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

Bai, Jinqiang; Lian, Shiguo; Liu, Zhaoxiang; Wang, Kai; Liu, Dijun

doi:10.1109/tce.2018.2859629

Cited by 134 publications

(78 citation statements)

References 19 publications

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“…To avoid the fall off and overturn, the vacuum system should provide a suction power that satisfies the condition (5). Moreover, the suction power should be more than enough to provide the necessary pressure difference.…”

Section: Rationale Behind Controlling Adhesionmentioning

confidence: 99%

“…Therefore, robots have been developed to cater to demands in cleaning activities in various domains of building environments, including floor cleaning [4], garden cleaning [5], window cleaning [6], and staircase cleaning [7] with the aid of different cleaning methods, such as vacuuming and wiping [8]. In this regard, nowadays, special attention has to be paid in the development of wall cleaning robots to eliminate the health and safety concerns of human labor involved in the conventional cleaning process of tall vertical structures [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Design and Control of a Wall Cleaning Robot with Adhesion-Awareness

et al. 2020

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Wall cleaning robots are developed to cater to the demands of the building maintenance sector. The ability to climb vertical surfaces is one of the crucial requirements of a wall cleaning robot. Robots that can climb vertical surfaces by adhesion to a surface are preferred since those do not require additional support structures. Vacuum suction mechanisms are widely used in this regard. The suction force acting on the robot due to the negative pressure built up is used by these robots for the adhesion. A robot will fall off or overturn when the pressure difference drops down a certain threshold. In contrast, if the pressure difference becomes too high, the excessive amount of frictional forces will hinder the locomotion ability. Moreover, a wall cleaning robot should be capable of adapting the adhesion force to maintain the symmetry between safe adhesion and reliable locomotion since adhesion forces which are too low or too high hinder the safety of adhesion and reliability of locomotion respectively. Thus, the pressure difference needs to be sustained within a desired range to ensure a robot’s safety and reliability. However, the pressure difference built up by a vacuum system may unpredictably vary due to unexpected variation of air leakages due to irregularities in surfaces. The existing wall cleaning robots that use vacuum suction mechanisms for adhesion are not aware of the adhesion status, or subsequently responding to them. Therefore, this paper proposes a design for a wall cleaning robot that is capable of adapting vacuum power based on the adhesion-awareness to improve safety and reliability. A fuzzy inference system is proposed here to adapt the vacuum power based on the variation of the adhesion and the present power setting of the vacuum. Moreover, an application of fuzzy logic to produce a novel controlling criterion for a wall cleaning robot to ensure safety and reliability of operation is proposed. A fuzzy inference system was used to achieve the control goals, since the exact underlying dynamics of the vacuum-adhesion cannot be mathematically modeled. The design details of the robot are presented with due attention to the proposed control strategy. Experimental results confirmed that the performance of a robot with proposed adhesion-awareness surpasses that of a robot with no adhesion-awareness in the aspects of safety, reliability, and efficiency. The limitations of the work and future design suggestions are also discussed.

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Section: Rationale Behind Controlling Adhesionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Control of a Wall Cleaning Robot with Adhesion-Awareness

et al. 2020

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“…Good accuracy obtained, most for bottles (91.87%), least for waste papers (77.7%). [2]A paper proposed a robot based indoor autonomous trash detection algorithm using Ultrasonic Sensors. The idea here was to eliminate the heavy image processing algorithms for waste classification.…”

Section: Literature Surveymentioning

confidence: 99%

Cost-Effective Autonomous Garbage Collecting Robot System Using Iot And Sensor Fusion

Sengupta*¹,

Varma²,

Kiran³

et al. 2019

IJITEE

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Waste collection and management is a subject undergoing extensive study, and solutions are being proposed meticulously. Thanks to an exponential rise in population, there is an increased production of waste, and also a significant amount of litter consisting of plastic, paper, and other such products carelessly thrown about and scattered in public. Thus, the need for a more robust waste management strategy is essential. Presently, waste management techniques either lack efficiency, or incur high costs. Several Governmental as well as Non-Governmental Organizations have made efforts to clean public spaces. Collection of the unorganized and scattered garbage is the preliminary and most vital step of waste management, following proper segregation and disposal. This paper proposes, explains, and implements an original concept of making a modular, scalable and cost effective system for garbage collection. Making an efficient use of Internet of Things to maintain a constant connection between a central server and a network of garbage processing and collecting, independent, autonomous robots, we rely upon such a system to produce accurate results, as well as considerably reduce the cost, hence providing a feasible solution to minimize human effort and costs during waste collection. It provides a gateway towards implementing garbage collecting robots in smart cities. Rather than describing the design of a single robot, we propose an entire system of robots interconnected in a network, to optimize time, energy and overall speed. There is always a trade-off between accuracy, efficiency and cost of garbage collection, especially when robots get into the picture. Our purpose is to find the perfect balance between these factors.

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“…In order to overcome the inefficiencies of classical methods, heuristic methods are employed for this purpose. Some of the popular heuristic methods used in mobile waste-robots include Artificial Neural Networks (ANN) [6], Fuzzy Logic Control (FLC) [7], or hybrid algorithms [8] etc. Of all these heuristic methods, ANNs are among the most recently-explored.…”

Section: Fig 1 Autonomous Navigation Processmentioning

confidence: 99%

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

Corpuz

2019

IJRTE

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In the field of mobile robotics, path planning is one of the most widely-sought areas of interest due to its nature of complexity, where such issue is also practically evident in the case of mobile robots used for waste disposal purposes. To overcome issues on path planning, researchers have studied various classical and heuristic methods, however, the extent of optimization applicability and accuracy still remain an opportunity for further improvements. This paper presents the exploration of Artificial Neural Networks (ANN) in characterizing the path planning capability of a mobile waste-robot in order to improve navigational accuracy and path tracking time. The author utilized proximity and sound sensors as input vectors, dual H-bridge Direct Current (DC) motors as target vectors, and trained the ANN model using Levenberg-Marquardt (LM) and Scaled Conjugate (SCG) algorithms. Results revealed that LM was significantly more accurate than SCG algorithm in local path planning with Mean Square Error (MSE) values of 1.75966, 2.67946, and 2.04963, and Regression (R) values of 0.995671, 0.991247, and 0.983187 in training, testing, and validation environments, respectively. Furthermore, based on simulation results, LM was also found to be more accurate and faster than SCG with Pearson R correlation coefficients of rx=.975, nx=6, px=0.001 and ry=.987, ny=6, py=0.000 and path tracking time of 8.47s.

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

Cited by 134 publications

References 19 publications

Design and Control of a Wall Cleaning Robot with Adhesion-Awareness

Design and Control of a Wall Cleaning Robot with Adhesion-Awareness

Cost-Effective Autonomous Garbage Collecting Robot System Using Iot And Sensor Fusion

Characterization of a Mobile Waste-Robot: A Heuristic Method to Path Planning using Artificial Neural Networks

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