Robust Task Scheduling for Heterogeneous Robot Teams Under Capability Uncertainty

This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.

Navigating Robots in Dynamic Environment With Deep Reinforcement Learning

In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots. IEEE

IoT-Based Epidemic Monitoring via Improved Gated Recurrent Unit Model

During the Coronavirus Disease 2019 (COVID-19) pandemic, non-contact health monitoring and human activity detection by various sensors have attracted tremendous attention. Robot monitoring will result in minimizing the life threat to health providers during the COVID-19 pandemic period. How to improve the performance and generalization of the monitoring model is a critical but challenging task. This paper constructs an epidemic monitoring architecture based on multi-sensor information fusion and applies it in medical robots’ services, such as patient-care, disinfection, garbage disposal, etc. We propose a gated recurrent unit model based on a genetic algorithm (GA-GRU)to realize the effective feature selection and improve the effectiveness and accuracy of the localization, navigation, and activity monitoring for indoor wireless sensor networks (WSNs). By using two GRU layers in the GA-GRU, we improve the generalization capability in multiple WSNs. All these advantages of GA-GRU make it outperform other representative algorithms in a variety of evaluation metrics. The experiments on the WSNs verify that the proposed GA-GRU leads to successful runs and provides optimal performances. These results suggest the GA-GRU method may be preferable for epidemic monitoring in medicine and allied areas with particular relation to the control of the epidemic or pandemic such as COVID-19 pandemic.

A Remotely Controlled Oropharyngeal Swab Sampling Robot and its Application in COVID-19 Prevention

Oropharyngeal swab sampling is the major viral nucleic acid detection method to diagnose COVID-19. Medical staff exposes themselves to the respiratory secretions of patients, which makes them vulnerable to infection. To protect medical staff, we summarize the clinical requirements for robot into five considerations (standardization, ergonomics, safety, isolation, and task allocation) and developed a remotely operated oropharyngeal swab sampling robot. With robot assistance, spatial isolation between medical staff and the patients can be achieved. We designed a hybrid force/position control scheme for the sampling robot to achieve intuitive operation and stable contact force. The experiment results on phantom tissue show that the sampling robot can achieve intuitive operation and stable contact on the soft posterior pharyngeal. Clinical trials for 20 volunteers and 2 patients diagnosed with COVID-19 are carried out. The results of the clinical trial indicated that the sampling robot can collect samples stably and effectively, and the contact force is gentler and more uniform. For two patients diagnosed with COVID-19, the robot sampling results are consistent with manual sampling. IEEE

Human-Robot Collaboration With Commonsense Reasoning in Smart Manufacturing Contexts

Human-robot collaboration (HRC), where humans and robots work together to handle specific tasks, requires designing robots that can effectively support human beings. Robots need to conduct reasoning using commonsense knowledge (CSK), e.g., fundamental knowledge that humans possess and use subconsciously, in order to assist humans in challenging and dynamic environments. Currently, there are several effective CSK systems used for organizing information and facts, along with detecting objects and determining their properties. HRC is employed in various manufacturing tasks, such as paint spraying and assembly, in order to keep humans safe while increasing efficiency. Although there is a large array of research on HRC and on CSK, there is minimal research linking the two concepts together. This paper presents a novel system on human-robot collaboration guided by commonsense reasoning for automation in manufacturing tasks. This fits within the general realm of smart manufacturing. The primary focus is on improving the efficacy of human-robot co-assembly tasks. Evaluations conducted with online simulations and real-world experiments indicate that reasoning using CSK-based robot priorities enhances HRC as compared to simpler robot priorities, e.g., merely handling nearby objects. This system is modifiable and can be used for larger and more complex real-world tasks, thereby leading to improved automation in manufacturing. This paper demonstrates the scope of combining HRC and CSK, while future works will be able to further utilize the benefits of combining the two fields with significant impacts. Note to Practitioners—This paper is motivated by the human-robot collaboration problem in smart manufacturing. Robots operating by reasoning with commonsense priorities in human-robot collaboration enable faster task execution and better human work life. This can help balance work for humans and prevent injury. Adding robots to tasks accordingly does not necessarily decrease costs, but can limit human exposure to danger which is significant (and can also lower costs overall). Simulations and real-world experiments in our research using commonsense reasoning demonstrate how work is easier and better with human-robot collaboration. These factors are highly significant when tasks are repeated multiple times. The system is presented within automated manufacturing and is scalable for different real-world applications. Such automation is particularly helpful during recent times in the aftermath of the COVID-19 pandemic. [ FROM AUTHOR] Copyright of IEEE Transactions on Automation Science & Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Human-Robot Communication System for an Isolated Environment

In this paper, we demonstrated a service robot navigation system based on the Message Queuing Telemetry Transport (MQTT) protocol communication system that updates the real-time robot states for multiple users. The proposed office assistant robot (OABot) consists of a navigable structure, a mobile app, and a central control workstation and these three components intercommunicate via a wireless network. The voice-recognition mobile app is used to interact with users with voice commands;these voice commands are processed inside the workstation and actions are assigned to the moving robot accordingly. The robot can navigate inside the room using real-time maps while localizing itself in the environment. In addition, the robot is equipped with a digital camera to identify people in predefined locations in the room. The WiFi communication system is provided with RESTful and Mosquitto servers for better human-robot communication. Hence, multiple users are notified about the robot status through updates on the real-time states via the MQTT protocol. The developed system successfully navigates to the instructed destinations and identifies the target person with an average accuracy of 96%. Most importantly, in an isolated indoor environment with social distancing restrictions to perform, the proposed system is essentially useful for contactless delivery.

RaspyLab: A Low-Cost Remote Laboratory to Learn Programming and Physical Computing Through Python and Raspberry Pi

This article describes the development and assessment of RaspyLab which is a low-cost Remote Laboratory (RL) to learn and teach programming with Raspberry Pi and Python language. The RL is composed of 16 stations or nodes that contain hardware components such as display LCD, robotic arm, temperature sensor, among others, and two modes of programming (graphical and text-based) for the students to experiment with their designed algorithms. The concept of the RL was conceived as a pedagogical tool to support the students of Engineering and Computer Science (CS) in an online learning format, given the context of the COVID-19 pandemic. The laboratory has been used by ([Formula Omitted]) CS students during the second semester of 2020 in the subject of mathematical logic through the methodology of Problem-Based Learning (PBL). To evaluate preliminary the laboratory, it was used a survey with 3 open-ended questions and 12 closed-ended questions on a Likert scale according to the Technology Acceptance Model (TAM). The outcomes show a good reception of the laboratory, an enhancement of the students’ learning regarding the concepts addressed in the course, and an interest of the students for the laboratory to be included in other subjects of the curricula.

A Low-Cost Telerehabilitation Paradigm for Bimanual Training

The COVID-19 pandemic has transformed daily life, as individuals must reduce contacts among each other to prevent the spread of the disease. Consequently, patients’ access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient’s gestures are translated into actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation.