Support Robot Research Articles

Vision Based Wall Following Framework: A Case Study With HSR Robot for Cleaning Application.

Periodic cleaning of all frequently touched social areas such as walls, doors, locks, handles, windows has become the first line of defense against all infectious diseases. Among those, cleaning of large wall areas manually is always tedious, time-consuming, and astounding task. Although numerous cleaning companies are interested in deploying robotic cleaning solutions, they are mostly not addressing wall cleaning. To this end, we are proposing a new vision-based wall following framework that acts as an add-on for any professional robotic platform to perform wall cleaning. The proposed framework uses Deep Learning (DL) framework to visually detect, classify, and segment the wall/floor surface and instructs the robot to wall follow to execute the cleaning task. Also, we summarized the system architecture of Toyota Human Support Robot (HSR), which has been used as our testing platform. We evaluated the performance of the proposed framework on HSR robot under various defined scenarios. Our experimental results indicate that the proposed framework could successfully classify and segment the wall/floor surface and also detect the obstacle on wall and floor with high detection accuracy and demonstrates a robust behavior of wall following.

Support System for Teachers in Communication with Educational Support Robot

With the growth of robot technology, more educational-support robots, which support learning, are paid attention to. For example, one robot supports the school life of students. Another robot helps students to learn English better. Most researches have focused on robot behavior and investigating the effect. Previous research has reported that a society in which robots and humans learn together will soon be a reality. If a society where robots and humans learn side by side is realized, children will be in houses where they will learn alongside multiple unspecified robots. We think that perspectives of third parties, such as educators and guardians, are important for further improvements in the field. Thus, we think that a system is necessary wherein third parties can direct robots to provide suitable learning support to learners. This paper proposes a system for teachers that can direct robots to provide suitable learning support to learners, who simultaneously can grasp their learning conditions as they study alongside robots.

Table Cleaning Task by Human Support Robot Using Deep Learning Technique.

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 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.

Research on Fuzzy PID Control of Physical Exercise Supporting Robot Speed Control System

Physical exercise assisted robot speed regulation has higher requirements on dynamic characteristics of the system and faster response speed, and there are many uncertain factors in the process of climbing athletes in the process of climbing the building. Single closed-loop negative feedback control based on traditional control strategy System speed regulation is unsatisfactory. Based on the above problems, this paper establishes the mathematical model of the fuzzy PID control system of the physical exercise assisted robot speed control system, and designs a double closed loop speed control system based on fuzzy PID, and compares it with the traditional PID control; The sprint speed is given to the sinusoidal speed given curve by referring to the stability of the elevator climbing process and the comfort of the occupant. Using Matlab simulation, the simulation results show that in the physical training robot speed control system, the fuzzy PID control is more stable than the traditional PID control start and braking process, with higher comfort, smaller error and higher precision. Stair climbing has a strong use value and brings good value to the economy and society.

Learning Effects of Robots Teaching Based on Cognitive Apprenticeship Theory

In recent years, educational support robots that assist learners have attracted attention. The main role of teacher-type robots in previous research has been to teach students how to solve problems and to explain learning material. Under such conditions, students may not learn the material adequately due to their reliance on the support of the robot; this paper utilizes the cognitive apprenticeship theory in order to prevent this problem. The cognitive apprenticeship theory asserts that the support provided to a student should change according to the student’s learning situation. Previous studies have reported that pedagogy based on the cognitive apprenticeship theory can improve students’ learning skills. Therefore, we hypothesize that students’ learning will improve when robots teach them how to solve questions based on the cognitive apprenticeship theory. In this paper, we investigate the learning effects of robot teaching based on the cognitive apprenticeship theory in collaborative learning with junior high-school and university students. The results of this experiment suggest that collaborative learning with robots that employ the cognitive apprenticeship theory improves the learning of high-school and university students.

Research on vibration suppression by a multi-point flexible following support head in thin-walled parts mirror milling

Large thin-walled parts are widely utilized in many different fields such as aircraft manufacturing. However, the vibration caused by the milling force during processing unfortunately affects the processing quality. To reduce the vibration, a multi-point flexible following support head is proposed, which can adjust the supporting force in mirror milling in real time. By installing the milling cutter and support head at the end of the milling robot and support robot, respectively, and allowing them to symmetrically move during processing, the vibration of the thin-walled parts can be suppressed. In this study, the supporting force equations of the multi-point flexible following support head are established. Then, the vibration response of the thin-walled part with two clamped edges and two free edges (CCFF plate) under alternating external loads is analyzed, and the simulation model of the mirror milling vibration is established. The simulation is carried out under the milling condition with and without the support head to verify the vibration suppression effect of the support head. Finally, the mirror milling experiment verifies that the multi-point flexible following support head can decrease the amplitude by 98% compared with milling without a support head, and the processing quality is significantly improved.

