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Date Added: Jan 24, 2022
Date Added: Jan 24, 2022
As more roboticists are turning to Nature for design inspiration, it is becoming increasingly apparent that multisystem-level investigations of biological processes can frequently lead to unexpected advances in the development of experimental research platforms. Inspired by these efforts, we present here a holistic approach to developing an autonomous starfish-inspired soft robot that embodies a number of key design, fabrication, and actuation principles. These key concepts include integrated and sequentially deployable magnetic tube feet for site-specific and reversible substrate attachment, individually addressable flexible arms, and highly efficient and self-contained fluidic engines. These individual features offer a level of synergistic motion control not previously seen in other starfish-inspired robots. For example, our bistable dome-like tube feet are capable of achieving high adhesion forces to ferrous surfaces and low removal forces. These tube feet are further integrated with a fluidic engine to drive the entire arm while maintaining the ability to accurately control the arm position with a 270° range of motion. Furthermore, the arm and fluidic engine are modular, allowing each of the five arms to be replaced in seconds or enabling the exploration of a variety of limb geometries. Through the incorporation of these different design elements, the ASTER-bot (named for its star-like body plan) is capable of locomotion on ferrous surfaces, above and below water, and on nonplanar surfaces. This article further describes the design, fabrication, and integration strategies and characterizes the energetic and locomotory performance of this pentaradial robotic prototype.
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Date Added: Jan 20, 2022
Date Added: Jan 20, 2022
Swarms of tiny flying robots hold great potential for exploring unknown, indoor environments. Their small size allows them to move in narrow spaces, and their light weight makes them safe for operating around humans. Until now, this task has been out of reach due to the lack of adequate navigation strategies. The absence of external infrastructure implies that any positioning attempts must be performed by the robots themselves. State-of-the-art solutions, such as simultaneous localization and mapping, are still too resource demanding. This article presents the swarm gradient bug algorithm (SGBA), a minimal navigation solution that allows a swarm of tiny flying robots to autonomously explore an unknown environment and subsequently come back to the departure point. SGBA maximizes coverage by having robots travel in different directions away from the departure point. The robots navigate the environment and deal with static obstacles on the fly by means of visual odometry and wall-following behaviors. Moreover, they communicate with each other to avoid collisions and maximize search efficiency. To come back to the departure point, the robots perform a gradient search toward a home beacon. We studied the collective aspects of SGBA, demonstrating that it allows a group of 33-g commercial off-the-shelf quadrotors to successfully explore a real-world environment. The application potential is illustrated by a proof-of-concept search-and-rescue mission in which the robots captured images to find “victims” in an office environment. The developed algorithms generalize to other robot types and lay the basis for tackling other similarly complex missions with robot swarms in the future.
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Date Added: Jan 4, 2022
Date Added: Jan 4, 2022
Navigation is a critical ability for animal survival and is important for food foraging, finding shelter, seeking mates and a variety of other behaviors. Given their fundamental role and universal function in the animal kingdom, it makes sense to explore whether space representation and navigation mechanisms are dependent on the species, ecological system, brain structures, or whether they share general and universal properties. One way to explore this issue behaviorally is by domain transfer methodology, where one species is embedded in another species’ environment and must cope with an otherwise familiar (in our case, navigation) task. Here we push this idea to the limit by studying the navigation ability of a fish in a terrestrial environment. For this purpose, we trained goldfish to use a Fish Operated Vehicle (FOV), a wheeled terrestrial platform that reacts to the fish’s movement characteristics, location and orientation in its water tank to change the vehicle’s; i.e., the water tank’s, position in the arena. The fish were tasked to “drive” the FOV towards a visual target in the terrestrial environment, which was observable through the walls of the tank, and indeed were able to operate the vehicle, explore the new environment, and reach the target regardless of the starting point, all while avoiding dead-ends and correcting location inaccuracies. These results demonstrate how a fish was able to transfer its space representation and navigation skills to a wholly different terrestrial environment, thus supporting the hypothesis that the former possess a universal quality that is species-independent.
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Date Added: Jan 23, 2022
Date Added: Jan 23, 2022
Machine learning (ML) is actively finding its way into modern cyber-physical
systems (CPS), many of which are safety-critical real-time systems. It is well
known that ML outputs are not reliable when testing data are novel with regards
to model training and validation data, i.e., out-of-distribution (OOD) test
data. We implement an unsupervised deep neural network-based OOD detector on a
real-time embedded autonomous Duckiebot and evaluate detection performance. Our
OOD detector produces a success rate of 87.5% for emergency stopping a
Duckiebot on a braking test bed we designed. We also provide case analysis on
computing resource challenges specific to the Robot Operating System (ROS)
middleware on the Duckiebot.
