Service Robots: Trends and Technology

Task-Motion Planning System for Socially Viable Service Robots Based on Object Manipulation

This paper presents a software architecture to implement a task-motion planning system that can improve human-robot interactions by including social behavior when social robots provide services related to object manipulation to users. The proposed system incorporates four main modules: knowledge reasoning, perception, task planning, and motion planning for autonomous service. This system adds constraints to the robot motions based on the recognition of the object affordance from the perception module and environment states from the knowledge reasoning module. Thus, the system performs task planning by adjusting the goal of the task to be performed, and motion planning based on the functional aspects of the object, enabling the robot to execute actions consistent with social behavior to respond to the user's intent and the task environment. The system is verified through simulated experiments consisting of several object manipulation services such as handover and delivery. The results show that, by using the proposed system, the robot can provide different services depending on the situation, even if it performs the same tasks. In addition, the system demonstrates a modular structure that enables the expansion of the available services by defining additional actions and diverse planning modules.

Design and analysis of a mobile robot with novel caster mechanism for high step-overcoming capability

The mobile robot market is experiencing rapid growth, playing a pivotal role in various human-centric environments like restaurants, offices, hotels, hospitals, apartments, and factories. However, current differential-driven mobile robots, employing conventional casters and wheel motors, encounter limitations in surmounting uneven surfaces and high steps due to constraints caused by wheel and caster dimensions. While some robots address these challenges by incorporating optimized wheel shapes and additional motors, this invariably leads to an increase in both size and cost. This research introduces an innovative solution; a novel caster-wheel mechanism designed to enhance the high-step overcoming capability of mobile robots without necessitating alterations to their overall size and structure. By incorporating a sub-wheel linked to a passive joint, the driving force is effeciently converted into a vertical force, thereby empowering the mobile robot to navigate obstacles 85% larger than its caster-wheel radius. Crucially, this innovative caster can be seamlessly manufactured and integrated, offering the potential for widespread adoption as a replacement for conventional casters. Validation through comprehensive simulations and experiments conducted on a prototype robot has been presented in this article, demonstrating its effectiveness even at a robot velocity of 0.1 m/s. This pioneering solution holds significant promise for diverse applications across various mobile robot configurations, presenting a compelling avenue for further exploration and implementation in the field.

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS's domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

Research status of elderly-care robots and safe human-robot interaction methods

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Towards a conceptualisation and critique of everyday life in HRI

This paper focuses on the topic of "everyday life" as it is addressed in Human-Robot Interaction (HRI) research. It starts from the argument that while human daily life with social robots has been increasingly discussed and studied in HRI, the concept of everyday life lacks clarity or systematic analysis, and it plays only a secondary role in supporting the study of the key HRI topics. In order to help conceptualise everyday life as a research theme in HRI in its own right, we provide an overview of the Social Science and Humanities (SSH) perspectives on everyday life and lived experiences, particularly in sociology, and identify the key elements that may serve to further develop and empirically study such a concept in HRI. We propose new angles of analysis that may help better explore unique aspects of human engagement with social robots. We look at the everyday not just as a reality as we know it (i.e., the realm of the "ordinary") but also as the future that we need to envision and strive to materialise (i.e., the transformation that will take place through the "extraordinary" that comes with social robots). Finally, we argue that HRI research would benefit not only from engaging with a systematic conceptualisation but also critique of the contemporary everyday life with social robots. This is how HRI studies could play an important role in challenging the current ways of understanding of what makes different aspects of the human world "natural" and ultimately help bringing a social change towards what we consider a "good life."

Biohybrid Soft Robots Powered by Myocyte: Current Progress and Future Perspectives

Myocyte-driven robots, a type of biological actuator that combines myocytes with abiotic systems, have gained significant attention due to their high energy efficiency, sensitivity, biocompatibility, and self-healing capabilities. These robots have a unique advantage in simulating the structure and function of human tissues and organs. This review covers the research progress in this field, detailing the benefits of myocyte-driven robots over traditional methods, the materials used in their fabrication (including myocytes and extracellular materials), and their properties and manufacturing techniques. Additionally, the review explores various control methods, robot structures, and motion types. Lastly, the potential applications and key challenges faced by myocyte-driven robots are discussed and summarized.

The Energy Efficiency Multi-Robot System and Disinfection Service Robot Development in Large-Scale Complex Environment

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations.