Development of Maintenance Support Robot System to Expansive Plant Field

Development of Maintenance Support Robot System to Expansive Plant Field

The Proposal of Spiral Ascending Scanning Method for Pregnant Woman Ultrasound Support Robot, Built through the Analyzation of Mechanism of Contact Point of It

The Proposal of Spiral Ascending Scanning Method for Pregnant Woman Ultrasound Support Robot, Built through the Analyzation of Mechanism of Contact Point of It

Hands cooperation motion control for liquid pouring motion by life support robot

Hands cooperation motion control for liquid pouring motion by life support robot

Study on the standing-up support with Intelligent Walking Support Robot

Study on the standing-up support with Intelligent Walking Support Robot

Activities to Promote Life Support Robots in Sagami Robot Industry Special Zone

Activities to Promote Life Support Robots in Sagami Robot Industry Special Zone

Improvement of Gripping Ability with 3 Chopsticks in Chopstick-type Meal Support Robot

Improvement of Gripping Ability with 3 Chopsticks in Chopstick-type Meal Support Robot

Surgical Support Robot and Manipulator

Surgical Support Robot and Manipulator

Development of a Bridge Inspection Support Robot System Using Two-Wheeled Multicopters

We developed a bridge inspection support robot system that uses a two-wheeled multicopter and 3D modeling technology. Our system is divided into three main parts: 1) a two-wheeled multicopter to capture close-up images of the inspection target, 2) damage extraction using 3D modeling technology, and 3) a 3D model-based bridge maintenance system that stores inspection information linked with the Industry Foundation Classes (IFC) 3D product model. An inspector can use this system to visually inspect the overall structure and accurately detect hairline cracks.

Consideration of Safety Management When Using Pepper, a Humanoid Robot for Care of Older Adults

Pepper, a humanoid robot, is 1.2 m in height and is designed to move its limbs. There are risks of the older adults experiencing falling and collision accidents when they interact with Pepper. When physical interaction happens between a humanoid robot and human beings, potential harmful physical contact might occur. The aim of this report was to examine the safety management aspects when using Pepper, a humanoid robot for the care of older adults. The older adults’ reactions to Pepper’s functions cannot be predicted. Hence, it is necessary to clarify methods to guarantee its safety in advance and to increase the safety and properties of the robots. The benefits of introducing support robots such as Pepper for aging medical and nursing care settings are obvious. Therefore, engagement in robot development while considering both the risks and benefits is critical. Our academic initiatives have just begun. Through information exchange among researchers, users, engineers, and law specialists, we need to identify latent and prominent risks in situations where Pepper and the older adults interact and deepen our examination of measures against such risks.

Feature Analysis for Classification of Physical Actions Using Surface EMG Data

Based on recent health statistics, there are several thousands of people with limb disability and gait disorders that require a medical assistance. A robot assisted rehabilitation therapy can help them recover and return to a normal life. In this scenario, a successful methodology is to use the EMG signal based information to control the support robotics. For this mechanism to function properly, the EMG signal from the muscles has to be sensed and then the biological motor intention has to be decoded and finally the resulting information has to be communicated to the controller of the robot. An accurate detection of the motor intention requires a pattern recognition based categorical identification. Hence in this paper, we propose an improved classification framework by identification of the relevant features that drive the pattern recognition algorithm. Major contributions include a set of modified spectral moment based features and another relevant inter-channel correlation feature that contribute to an improved classification performance. Next, we conducted a sensitivity analysis of the classification algorithm to different EMG channels. Finally, the classifier performance is compared to that of the other state-of the art algorithms

An intelligent shopping support robot: understanding shopping behavior from 2D skeleton data using GRU network

In supermarkets or grocery, a shopping cart is a necessary tool for shopping. In this paper, we have developed an intelligent shopping support robot that can carry a shopping cart while following its owners and provide the shopping support by observing the customer’s head orientation, body orientation and recognizing different shopping behaviors. Recognizing shopping behavior or the intensity of such action is important for understanding the best way to support the customer without disturbing him or her. This system also liberates elderly and disabled people from the burden of pushing shopping carts, because our proposed shopping cart is essentially a type of autonomous mobile robots that recognizes its owner and following him or her. The proposed system discretizes the head and body orientation of customer into 8 directions to estimate whether the customer is looking or turning towards a merchandise shelf. From the robot’s video stream, a DNN-based human pose estimator called OpenPose is used to extract the skeleton of 18 joints for each detected body. Using this extracted body joints information, we built a dataset and developed a novel Gated Recurrent Neural Network (GRU) topology to classify different actions that are typically performed in front of shelves: reach to shelf, retract from shelf, hand in shelf, inspect product, inspect shelf. Our GRU network model takes series of 32 frames skeleton data then gives the prediction. Using cross-validation tests, our model achieves an overall accuracy of 82%, which is a significant result. Finally, from the customer’s head orientation, body orientation and shopping behavior recognition we develop a complete system for our shopping support robot.

