A robust obstacle detection method for robotic vacuum cleaners

Kang, Mun-Cheon; Kim, Kwang-Shik; Noh, DongKi; Han, Jong-Woo; Ko, Sung-Jea

doi:10.1109/tce.2014.7027291

Cited by 33 publications

(12 citation statements)

References 16 publications

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“…With the extended application of robots in recent years, many researchers have attempted to develop robots for cleaning regional environments [ 19 , 20 , 21 , 22 ]. However, those designed to clear garbage from water surfaces require further development [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

Chang

Hsu

Hung

et al. 2021

Sensors

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Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective.

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Section: Introductionmentioning

confidence: 99%

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

Chang

Hsu

Hung

et al. 2021

Sensors

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“…Wheeled mobile robot (WMR) has been utilized in many fields such as the automated guided vehicle (AGV) [ 1 , 2 ], robotic cleaner [ 3 ], exploration robot [ 4 , 5 ] recently. The WMR has the capability to respond flexibility by itself in various complex unknown environments.…”

Section: Introductionmentioning

confidence: 99%

A Sensor Fusion Based Nonholonomic Wheeled Mobile Robot for Tracking Control

Tsai

Kao

Lin

et al. 2020

Sensors

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In this paper, a detail design procedure of the real-time trajectory tracking for the nonholonomic wheeled mobile robot (NWMR) is proposed. A 9-axis micro electro-mechanical systems (MEMS) inertial measurement unit (IMU) sensor is used to measure the posture of the NWMR, the position information of NWMR and the hand-held device are acquired by global positioning system (GPS) and then transmit via radio frequency (RF) module. In addition, in order to avoid the gimbal lock produced by the posture computation from Euler angles, the quaternion is utilized to compute the posture of the NWMR. Furthermore, the Kalman filter is used to filter out the readout noise of the GPS and calculate the position of NWMR and then track the object. The simulation results show the posture error between the NWMR and the hand-held device can converge to zero after 3.928 seconds for the dynamic tracking. Lastly, the experimental results show the validation and feasibility of the proposed results.

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“…ANUAL garbage pickup and cleaning is a tedious, boring and repetitive task [1], and autonomous robot can be a potential candidate for this application. The autonomous floor-cleaning [2], aquatic-cleaning [3] [4], wall-cleaning [5] and rubbish collecting [6] robots have been developed for years, while autonomous cleaning robot that can operate on the grass still remains a challenging task due to the lack of garbage recognition ability.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass

Bai

Lian²,

Liu³

et al. 2018

IEEE Trans. Consumer Electron.

129

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This paper presents a novel garbage pickup robot which operates on the grass. The robot is able to detect the garbage accurately and autonomously by using a deep neural network for garbage recognition. In addition, with the ground segmentation using a deep neural network, a novel navigation strategy is proposed to guide the robot to move around. With the garbage recognition and automatic navigation functions, the robot can clean garbage on the ground in places like parks or schools efficiently and autonomously. Experimental results show that the garbage recognition accuracy can reach as high as 95%, and even without path planning, the navigation strategy can reach almost the same cleaning efficiency with traditional methods. Thus, the proposed robot can serve as a good assistance to relieve dustman's physical labor on garbage cleaning tasks. He has author over 80 refereed international journal papers covering topics of artificial intelligence, multimedia communication, and human computer interface. He has authored and co-edited over 10 books, and held over 50 patents.Dr. Lian is on the editor board of several refereed international journals.Zhaoxiang Liu received the B.S. degree and Ph.D. degree from the College of Information and Electrical Engineering,

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A robust obstacle detection method for robotic vacuum cleaners

Cited by 33 publications

References 16 publications

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

Autonomous Water Quality Monitoring and Water Surface Cleaning for Unmanned Surface Vehicle

A Sensor Fusion Based Nonholonomic Wheeled Mobile Robot for Tracking Control

Deep Learning Based Robot for Automatically Picking Up Garbage on the Grass

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