A macro-micro robot for precise force applications

This paper describes an 8 degree-of-freedom macro-micro robot capable of performing tasks which require accurate force control. Applications such as polishing, finishing, grinding, deburring, and cleaning are a few examples of tasks which need this capability. Currently these tasks are either performed manually or with dedicated machinery because of the lack of a flexible and cost effective tool, such as a programmable force-controlled robot. The basic design and control of the macro-micro robot is described in this paper. A modular high-performance multiprocessor control system was designed to provide sufficient compute power for executing advanced control methods. An 8 degree of freedom macro-micro mechanism was constructed to enable accurate tip forces. Control algorithms based on the impedance control method were derived, coded, and load balanced for maximum execution speed on the multiprocessor system

Control strategies for cleaning robots in domestic applications: A comprehensive review:

Service robots are built and developed for various applications to support humans as companion, caretaker, or domestic support. As the number of elderly people grows, service robots will be in increasing demand. Particularly, one of the main tasks performed by elderly people, and others, is the complex task of cleaning. Therefore, cleaning tasks, such as sweeping floors, washing dishes, and wiping windows, have been developed for the domestic environment using service robots or robot manipulators with several control approaches. This article is primarily focused on control methodology used for cleaning tasks. Specifically, this work mainly discusses classical control and learning-based controlled methods. The classical control approaches, which consist of position control, force control, and impedance control , are commonly used for cleaning purposes in a highly controlled environment. However, classical control methods cannot be generalized for cluttered environment so that learning-based control methods could be an alternative solution. Learning-based control methods for cleaning tasks can encompass three approaches: learning from demonstration (LfD), supervised learning (SL), and reinforcement learning (RL). These control approaches have their own capabilities to generalize the cleaning tasks in the new environment. For example, LfD, which many research groups have used for cleaning tasks, can generate complex cleaning trajectories based on human demonstration. Also, SL can support the prediction of dirt areas and cleaning motion using large number of data set. Finally, RL can learn cleaning actions and interact with the new environment by the robot itself. In this context, this article aims to provide a general overview of robotic cleaning tasks based on different types of control methods using manipulator. It also suggest a description of the future directions of cleaning tasks based on the evaluation of the control approaches

Fuzzy Floor Dust Cleaning Robot Prototype Based On Arduino

The design of a Floor Cleaning Robot Based on Arduino Uno R3 aims to make it easier for humans to clean the dust on the floor efficiently. This robot consists of several series of components including Arduino Uno r3: Arduino Uno r3, sensor ultrasonic, motor servo, This robot consists of several components including Arduino Uno R3, ultrasonic sensor, servo motor, motor shield driver, dc motor, and vacuum blower motor. The entire system synergizes in the process of cleaning the dust on the floor and all of them are connected to a power source in the form of a dc battery, which supplies a voltage of 7 volts for the Arduino circuit and 12 volts for the vacuum blower. The working principle of this robot starts when the ultrasonic sensor can measure and distinguish the closest distance between the robot and an obstacle, at the same time the servo motor will move up to 180° to assist the ultrasonic sensor in detecting obstacles, both the front, right, and left sides of the robot. This Floor Cleaning Robot is programmed by adapting fuzzy logic artificial intelligence, the fuzzy rules used in this robot aim to control the speed of the robot based on the obstacle distance detected by the ultrasonic sensor. Fuzzy logic executes the data and continues the command to drive the motor so that the robot can work efficiently cleaning dust on the floor by minimizing the occurrence of collisions against obstacles. Dust suction carried out by the vacuum blower motor is not included in the Arduino circuit, because the vacuum blower motor requires an input power of 12 volts. It is not possible to unite it to the Arduino circuit because the power it has is only 7 volts, but these two separate circuits can still synergize well in the process of cleaning the dust on the floor. The results of observations and experiments show that the fuzzy logic embedded in the Arduino as the brain of the dust-cleaning robot on the floor works quite well

Development of water surface mobile garbage collector robot

This paper presents a prototype of Water Surface Mobile Garbage Collector Robot built in motivation to educate the people to love and monitor the health of our rivers by collecting the trash themselves using mobile robot. The garbage collector is designed aimed for the cleaning of small-scale lakes, narrow rivers, and drains in Malaysia. The navigation of the robot is controlled using wireless Bluetooth communication from a smartphone application. The performance of the water garbage collector in terms of manoeuvring control efficiency and garbage collection load capacity was tested and evaluated. Based on the experimental results from a swimming pool, it can operate within a 4-metre range and collect 192 grams of small to medium sized recyclable garbage such as food packages, water bottles, and plastics in 10 seconds. It managed to float and navigate on the Panchor River within Bluetooth network range. A strong, lightweight and waterproof material is recommended for use for this water garbage collector. A proximity sensor or image processing technique for detecting garbage on the water surface may be studied and included in the future to enable a fully autonomous manoeuvring control system

