scholarly journals An Autonomous Robot-Aided Auditing Scheme for Floor Cleaning

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4332
Author(s):  
Thejus Pathmakumar ◽  
Manivannan Kalimuthu ◽  
Mohan Rajesh Elara ◽  
Balakrishnan Ramalingam
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Density Estimation ◽  
Research Work ◽  
Kernel Density ◽  
Fundamental Question ◽  
Outdoor Environments ◽  
Audit Score ◽  
Density Map

Cleaning is an important factor in most aspects of our day-to-day life. This research work brings a solution to the fundamental question of “How clean is clean” by introducing a novel framework for auditing the cleanliness of built infrastructure using mobile robots. The proposed system presents a strategy for assessing the quality of cleaning in a given area and a novel exploration strategy that facilitates the auditing in a given location by a mobile robot. An audit sensor that works by the “touch and inspect” analogy that assigns an audit score corresponds to its area of inspection has been developed. A vision-based dirt-probability-driven exploration is proposed to empower a mobile robot with an audit sensor on-board to perform auditing tasks effectively. The quality of cleaning is quantified using a dirt density map representing location-wise audit scores, dirt distribution pattern obtained by kernel density estimation, and cleaning benchmark score representing the extent of cleanliness. The framework is realized in an in-house developed audit robot to perform the cleaning audit in indoor and semi-outdoor environments. The proposed method is validated by experiment trials to estimate the cleanliness in five different locations using the developed audit sensor and dirt-probability-driven exploration.

The Disturbance of Inductive Proximity Sensor for Mobile Robot

2014 ◽  
Vol 1079-1080 ◽  
pp. 909-912 ◽  
Author(s):  
Tsing Tshih Tsung ◽  
Thi Khanh Tang ◽  
Nguyen Hoai
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Ferrous Metal ◽  
Sheet Thickness ◽  
Negative Effects ◽  
Proximity Sensor ◽  
Position Detection ◽  
Set Up ◽  
Metal Tape

Non-contactingproximity sensors are widely promoted for position detection through determiningthe distance between sensor and object. Besides, the usage of non-contactinginductive proximity sensors for object detections such as finding non-ferrousand ferrous metal tape is the popular technique in mobile robots. Most of thetechnology uses simple HF- oscillation principle as an inductive proximitysensor (IPS) with a decrease in the quality of the oscillator circuit’selectromagnetic to find the tape. By applying this technique, the externalfactors may cause negative effects to systemperformance. To overcome this situation, we set up a hand measurement withinductive proximity sensors and two tapes, meanwhile main tape and disturbingtape are separated by an obstruction sheet. After measuring, dataresults are used to analyze the influence of the obstruction sheet thickness anddisturbance tape to the noise in received signals. The research isthe fundament for further applications, based on inductive proximity sensor formobile robot that could be more robust against noises and disturbances.

scholarly journals A Reinforcement Learning Based Dirt-Exploration for Cleaning-Auditing Robot

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8331
Author(s):  
Thejus Pathmakumar ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Balakrishnan Ramalingam
Keyword(s):  
Reinforcement Learning ◽  
Research Work ◽  
Leading Edge ◽  
Research Problem ◽  
Fundamental Question ◽  
Policy Learning ◽  
Policy Network ◽  
Important Research ◽  
Important Research Problem

Cleaning is one of the fundamental tasks with prime importance given in our day-to-day life. Moreover, the importance of cleaning drives the research efforts towards bringing leading edge technologies, including robotics, into the cleaning domain. However, an effective method to assess the quality of cleaning is an equally important research problem to be addressed. The primary footstep towards addressing the fundamental question of “How clean is clean” is addressed using an autonomous cleaning-auditing robot that audits the cleanliness of a given area. This research work focuses on a novel reinforcement learning-based experience-driven dirt exploration strategy for a cleaning-auditing robot. The proposed approach uses proximal policy approximation (PPO) based on-policy learning method to generate waypoints and sampling decisions to explore the probable dirt accumulation regions in a given area. The policy network is trained in multiple environments with simulated dirt patterns. Experiment trials have been conducted to validate the trained policy in both simulated and real-world environments using an in-house developed cleaning audit robot called BELUGA.

scholarly journals Dengue Vector Density Incident and Its Implication to Urban Livability

2020 ◽  
Author(s):  
Nuriah Abd Majid ◽  
Muhammad Rizal Razman ◽  
Sharifah Zarina Syed Zakaria ◽  
Nurafiqah Muhamad Nazi
Keyword(s):  
Quality Of Life ◽  
Urban Area ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Urban Areas ◽  
Management Strategies ◽  
Kernel Density ◽  
Kuala Lumpur ◽  
Future Management

