关键词： Coupling dynamics   control   legged robot   simulation  

摘要： This article develops a coupling dynamic method focusing on the coupling relationship between the leg and the body of a legged robot. The dynamic equation of the method reflects the mechanism between an active operating part and a passive moving part among a multibody system. It is used to adjust the position and posture of the body by driving the motion of the joint of the leg, thereby solving problems such as rollover or overturning of the body during its walking or running. Because the robot has enough redundant degrees of freedom, we can design other additional constraints to achieve coordinated control of the robot. The developed dynamic modeling method is applied for two control tasks. By the results of numerical simulations, the effectiveness of this method is verified.

关键词： Bipedal robots   Hybrid systems   Port-Hamiltonian dynamics   Fractional sliding surface   Finite-time control   Disturbance estimator  

摘要： In this paper, disturbance reconstruction and robust trajectory tracking control of biped robots with hybrid dynamics in the port-Hamiltonian form is investigated. A new type of Hamiltonian function is introduced, which ensures the finite-time stability of the closed-loop system. The proposed control system consists of two loops: an inner and an outer loop. A fractional proportional-integral-derivative filter is used to achieve finite-time convergence for position tracking errors at the outer loop. A fractional-order sliding mode controller acts as a centralized controller at the inner-loop, ensuring the finite-time stability of the velocity tracking error. In this loop, the undesired effects of unknown external disturbance and parameter uncertainties are compensated using estimators. Two disturbance estimators are envisioned. The former is designed using fractional calculus. The latter is an adaptive estimator, and it is constructed using the general dynamic of biped robots. Stability analysis shows that the closed-loop system is finite-time stable in both contact-less and impact phases. Simulation studies on three types of biped robots (i.e., two-link walker, RABBIT biped robot, and flat-feet biped robot) demonstrate the proposed controller's tracking performance and disturbance rejection capability. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Cognition   Perception   Reaching and grasping task   Visual servoing   Sensing approach   Learning approach   Gripping approach  

摘要： Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object's location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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： wearable assistive robotics   wearable sensors   machine learning   human factors   standards  

摘要： Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

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： 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.

关键词： anti-freezing hydrogel   diverse surface adhesion   human-machine interface   ionic skin   soft robotics   stretchable electronics  

摘要： Natural living systems such as wood frogs develop tissues composed of active hydrogels with cryoprotectants to survive in cold environments. Recently, hydrogels have been intensively studied to develop stretchable electronics for wearables and soft robots. However, regular hydrogels are inevitably frozen at the subzero temperature and easily dehydrated, and have weak surface adhesion. Herein, a novel hydrogel-based ionic skin (iSkin) capable of strain sensing is demonstrated with high toughness, high stretchability, excellent ambient stability, superior anti-freezing capability, and strong surface adhesion. The iSkin consists of a piece of ionically and covalently cross-linked tough hydrogel with a thin bioadhesive layer. With the addition of biocompatible cryoprotectant and electrolyte, the iSkin shows good conductivity in wide ranges of relative humidity (15-90%) and temperature (-95-25 degrees C). In addition, the iSkin can adhere firmly to diverse material surfaces under different conditions, including cloth fabric, skin, and elastomers, in both dry and wet conditions, at subzero temperature, and/or with dynamic movement. The iSkin is demonstrated for applications including strain sensing on both human body and winter coat, human-machine interaction, motion/deformation sensing on a soft gripper and a soft robot at extremely cold conditions. This work provides a new paradigm for developing high-performance artificial skins for wearable sensing and soft robotics.

关键词： Deep Reinforcement Learning (DRL)   Proximal Policy Optimisation (PPO)   Grad-CAM analysis   Domain randomisation   Robot welding  

摘要： Manufacturing companies are in constant need for improved agility. An adequate combination of speed, responsiveness, and business agility to cope with fluctuating raw material costs is essential for today's increasingly demanding markets. Agility in robots is key in operations requiring on-demand control of a robot's tool position and orientation, reducing or eliminating extra programming efforts. Vision-based perception using full-state or partial-state observations and learning techniques are useful to create truly adaptive industrial robots. We propose using a Deep Reinforcement Learning (DRL) approach to solve path-following tasks using a simplified virtual environment with domain randomisation to provide the agent with enough exploration and observation variability during the training to generate useful policies to be transferred to an industrial robot. We validated our approach using a KUKA KR16HW robot equipped with a Fronius GMAW welding machine. The path was manually drawn on two workpieces so the robot was able to perceive, learn and follow it during welding experiments. It was also found that small processing times due to motion prediction (3.5 ms) did not slow down the process, which resulted in smooth robot operations. The novel approach can be implemented onto different industrial robots to carry out different tasks requiring material deposition.

关键词： artificial intelligence   machine learning   deep learning   digital twin   digital shadow   Industry 4.0 & nbsp   sustainability   sustainable smart manufacturing   robotics   review  

摘要： Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human-robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.