Virtual Borders: Accurate Definition of a Mobile Robot's Workspace Using Augmented Reality

We address the problem of interactively controlling the workspace of a mobile robot to ensure a human-aware navigation. This is especially of relevance for non-expert users living in human-robot shared spaces, e.g. home environments, since they want to keep the control of their mobile robots, such as vacuum cleaning or companion robots. Therefore, we introduce virtual borders that are respected by a robot while performing its tasks. For this purpose, we employ a RGB-D Google Tango tablet as human-robot interface in combination with an augmented reality application to flexibly define virtual borders. We evaluated our system with 15 non-expert users concerning accuracy, teaching time and correctness and compared the results with other baseline methods based on visual markers and a laser pointer. The experimental results show that our method features an equally high accuracy while reducing the teaching time significantly compared to the baseline methods. This holds for different border lengths, shapes and variations in the teaching process. Finally, we demonstrated the correctness of the approach, i.e. the mobile robot changes its navigational behavior according to the user-defined virtual borders.Comment: Accepted on 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), supplementary video: https://youtu.be/oQO8sQ0JBR

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

Microbial contamination and efficacy of disinfection procedures of companion robots in care homes

Background Paro and other robot animals can improve wellbeing for older adults and people with dementia, through reducing depression, agitation and medication use. However, nursing and care staff we contacted expressed infection control concerns. Little related research has been published. We assessed (i) how microbiologically contaminated robot animals become during use by older people within a care home and (ii) efficacy of a cleaning procedure. Methods This study had two stages. In stage one we assessed microbial load on eight robot animals after interaction with four care home residents, and again following cleaning by a researcher. Robot animals provided a range of shell-types, including fur, soft plastic, and solid plastic. Stage two involved a similar process with two robot animals, but a care staff member conducted cleaning. The cleaning process involved spraying with anti-bacterial product, brushing fur-type shells, followed by vigorous top-to-tail cleaning with anti-bacterial wipes on all shell types. Two samples were taken from each of eight robots in stage one and two robots in stage two (20 samples total). Samples were collected using contact plate stamping and evaluated using aerobic colony count and identification (gram stain, colony morphology, coagulase agglutination). Colony counts were measured by colony forming units per square centimetre (CFU/cm2). Results Most robots acquired microbial loads well above an acceptable threshold of 2.5 CFU/cm2 following use. The bacteria identified were micrococcus species, coagulase negative staphylococcus, diptheriods, aerobic spore bearers, and staphylococcus aureus, all of which carry risk for human health. For all devices the CFU/cm2 reduced to well within accepted limits following cleaning by both researcher and care staff member. Conclusions Companion robots will acquire significant levels of bacteria during normal use. The simple cleaning procedure detailed in this study reduced microbial load to acceptable levels in controlled experiments. Further work is needed in the field and to check the impact on the transmission of viruses

Microbial contamination and efficacy of disinfection procedures of companion robots in care homes

Contains fulltext : 221797.pdf (publisher's version ) (Open Access)Background: Paro and other robot animals can improve wellbeing for older adults and people with dementia, through reducing depression, agitation and medication use. However, nursing and care staff we contacted expressed infection control concerns. Little related research has been published. We assessed (i) how microbiologically contaminated robot animals become during use by older people within a care home and (ii) efficacy of a cleaning procedure. Methods: This study had two stages. In stage one we assessed microbial load on eight robot animals after interaction with four care home residents, and again following cleaning by a researcher. Robot animals provided a range of shell-types, including fur, soft plastic, and solid plastic. Stage two involved a similar process with two robot animals, but a care staff member conducted cleaning. The cleaning process involved spraying with anti-bacterial product, brushing fur-type shells, followed by vigorous top-to-tail cleaning with anti-bacterial wipes on all shell types. Two samples were taken from each of eight robots in stage one and two robots in stage two (20 samples total). Samples were collected using contact plate stamping and evaluated using aerobic colony count and identification (gram stain, colony morphology, coagulase agglutination). Colony counts were measured by colony forming units per square centimetre (CFU/cm2). Results: Most robots acquired microbial loads well above an acceptable threshold of 2.5 CFU/cm2 following use. The bacteria identified were micrococcus species, coagulase negative staphylococcus, diptheriods, aerobic spore bearers, and staphylococcus aureus, all of which carry risk for human health. For all devices the CFU/cm2 reduced to well within accepted limits following cleaning by both researcher and care staff member. Conclusions: Companion robots will acquire significant levels of bacteria during normal use. The simple cleaning procedure detailed in this study reduced microbial load to acceptable levels in controlled experiments. Further work is needed in the field and to check the impact on the transmission of viruses.17 p