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A Novel Path Planning Strategy for a Cleaning audit robot Using Geometrical Features and Swarm Algorithms

SENSORS 2022年第14期22卷 5317-5317页

作者： Pathmakumar, Thejus Muthugala, M. A. Viraj J. Samarakoon, S. M. Bhagya P. Gomez, Braulio Felix Elara, Mohan Rajesh Singapore Univ Technol & Design Engn Prod Dev Singapore 487372 Singapore

robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region's cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

关键词： audit robot geometrical feature cleaning auditing swarm algorithms

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Research on Substantive Procedure audit robot of Main Business Cost Based on RPA

World Scientific Research Journal

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World Scientific Research Journal 2022年第6期8卷 548-554页

作者： Zhi Huang Huaqin Li

The main business cost audit is one of the important items in the financial statement audit. The main purpose is to find out whether there is a major risk of misstatement in the statement items. It is necessary to obtain the internal financial data, business data, gross profit margin and other external data of the same industry. There are some problems, such as long data collection time, low accuracy of data processing, difficult to control the audit cost and so on. robot process automation (RPA) technology aims to solve the work with high degree of standardization and strong repeatability. Taking a accounting firm as the research object, based on the current situation of its main business cost audit substantive program, this paper analyzes its problems in obtaining external data, data processing and controlling audit cost, constructs the main business cost substantive program audit robot model by introducing RPA technology, analyzes the operation mechanism of the robot, from the technical path, development and implementation of the robot The focus of its application is described in detail in terms of impact, value and risk analysis on organizations and personnel.

关键词： RPA Main business cost Substantive procedures audit robot

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Research on audit of Endowment Insurance Fund based on RPA audit robot

Frontiers in Economics and Management

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Frontiers in Economics and Management 2023年第4期4卷 89-95页

作者： Ziyi Wang

Due to the increasingly obvious trend of China's aging population, and the high economic and social growth in China, the broad masses of people's demands for welfare benefits are also constantly improving. In order to solve this problem, China's basic old‐age insurance policy reform has followed. Since the social old‐age insurance fund will directly affect the overall situation of China's social reform, development and stability, it is also related to the vital interests of all the people, so it is of great significance to strengthen the audit of the basic old‐age insurance fund by local government departments. In view of the fact that the previous accounting model, based on the audit method, focused on the stability of fund collection, the authenticity of fund collection, the effectiveness of expenditure, etc., which obviously could not meet the requirements of the current society for the financial management of basic endowment insurance funds, this paper mainly used RPA technology to study the artificial intelligence technology in fund audit, and briefly described the application effect of automatic technology artificial intelligence, hoping to provide reference for the future information development of capital accounting of basic endowment insurance in China.

关键词： RPA Endowment Insurance Fund audit robot Big Data audit

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An Autonomous robot-Aided auditing Scheme for Floor Cleaning

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SENSORS 2021年第13期21卷 4332-4332页

作者： Pathmakumar, Thejus Kalimuthu, Manivannan Elara, Mohan Rajesh Ramalingam, Balakrishnan Singapore Univ Technol & Design SUTD Engn Prod Dev Pillar Singapore 487372 Singapore

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.

关键词： autonomous cleaning audit cleaning benchmark audit robot dirt driven exploration

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A Reinforcement Learning Based Dirt-Exploration for Cleaning-auditing robot

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SENSORS 2021年第24期21卷 8331-8331页

作者： Pathmakumar, Thejus Elara, Mohan Rajesh Gomez, Braulio Felix Ramalingam, Balakrishnan Singapore Univ Technol & Design SUTD Engn Prod Dev Pillar Singapore 487372 Singapore

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.

关键词： audit robot path planning reinforcement learning cleaning-auditing

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Toward a Comprehensive Domestic Dirt Dataset Curation for Cleaning auditing Applications

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SENSORS 2022年第14期22卷 5201-5201页

作者： Pathmakumar, Thejus Elara, Mohan Rajesh Soundararajan, Shreenhithy, V Ramalingam, Balakrishnan Singapore Univ Technol & Design Engn Prod Dev Singapore 487372 Singapore

Cleaning is an important task that is practiced in every domain and has prime importance. The significance of cleaning has led to several newfangled technologies in the domestic and professional cleaning domain. However, strategies for auditing the cleanliness delivered by the various cleaning methods remain manual and often ignored. This work presents a novel domestic dirt image dataset for cleaning auditing application including AI-based dirt analysis and robot-assisted cleaning inspection. One of the significant challenges in an AI-based robot-aided cleaning auditing is the absence of a comprehensive dataset for dirt analysis. We bridge this gap by identifying nine classes of commonly occurring domestic dirt and a labeled dataset consisting of 3000 microscope dirt images curated from a semi-indoor environment. The dirt dataset gathered using the adhesive dirt lifting method can enhance the current dirt sensing and dirt composition estimation for cleaning auditing. The dataset's quality is analyzed by AI-based dirt analysis and a robot-aided cleaning auditing task using six standard classification models. The models trained with the dirt dataset were capable of yielding a classification accuracy above 90% in the offline dirt analysis experiment and 82% in real-time test results.

关键词： domestic dirt dirt dataset audit robot cleaning benchmark dirt classification robot-aided cleaning auditing

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