关键词： kirigami   origami   paper cutting   paper folding  

摘要： Origami/kirigami, the ancient art of paper folding and cutting techniques, has provided considerable inspiration for structural design routes in the engineering and medical fields over the last few decades. The practicability of the methods and concepts of origami/kirigami has been demonstrated in several emerging classes of technologies, e.g., stretchable electronics, deformable devices, self-assembly fabrication, etc. More and more related products are produced pursuing a folding form for better storage, deformation capacity, and multifunction realization. Herein, the innovative creased patterns of origami/kirigami designs are distinguished and discussed, and the four most widely used creased pattern types are introduced, which may potentially provide origami/kirigami related inspiration and additional solutions toward many research fields.

关键词： Robotics  

摘要： This study investigates the interdependence between AI & Robotics stocks and traditional (including stocks and bonds) and alternative (commodities and crypto-currencies) assets, employing wavelet coherence analysis in time-frequency space. We further provide a fresh perspective on potential hedging and diversification benefits of AI & Robotics stocks. Overall, our results suggest that co-movements between AI & Robotics stocks and other assets significantly depend on the wavelet decomposition levels, suggesting time-scale-dependent investment benefits. Wavelet coherences and correlations have substantially increased, mostly in the low frequencies, during the COVID-19 pandemic. Government securities exhibit safe haven properties for investors at the highest and lowest scales. Even if crypto-currencies can provide hedging benefits over the full sample, these benefits seem to be diminished during the COVID-19 period. We observe substantially higher co-movements of AI stocks with the composite stock index, corporate bonds, and commodities at all scales after March 2020, implying that inclusion of these assets in AI & Robotics stock portfolios may not enhance risk-adjusted portfolio performance in times of market turbulence. These results offer potential implications for investors and portfolio managers in terms of hedging/diversification benefits as well as for authorities and policy makers regarding the development of strategies to mitigate financial risk.

关键词： Augmented Reality   Robotics   Industrial integration   Distributed automation architectures   Spatial referencing   Robot programming  

摘要： Augmented Reality (AR) has the potential for facilitating the interaction with robots by enhancing the operator's spatial understanding as well as providing further cognitive support, e.g. in order to make manual programming processes more efficient and provide on-site simulation. However, we consider major issues that prevent a widespread use of AR towards robotics in industries. Initially, the commissioning of AR devices for robotic applications demands spatial referencing of robots and AR devices. Fiducial markers are a popular artificial aid, but hard to implement in industrial use cases because of additional efforts and a lack of robustness. A further bottleneck for developing AR applications in robotics is the restricted technical and ergonomic maturity of AR devices, e.g. limited local computation and short product life-cycles. Furthermore, benefit through AR in comparison to classic online and offline programming techniques is still unclear for most applicators. In this paper, we present approaches towards a distributed, hardware-agnostic microservice architecture with standard interfaces for an interoperable usage of AR in robotics. Furthermore, we present marker-less pose estimation methods for articulated robot arms as well as mobile robots based on RGB and depth images that serve automatic referencing. Finally, we reveal further application potential of AR in robotics in terms of combining advantages from online and offline programming.

关键词： Mathematical model   Computational modeling   Neural networks   Analytical models   Robots   Convergence   Kinematics   Computer simulations   robot experiments   saturation-allowed neural dynamics (SAND)   theoretical analyses   time-dependent system of linear equations  

摘要： Neural networks as well as the related neural dynamics have been widely exploited to conduct online computing operations for solving various problems in recent years. This article makes progress along this direction by proposing a saturation-allowed neural dynamics (SAND) model for solving the perturbed time-dependent system of linear equations with noise-tolerant capacity. Specifically, by elaborately constructing a new general framework enhanced by the error-integration information and nonlinear projection functions (PFs), a SAND model is proposed and investigated under various additive noises. In addition, theoretical analyses reveal that the proposed SAND model is of global convergence with zero theoretical error. Moreover, when the value of PF is strictly limited by bounds, i.e., a saturation function, the upper bound of the residual errors of the proposed SAND model is subject to the noises and the bounds of PF. Computer simulation results, as well as robot experiments, verify the superior property of the proposed SAND model for solving the perturbed time-dependent system of linear equations, compared with the state of the prior art.