Abstract Background: Malaysia's population is set to reach 33.10 million by the end of 2020. About 75% of the population of Malaysia lived in urban areas and cities. The metropolitan area of Greater Kuala Lumpur had a population of more than seven million that year, making it the largest urban area in Malaysia. Kuala Lumpur as the city centre for Greater Kuala Lumpur has been ranked as Southeast Asia's second most liveable city after Singapore. The livable city imperative is relevant because Malaysia's urbanization process is moving towards harmonization with the principles of sustainable development. Livable city involves many interdependent factors contributing to the urban quality of life. With their complete physical and social infrastructures, the urban types are an essential basis for improving the quality of life of the urbanites. However, increasing population and rapid land-use changes led to the emergence of vector-borne diseases such as dengue in an urban area. Prolong dengue outbreaks will reduce livability in urban areas. Therefore, this study aims to look at the density of dengue distribution in Bandar Baru Bangi town in 2014, 2015, 2016 and 2017.Methods: The study uses data provided from the Ministry of Health Malaysia and shows the focus of dengue cases in residential and industrial areas of Bandar Baru Bangi town. Spatial analysis using Geographical Information System (GIS) was applied to identify the locality of dengue incidence within the study area. Spatial statistical analysis of dengue cases used Kernel Density Estimation to distinguish dengue hotspots from the distribution of the exact location of dengue cases reported in Bandar Baru Bangi town.Results: Kernel density estimation showed the dengue hotspots concentrated on the east of Bandar Baru Bangi town. The results found that the highest density was in 2015 was 605 to 706 points per square kilometres. This study also discovers that most of the hotspots constructed were located in the residential area of Bandar Baru Bangi.Conclusions: This study is essential to help local authorities eradicate dengue in urban areas for future management strategies; therefore, this study is vital to help local authorities eradicate dengue in urban areas for future management strategies.

Geospatial Crime Analysis to Determine Crime Density Using Kernel Density Estimation for the Indian Context

2020 ◽  
Vol 17 (1) ◽  
pp. 74-86
Author(s):  
Boppuru Rudra Prathap ◽  
K. Ramesha
Keyword(s):  
Law Enforcement ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Financial Burden ◽  
Estimation Method ◽  
Kernel Density ◽  
Law Enforcement Agencies ◽  
The Government ◽  
Geographical Locations

Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime.

scholarly journals Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments

2020 ◽  
Vol 32 (6) ◽  
pp. 1211-1218
Author(s):  
Tomohiro Umetani ◽  
◽  
Yuya Kondo ◽  
Takuma Tokuda
Keyword(s):  
Mobile Robot ◽  
Mobile Robots ◽  
Rapid Development ◽  
Field Tests ◽  
Intelligent Environments ◽  
Mobile Platforms ◽  
Robot System ◽  
Location Data ◽  
Localization And Mapping ◽  
Outdoor Environments

Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.

Empirically Investigating a Hybrid Lean-Agile Design Paradigm for Mobile Robots

2015 ◽  
Vol 24 (1) ◽  
pp. 117-134
Author(s):  
Salah A.M. Elmoselhy
Keyword(s):  
Cost Effectiveness ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Development Process ◽  
Robot Design ◽  
Design Activities ◽  
Design Paradigm ◽  
Agile Design ◽  
Robot Performance

AbstractLean design and agile design paradigms have been proposed for designing robots; yet, none of them could strike a balance between cost-effectiveness and short duration of the design process without compromising the quality of performance. The present article identifies the key determinants of the mobile robots development process. It also identifies empirically the mobile robot design activities and strategies with the most influence on mobile robot performance. The study identified statistically the mobile robot design activities and strategies most positively correlated with mobile robot performance. The results showed that 65% of typical mobile robot design activities and strategies are affiliated with the lean design paradigm, while the remaining 35% are affiliated with the agile design paradigm. In addition, it was found that 22% of the lean mobile robot design activities and strategies and 25% of the agile mobile robot design activities and strategies, significantly with 99% confidence, are among the design activities and strategies most positively correlated with improving mobile robot performance. A hybrid lean-agile design paradigm is thus proposed.

scholarly journals How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

2019 ◽  
Vol 8 (12) ◽  
pp. 544 ◽  
Author(s):  
Zengli Wang ◽  
Lin Liu ◽  
Hanlin Zhou ◽  
Minxuan Lan
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Point Location ◽  
Point Distribution ◽  
Density Mapping ◽  
Mapping Process ◽  
Density Map ◽  
Location Map ◽  
Original Point

Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential.

Online Modelling and Tracking Control of Mobile Robots with Slippage in Outdoor Environments

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Bo ZHOU ◽  
Xianzhong DAI ◽  
Jianda HAN
Keyword(s):  
Mobile Robots ◽  
Tracking Control ◽  
Outdoor Environments
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