An Empirical Evaluation Framework for Autonomous Vacuum Cleaners in Industrial and Commercial Settings: A Multi-Metric Approach

Despite advancements in cleaning automation, there is a noticeable gap in standardized evaluation methods for autonomous vacuum cleaners in industrial and commercial settings. Existing assessments often lack a unified approach, focusing narrowly on either technical capabilities or financial aspects, without integrating both perspectives. This research presents a framework for the evaluation of autonomous vacuum cleaners in industrial and commercial settings, focusing on eight key metrics. These metrics are designed to provide a unified empirical perspective of the vacuum cleaners\u27 performance, operational efficiency, cost, productivity, durability, safety, return on investment, and adaptability. The proposed framework starts with an analysis of cleaning efficiency, examining both the area covered by the cleaners and the quality of cleaning. Advanced image processing techniques are suggested for mapping the area coverage, tailored to different vacuum designs. For assessing cleaning quality, the proposal highlights the potential integration of real-time dirt detection technologies, such as gravimetric sampling and light sensors, to dynamically adapt to varying dirt concentrations and types. Operational efficiency part encompasses the assessment of battery life, charge time, and operational downtime. It advocates for a dual approach of empirical testing and analytical modeling to measure battery life and charge time accurately. The evaluation of operational downtime incorporates tracking of maintenance, charging periods, and other non-operational activities, complemented by predictive modeling for efficient future planning. The financial aspect of the proposed framework encompassed under cost metrics, considers the initial investment, operational and maintenance costs, and potential labor cost savings. This study argues that these cost analysis aids in understanding the long-term financial implications of adopting autonomous vacuum cleaners. Productivity metrics focus on the cleaning speed and the level of autonomy of the vacuum cleaners. Cleaning speed is evaluated using formulas that take into account various environmental factors, while the autonomy level is determined using Sheridan\u27s Levels of Autonomy, which reflects the vacuum\u27s operational independence and its impact on human productivity. Durability, reliability, safety, and compliance are key for vacuum cleaners, evaluated through metrics like Mean Time Between Failures, Mean Time To Repair, Service Life, safety incidents, and adherence to standards and regulations. Lastly, the suggested framework evaluates the vacuum\u27s flexibility and adaptability in different environments, such as various floor types and conditions, highlighting the importance of versatility in autonomous cleaning solutions. Article history: Received: 01/December /2022; Available online: 07/ February/2023; This work is licensed under a Creative Commons International License

Incremental Learning for Robot Perception through HRI

Scene understanding and object recognition is a difficult to achieve yet crucial skill for robots. Recently, Convolutional Neural Networks (CNN), have shown success in this task. However, there is still a gap between their performance on image datasets and real-world robotics scenarios. We present a novel paradigm for incrementally improving a robot's visual perception through active human interaction. In this paradigm, the user introduces novel objects to the robot by means of pointing and voice commands. Given this information, the robot visually explores the object and adds images from it to re-train the perception module. Our base perception module is based on recent development in object detection and recognition using deep learning. Our method leverages state of the art CNNs from off-line batch learning, human guidance, robot exploration and incremental on-line learning

More than Three Laws of Robotics

Wired for War: The Robotics Revolution and Conflict in the 21st Centur

Phone virtual environment for RC assistive robot

Robotic systems nowadays are all around us and interact in many aspects such as homeowners use robotic vacuums in their homes. However, a push needed to make these systems more and more interactive with humans. The complexity of the robot controller might be one of the boundaries between the robot interactions with human. An assistive robot with an Android phone controller needed since smart phones nowadays are used almost by everyone. This assistive robot controller does not require much effort from the user, just needs moving a finger to control them. This project uses SolidWorks and Processing in the software development phase. The IOIO board has been applied as a main controller to run the system as an embedded system, and integrate the android application with the assistive robot. The communication has been established using the standard 0 dBm radio Bluetooth with range of 10 meters radius. The joint have a good desired tracking with less than 1% error. Finally the aim of this project, which is a user friendly smart phone application to monitor and control the assistive robot has been developed

Robots that Teach: Developing an Integrated Curriculum for Middle School Math

Keeping middle school students interested in mathematics and motivating them to succeed are challenges that continually present themselves to even the most seasoned teachers. Students will be drawn to mathematics if they are able to connect it to exciting careers. This paper describes a research-based curriculum model for teachers, grant writers, and others interested in incorporating Lego robotics technologies into an existing middle school mathematics curriculum. The model is based on data gathered from a three year research study in which approximately 150 sixth grade students per year and their teachers collaborated with a middle school Math Coordinator, a school Numeracy Coach, and university-level educational technology professors