A multimodal human-robot sign language interaction framework applied in social robots

Deaf-mutes face many difficulties in daily interactions with hearing people through spoken language. Sign language is an important way of expression and communication for deaf-mutes. Therefore, breaking the communication barrier between the deaf-mute and hearing communities is significant for facilitating their integration into society. To help them integrate into social life better, we propose a multimodal Chinese sign language (CSL) gesture interaction framework based on social robots. The CSL gesture information including both static and dynamic gestures is captured from two different modal sensors. A wearable Myo armband and a Leap Motion sensor are used to collect human arm surface electromyography (sEMG) signals and hand 3D vectors, respectively. Two modalities of gesture datasets are preprocessed and fused to improve the recognition accuracy and to reduce the processing time cost of the network before sending it to the classifier. Since the input datasets of the proposed framework are temporal sequence gestures, the long-short term memory recurrent neural network is used to classify these input sequences. Comparative experiments are performed on an NAO robot to test our method. Moreover, our method can effectively improve CSL gesture recognition accuracy, which has potential applications in a variety of gesture interaction scenarios not only in social robots.

Application of High-Photoelasticity Polyurethane to Tactile Sensor for Robot Hands

We developed a tactile sensor for robot hands that can measure normal force (F_Z) and tangential forces (F_X and F_Y) using photoelasticity. This tactile sensor has three photodiodes and three light-emitting diode (LED) white light sources. The sensor is composed of multiple elastic materials, including a highly photoelastic polyurethane sheet, and the sensor can detect both normal and tangential forces through the deformation, ben sding, twisting, and extension of the elastic materials. The force detection utilizes the light scattering resulting from birefringence.

Memory-based crowd-aware robot navigation using deep reinforcement learning

The evolution of learning techniques has led robotics to have a considerable influence in industrial and household applications. With the progress in technology revolution, the demand for service robots is rapidly growing and extends to many applications. However, efficient navigation of service robots in crowded environments, with unpredictable human behaviors, is still challenging. The robot is supposed to recognize surrounding information while navigating, and then act accordingly. To address this issue, the proposed method crowd Aware Memory-based Reinforcement Learning (CAM-RL) uses gated recurrent units to store the relative dependencies among the crowd, and utilizes the human–robot interactions in the reinforcement learning framework for collision-free navigation. The proposed method is compared with the state-of-the-art techniques of multi-agent navigation, such as Collision Avoidance with Deep Reinforcement Learning (CADRL), Long Short-Term Memory Reinforcement Learning (LSTM-RL) and Social Attention Reinforcement Learning (SARL). Experimental results show that the proposed method can identify and learn human–robot interactions more extensively and efficiently than above-mentioned methods while navigating in a crowded environment. The proposed method achieved a success rate of greater than or equal to $$99\%$$ 99 % and a collision rate of less than or equal to $$1\%$$ 1 % in all test case scenarios, which is better compared to the previously proposed methods.

An Open-Environment Tactile Sensing System: Toward Simple and Efficient Material Identification

Robotic perception can have simple and effective sensing functions that are unreachable for humans using only the isolated tactile perception method, with the assistance of a triboelectric nanogenerator (TENG). However, the reliability of triboelectric sensors remains a major challenge due to the inherent environmental limitations. Here, an intelligent tactile sensing system that combines a TENG and deep-learning technology is proposed. Using a triboelectric triple tactile sensor array, typical characteristics of each testing material can be maintained stably even under different contact conditions (touch conditions and external environmental conditions) by extracting features from three independent electrical signals as well as the normalized output signals. Furthermore, a convolutional neural network model is integrated, and a high accuracy of 96.62% is achieved in a material identification task. The tactile sensing system is exhibited to an open environment for material identification and the real-time demonstration. Compared to the complex process that humans must integrate multiple sensing (touching and viewing) to accomplish tactile perception, the proposed sensing system shows a huge advantage in cognitive learning for the visually impaired, biomimetic prosthetics, and virtual spaces construction.

A New Approach for Including Social Conventions into Social Robots Navigation by Using Polygonal Triangulation and Group Asymmetric Gaussian Functions

Many authors have been working on approaches that can be applied to social robots to allow a more realistic/comfortable relationship between humans and robots in the same space. This paper proposes a new navigation strategy for social environments by recognizing and considering the social conventions of people and groups. To achieve that, we proposed the application of Delaunay triangulation for connecting people as vertices of a triangle network. Then, we defined a complete asymmetric Gaussian function (for individuals and groups) to decide zones where the robot must avoid passing. Furthermore, a feature generalization scheme called socialization feature was proposed to incorporate perception information that can be used to change the variance of the Gaussian function. Simulation results have been presented to demonstrate that the proposed approach can modify the path according to the perception of the robot compared to a standard A* algorithm.

Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results.