At present, most CMRRs are in the experimental research stage, and only a few CMRRs are involved in mine rescue.…”

Section: Introductionmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described. INDEX TERMS Coal mine rescue robots, binocular vision, camera calibration, stereo vision matching.

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Section: Introductionmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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“…For example, the work presented in [7] controls the motion of a robot by vision. In [22] the payload is managed, while in [19] a robot is operated by using a remote connection for rescue situations. In other cases, the recognition system can be used to allow a car driver to interact with the radio [10], or to be able to answer phone calls while driving [11].…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller

Mantecón

del-Blanco

Jaureguizar

et al. 2016

Lecture Notes in Computer Science

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Hand gestures are one of the main alternatives for Human-Computer Interaction. For this reason, a hand gesture recognition system using near-infrared imagery acquired by a Leap Motion sensor is proposed. The recognition system directly characterizes the hand gesture by computing a global image descriptor, called Depth Spatiograms of Quantized Patterns, without any hand segmentation stage. To deal with the high dimensionality of the image descriptor, a Compressive Sensing framework is applied, obtaining a manageable image feature vector that almost preserves the original information. Finally, the resulting reduced image descriptors are analyzed by a set of Support Vectors Machines to identify the performed gesture independently of the precise hand location in the image. Promising results have been achieved using a new hand-based near-infrared database.

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“…Kinect is a motion detection and recognition smart sensor which allows human/computer interaction without the need of any physical controllers [3]. Indeed, Kinect sensor is a motion sensor that provides a natural user interface available for several applications in different fields including gamebased learning systems [4,5], stroke rehabilitation [6,7], helping visually impaired people [8,9], navigation systems [10][11][12], and other fields [13,14]. Based on its internal processor, Kinect sensor can recognize human movement patterns to generate a corresponding skeleton coordinates that can be provided in a computer environment such as Matlab [15,16] and LabVIEW [17,18].…”

Section: Introductionmentioning

confidence: 99%

Kinect-Based Sliding Mode Control for Lynxmotion Robotic Arm

Abdallah

Bouteraa

Rekik

2016

Advances in Human-Computer Interaction

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Recently, the technological development of manipulator robot increases very quickly and provides a positive impact to human life. The implementation of the manipulator robot technology offers more efficiency and high performance for several human’s tasks. In reality, efforts published in this context are focused on implementing control algorithms with already preprogrammed desired trajectories (passive robots case) or trajectory generation based on feedback sensors (active robots case). However, gesture based control robot can be considered as another channel of system control which is not widely discussed. This paper focuses on a Kinect-based real-time interactive control system implementation. Based on LabVIEW integrated development environment (IDE), a developed human-machine-interface (HMI) allows user to control in real time a Lynxmotion robotic arm. The Kinect software development kit (SDK) provides a tool to keep track of human body skeleton and abstract it into 3-dimensional coordinates. Therefore, the Kinect sensor is integrated into our control system to detect the different user joints coordinates. The Lynxmotion dynamic has been implemented in a real-time sliding mode control algorithm. The experimental results are carried out to test the effectiveness of the system, and the results verify the tracking ability, stability, and robustness.

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Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment

Cited by 12 publications

References 18 publications

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Hand Gesture Recognition Using Infrared Imagery Provided by Leap Motion Controller

Kinect-Based Sliding Mode Control for Lynxmotion Robotic Arm

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