Accountable system design architecture for embodied AI: a focus on physical human support robots

Although the development of robot-based support systems for elderly people has become more popular, it is difficult for humans to understand the actions, plans, and behavior of autonomous robots and the reasons behind them, particularly when the robots include learning algorithms. Learning-based autonomous systems which are called AI are treated as an inherently untrustworthy ‘black box,’ because machine learning or deep learning algorithms are difficult for humans to understand. Robot systems such as assistive robots, which work closely with humans, however, should be trusted. Systems should therefore achieve accountability for all stakeholders. However, most research in this field has focused on particular systems and situations, and no general design architecture exists. In this study, we propose a new design method, focused on accountability and transparency, for learning-based robot systems. Describing the entire system is a necessary first step, and transcribing the described system for each stakeholder based on several principles is effective for achieving accountability. The method improves transparency for systems, including learning algorithms. A standing assistive robot is used as an example of the entire system to clarify which system parts require greater transparency. This study adopted the Systems Modeling Language (SysML) to describe the system and the described system is used for the information representation. Information should be represented considering the relationships between stakeholders, information, and the system interface. Because of their complexity, it is difficult for humans to understand the complete set of information available in robot systems. Systems should therefore present only the information required, depending on the situation. The stakeholder–interface relationship is also important because it is more beneficial for professionals to view information relevant to their specialized field, which would be difficult for others to understand. By contrast, the interface should be intuitive for general users. Visualization and sound are very useful means of transmitting information, with advantages and disadvantages for different circumstances. These relationships are important for achieving accountability. Finally, we show an example of implementation with a developed support system. It is confirmed that accountable systems can be designed based on the proposed design architecture.

Development of Human Support Robot as the research platform of a domestic mobile manipulator

There has been an increasing interest in mobile manipulators that are capable of performing physical work in living spaces worldwide, corresponding to an aging population with declining birth rates with the expectation of improving quality of life (QoL). We assume that overall research and development will accelerate by using a common robot platform among a lot of researchers since that enables them to share their research results. Therefore we have developed a compact and safe research platform, Human Support Robot (HSR), which can be operated in an actual home environment and we have provided it to various research institutes to establish the developers community. Currently, the number of HSR users is expanding to 44 sites in 12 countries worldwide (as of November 30th, 2018). To activate the community, we assume that the robot competition will be effective. As a result of international public offering, HSR has been adopted as a standard platform for international robot competitions such as RoboCup@Home and World Robot Summit (WRS). HSR is provided to participants of those competitions. In this paper, we describe HSR’s development background since 2006, and technical detail of hardware design and software architecture. Specifically, we describe its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR’s operational movement and a novel whole body motion control system. Finally, we describe the verification of autonomous task capability and the results of utilization in RoboCup@Home in order to demonstrate the effect of introducing the platform.

Novel Operation Support Robot with Sensory-Motor Feedback System for Neuroendovascular Intervention

Robotic technology is rapidly developing in the medical field, particularly contributing to support operative intervention using the da Vinci system during endoscopic surgery. Neuroendovascular intervention robot surgery is preferred when aiming to reduce radiation exposure among surgeons. We developed a prototype of a support robot with 2 independent slaves manipulating both the microcatheter and microguidewire connected with the remote master driver with 2 joysticks. This design simulates usual catheterization with both hands. The slave manipulator has a sufficient output force >1 N to reproduce the exact master intervention without slip and delay. This machine has a unique function that indicates the reaction force of the resistance on wire stuck using the sensor system. We investigated the master-slave response, reliability of the force gauge, and degree of slippage of the slave motion on the table and checked the controllability, safety, and reproducibility of microcatheterization and insertion maneuver into the experimental aneurysm in the invivo silicone vessel model. We realized the well master-slave response with a stable driving speed of the microguidewire at approximately 1 mm/s and with linear correlation between the output voltage and driving force. Also, we confirmed the well safety function to avoid the overloading to the vascular wall with the slippage of the slave roller on loading >1 N pushing force. Successful microcatheterization and insertion into the aneurysm model was performed in the wet vascular model corresponding to the 3-dimensional handling without excessive stress to the vascular or aneurysmal wall. Neuroendovascular intervention requires delicate power adjustment with fine finger control. Our support robot for neuroendovascular interventions demonstrated the accurate reproducibility of the operator's maneuver and safe operation in the vascular model using the sensor system. This system will realize the neurointervention without human operators in the AngioSuite and may facilitate telesurgery with remote control in the near future.