Acoustic Cybersecurity: Exploiting Voice-Activated Systems

In this study, we investigate the emerging threat of inaudible acoustic attacks targeting digital voice assistants, a critical concern given their projected prevalence to exceed the global population by 2024. Our research extends the feasibility of these attacks across various platforms like Amazon's Alexa, Android, iOS, and Cortana, revealing significant vulnerabilities in smart devices. The twelve attack vectors identified include successful manipulation of smart home devices and automotive systems, potential breaches in military communication, and challenges in critical infrastructure security. We quantitatively show that attack success rates hover around 60%, with the ability to activate devices remotely from over 100 feet away. Additionally, these attacks threaten critical infrastructure, emphasizing the need for multifaceted defensive strategies combining acoustic shielding, advanced signal processing, machine learning, and robust user authentication to mitigate these risks

PV boost converter conditioning using neural network

This master report presents a voltage control system for DC-DC boost converter integrated with Photovoltaic (PV) array using optimized feed-forward neural network controller. A specific output voltage of a boost converter is regulated at a constant value under input voltage variations caused by a sudden changes in irradiation for a purpose of supplying a stabilize dc voltage to Base Transceiver Station (BTS) telecommunication equipment that required a 48V dc input supply to be operated. For a given solar irradiance, the tracking algorithm changes the duty ratio of the converter such that the output voltage produced equals to 48V. This is done by the feed-forward loop, which generates an error signal by comparing converter output voltage and reference voltage. Depending on the error and change of error signals, the neural network controller generates a control signal for the pulse widthmodulation generator which in turn adjusts the duty ratio of the converter. The effectiveness of the proposed method is verified by developing a simulation model in MATLAB-Simulink program. Tracking performance of the proposed controller is also compared with the conventional proportional-integral-differential (PID) controller. The simulation results show that the proposed neural network controller (NNC) produce an improvement of control performance compared to the PID controller

Enhanced Unscented Kalman Filter-Based SLAM in Dynamic Environments: Euclidean Approach

This paper introduces an innovative approach to Simultaneous Localization and Mapping (SLAM) using the Unscented Kalman Filter (UKF) in a dynamic environment. The UKF is proven to be a robust estimator and demonstrates lower sensitivity to sensor data errors compared to alternative SLAM algorithms. However, conventional algorithms are primarily concerned with stationary landmarks, which might prevent localization in dynamic environments. This paper proposes an Euclidean-based method for handling moving landmarks, calculating and estimating distances between the robot and each moving landmark, and addressing sensor measurement conflicts. The approach is evaluated through simulations in MATLAB and comparing results with the conventional UKF-SLAM algorithm. We also introduce a dataset for filter-based algorithms in dynamic environments, which can be used as a benchmark for evaluating of future algorithms. The outcomes of the proposed algorithm underscore that this simple yet effective approach mitigates the disruptive impact of moving landmarks, as evidenced by a thorough examination involving parameters such as the number of moving and stationary landmarks, waypoints, and computational efficiency. We also evaluated our algorithms in a realistic simulation of a real-world mapping task. This approach allowed us to assess our methods in practical conditions and gain insights for future enhancements. Our algorithm surpassed the performance of all competing methods in the evaluation, showcasing its ability to excel in real-world mapping scenarios.Comment: 9 pages, 9 figure

Improving Human-Machine Collaboration Through Transparency-based Feedback – Part I: Human Trust and Workload Model

In this paper, we establish a partially observable Markov decision process(POMDP) model framework that captures dynamic changes in human trust and workload for contexts that involve interactions between humans and intelligent decision-aid systems. We use a reconnaissance mission study to elicit a dynamic change in human trust and workload with respect to the system’s reliability and user interface transparency as well as the presence or absence of danger. We use human subject data to estimate transition and observation probabilities of the POMDP model and analyze the trust-workload behavior of humans. Our results indicate that higher transparency is more likely to increase human trust when the existing trust is low but also is more likely to decrease trust when it is already high. Furthermore, we show that by using high transparency, the workload of the human is always likely to increase. In our companion paper, we use this estimated model to develop an optimal control policy that varies system transparency to affect human trust-workload behavior towards improving human-machine collaboration