关键词： Cellular automata   Swarm robotics   Bio-inspired controller   Ant colony optimization   Genetic algorithms   Surveillance task   Tabu search  

摘要： The area of swarm robotics has grown widely in recent years, precisely because its formulation is based on the use of various techniques, ranging from computer networks to controllers. We can employ different types of techniques to carry out the control of a robots team. In this work we will focus on creating techniques based on bio-inspired computing. Within this theme, we will be focused on using cellular automata with synchronous and asynchronous rules and ant colonies optimization. Additionally, we will consider greedy approaches to select the next robot's state cell and a local Tabu search with a queue of robot movement restrictions. Thus, we have a surrogate model capable of providing the team robot navigation in the surveillance task. We developed two different controllers, a simpler first, based on a precursor model and a second optimized model, based on the previous controller refinement. At the end, we used a genetic algorithm, which received the surrogate model as input for the improvement of our proposed models parameters. In addition, a survey with the evolution of surveillance models using cellular automata in a systematic review of literature will be shown. Experiments were performed to demonstrate the degree of robot team coverage by different environments. We accomplished statistical analysis with the intention of presenting different sizes of robot teams and amounts of pheromone deposited into the environment. In the end, we fulfilled experiments using the empirical simulation methodology of a robots team using the Webots simulator with e-Puck architecture. The results were promising, the robot team performed this task efficiently and the system is highly scalable. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Agility   Best effort   Robotics   Industry   Control   Standardization  

摘要： In this paper we discuss our evolved understanding on the meaning of agile robotics. The lessons learned are concluded from the four years of our participation in ARIAC, the Agile Robotics for Industrial Automation Competition, that we managed to win in 2020. After elaborating on ARIAC tasks, challenges and scoring and their consequences on algorithm design, error handling and software development processes and methodologies, we introduce the concept of Best Effort Agile Robotics (BEAR). The conclusions are mapped to agility standardization directions. The concept of Best Effort Robotics (BER) is also proposed and discussed, highlighting the similarities with best effort networking, a proven concept in Internet technology.

关键词： Transfer functions   Robots   Unmanned aerial vehicles   Control systems   Vehicle dynamics   Stability analysis   Robustness   Adaptive strictly negative-imaginary (ASNI) controller   distributed unmanned aerial vehicle (UAV) formation control   fuzzy inference systems (FIS)   robust control  

摘要： Wind gusts are significant barriers to the outdoor operations of networked multiple unmanned aerial vehicles (UAVs), which fly in close proximity to each other or around obstacles. As such, traditional control methods such as PID control may not perform adequately. Based on the strictly negative imaginary (SNI) systems theory, this article presents a novel decentralized and adaptive consensus-based formation control law that drives multiple UAVs to follow the desired formation in the presence of limited bandwidth for information exchange and dynamically changing environmental conditions. To be consistent with a decentralized approach, each UAV only measures its relative position with respect to its neighbors according to a fixed information graph. As a result, the required formation is obtained by maintaining the desired relative positions among UAVs. Moreover, to deal with the challenging dynamics of flight environments, we also employ a knowledge-based fuzzy inference system to automatically adjust the parameters of the SNI consensus controllers, leading to the development of a fast and robust adaption method. In this article, we conduct a stability analysis based on the SNI theorem and rigorously compare the performance of our controllers with respect to the performance of conventional PID controllers. The efficacy of the overall closed loop control system is highlighted in real-time flight tests.

关键词： Caregiver robot   Human caregiver   Assistive robot   Service robot   Cognitive impairment   Systematic mapping study  

摘要： The World Health Organization (WHO) reported that more than 1 billion people live with some form of disability. Moreover, the number of elderly is increasing in recent years. According to the United Nations (UN), in 2050, there will be 2.1 billion people above 60 years of age worldwide. Many of these people live alone in their homes or clinics and rely on some kind of help to fulfill their specific needs. In this context, emerging opportunities for the application of robotics to support ubiquitous healthcare may reflect in reducing medical costs and increasing the convenience of patients and people in general. This paper presents a systematic mapping study to identify the application of service robots in the assistance of human care, focusing on the employment of computational technologies and unexplored research gaps in the literature. The study conducted searches in eight scientific repositories in the area of service robots through a systematic filtering process to remove bias. Afterward, the filtering process allowed to reduce from an initial sample of 9372 to 69 studies. As a result, these studies were reviewed entirely, analyzed, and categorized to answer six research questions. In addition, the study proposed four taxonomies illustrating the state-of-the-art of robotics in human care. The results highlight therapy and entertainment as the most common categories of the usage of robotics in human care. The most widely-used technologies to integrate with smart environments are smartphone sensors, smart device integration, wearables, and cloud services. The most frequently used mean of human-robot interaction is verbal communication, which is useful to help the elderly, children, and people with a mental health disorder. The most commonly cited diseases were cognition impairment, autism spectrum disorder, and motor impairment. Finally, we observed a trend in the growth of the use of service robots to improve the intelligence of the environment supporting human ca

关键词： Cloud robotics   Edge computing   Multi-robot systems   Robot perception  

摘要： Today's robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run themost accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally. In this paper, we formulate a novel Robot Offloading Problem-how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3 and 2.3x of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.