Mud and dirt separation method for floor cleaning robot

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96% detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.Sensors 2020, 20, 1698 2 of 20 vision-based techniques are widely used in cleaning robots for recognizing the litter and compute the cleaning action [14][15][16][17][18][19]. Andersen et al., built up a visual cleaning map for cleaning robots using a vision algorithm and a powerful light-transmitting diode. The sensor recognizes the grimy region and generates the dirt map by examining the surface pictures pixel-by-pixel utilizing the multi-variable statistical method [15]. David et al., proposed high-level manipulation actions for cleaning dirt from table surfaces using REEM a humanoid service robot. The author uses a background subtraction algorithm for recognizing the dirt from the table and Noisy Indeterministic Deictic (NID) rules-based learning algorithm to generate the sequence of cleaning action [16]. Ariyan et al., developed a planning algorithm for the removal of stains from non-planar surfaces where the author uses a depth-first branch-and-bound search to generate cleaning trajectories with the K-means clustering algorithm [17]. Hass et al., demonstrated the use of unsupervised clustering algorithm and Markov Decision Problem (MDP) for performing the cleaning task where unsupervised clustering algorithm is used to distinguish the dirt from surface and MDP algorithm is used to generate the maps, and transition model from clustered image is used to describe the robot cleaning action [18]. Nonetheless, these approaches have some practical issues and disadvantages for using in food court table cleaning; the detection ratio relies heavily on the textured surfaces, which makes it challenging to identify the litter type as solid...

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“…Floor cleaning: Dirt and mud detection [38] Spectral residual filter + Maximally Stable Extremal Regions 80.12…”

Section: Case Study Algorithm Detection Accuracymentioning

confidence: 99%

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Yin

Apuroop

Tamilselvam

et al. 2020

Sensors

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“…Images taken from the sensors, through their visible spectral bands, are used to detect mud. Milinda et al propose a new algorithm for detecting types of dirt on textured surfaces using camera images for domestic robotics [36]. consider guided ultrasonic waves with post-processing techniques by FE and FS by PCA and NLPCA in a case study based on the SHM online of scarf repairs in composite laminates [40].…”

Section: State Of the Artmentioning

confidence: 99%

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Jiménez

Muñoz

Márquez

2019

Reliability Engineering & System Safety

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“…Dirt detection using visual sensors is proposed in [2] and [3]. Similarly, mud and dirt separation has been proposed in [4]. Some researchers have focused on particular environments in which cleaning robots will be used.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Algorithm for Cleaning Robots Using Vision Sensors

Ravankar

Watanabe

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

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In recent years, cleaning robots like Roomba have gained popularity. These cleaning robots have limited battery power, and therefore, efficient cleaning is important. Efforts are being undertaken to improve the efficiency of cleaning robots. Most of the previous works have used on-robot cameras, developed dirt detection sensors which are mounted on the cleaning robot, or built a map of the environment to clean periodically. However, a critical limitation of all the previous works is that robots cannot know if the floor is clean or not unless they actually visit that place. Hence, timely information is not available if the room needs to be cleaned. To overcome such limitations, we propose a novel approach that uses external cameras, which can communicate with the robots. The external cameras are fixed in the room and detect if the floor is untidy or not through image processing. The external camera detects if the floor is untidy, along with the exact areas, and coordinates of the portions of the floor that must be cleaned. This information is communicated to the cleaning robot through a wireless network. Thus, cleaning robots have access to a `bird’s-eye view’ of the environment for efficient cleaning. In this paper, we demonstrate the dirt detection using external camera and communication with robot in actual scenarios.

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Mud and dirt separation method for floor cleaning robot

Cited by 15 publications

References 8 publications

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

An Efficient Algorithm for Cleaning Robots Using Vision Sensors

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