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Date Added: Jan 23, 2022
Date Added: Jan 23, 2022
Small soft robotic systems are being explored for myriad applications in medicine. Specifically, magnetically actuated microrobots capable of remote manipulation hold significant potential for the targeted delivery of therapeutics and biologicals. Much of previous efforts on microrobotics have been dedicated to locomotion in aqueous environments and hard surfaces. However, our human bodies are made of dense biological tissues, requiring researchers to develop new microrobotics that can locomote atop tissue surfaces. Tumbling microrobots are a sub-category of these devices capable of walking on surfaces guided by rotating magnetic fields. Using microrobots to deliver payloads to specific regions of sensitive tissues is a primary goal of medical microrobots. Central nervous system (CNS) tissues are a prime candidate given their delicate structure and highly region-specific function. Here we demonstrate surface walking of soft alginate capsules capable of moving on top of a rat cortex and mouse spinal cord ex vivo, demonstrating multi-location small molecule delivery to up to six different locations on each type of tissue with high spatial specificity. The softness of alginate gel prevents injuries that may arise from friction with CNS tissues during millirobot locomotion. Development of this technology may be useful in clinical and preclinical applications such as drug delivery, neural stimulation, and diagnostic imaging.
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Date Added: Dec 27, 2021
Link to article: https://iep.utm.edu/ethic-ai/“This article provides a comprehensive overview of the main ethical issues related to the impact of Artificial Intelligence (AI) on human society. AI is the use of machines to do things that would normally require human intelligence. In many areas of human life, AI has rapidly and significantly affected human society and the ways we interact with each other. It will continue to do so. Along the way, AI has presented substantial ethical and socio-political challenges that call for a thorough philosophical and ethical analysis. Its social impact should be studied so as to avoid any negative repercussions. AI systems are becoming more and more autonomous, apparently rational, and intelligent. This comprehensive development gives rise to numerous issues. In addition to the potential harm and impact of AI technologies on our privacy, other concerns include their moral and legal status (including moral and legal rights), their possible moral agency and patienthood, and issues related to their possible personhood and even dignity. It is common, however, to distinguish the following issues as of utmost significance with respect to AI and its relation to human society, according to three different time periods: (1) short-term (early 21st century): autonomous systems (transportation, weapons), machine bias in law, privacy and surveillance, the black box problem and AI decision-making; (2) mid-term (from the 2040s to the end of the century): AI governance, confirming the moral and legal status of intelligent machines (artificial moral agents), human-machine interaction, mass automation; (3) long-term (starting with the 2100s): technological singularity, mass unemployment, space colonisation.”
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Date Added: Dec 11, 2021
Date Added: Dec 11, 2021
Could robotization make the gender pay gap worse? We provide the first large-scale evidence on the impact of industrial robots on the gender pay gap using data from 20 European countries. We show that robot adoption increases both male and female earnings but also increases the gender pay gap. Using an instrumental variable strategy, we find that a ten percent increase in robotization leads to a 1.8% increase in the gender pay gap. These results are driven by countries with high initial levels of gender inequality and can be explained by the fact that men at medium- and high-skill occupations disproportionately benefit from robotization, through a productivity effect. We rule out the possibility that our results are driven by mechanical changes in the gender composition of the workforce.
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Date Added: Jan 13, 2022
Date Added: Jan 13, 2022
The outbreak of the COVID-19 pandemic is unarguably the biggest catastrophe of the 21st century, probably the most significant global crisis after the second world war. The rapid spreading capability of the virus has compelled the world population to maintain strict preventive measures. The outrage of the virus has rampaged through the healthcare sector tremendously. This pandemic created a huge demand for necessary healthcare equipment, medicines along with the requirement for advanced robotics and artificial intelligence-based applications. The intelligent robot systems have great potential to render service in diagnosis, risk assessment, monitoring, telehealthcare, disinfection, and several other operations during this pandemic which has helped reduce the workload of the frontline workers remarkably. The long-awaited vaccine discovery of this deadly virus has also been greatly accelerated with AI-empowered tools. In addition to that, many robotics and Robotics Process Automation platforms have substantially facilitated the distribution of the vaccine in many arrangements pertaining to it. These forefront technologies have also aided in giving comfort to the people dealing with less addressed mental health complicacies. This paper investigates the use of robotics and artificial intelligence-based technologies and their applications in healthcare to fight against the COVID-19 pandemic. A systematic search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method is conducted to accumulate such literature, and an extensive review on 147 selected records is performed.
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Date Added: Jan 17, 2022
Date Added: Jan 17, 2022
Aerial robots the size of a honeybee (∼100 mg) have advantages over larger robots because of their small size, low mass and low materials cost. Previous iterations have demonstrated controlled flight but were difficult to fabricate because they consisted of many separate parts assembled together. They also were unable to perform locomotion modes besides flight. This paper presents a new design of a 74 mg flapping-wing robot that dramatically reduces the number of parts and simplifies fabrication. It also has a lower center of mass, which allows the robot to additionally land without the need for long legs, even in case of unstable flight. Furthermore, we show that the new design allows for wing-driven ground and air-water interfacial locomotion, improving the versatility of the robot. Forward thrust is generated by increasing the speed of downstroke relative to the upstroke of the flapping wings. This also allows for steering. The ability to land and subsequently move along the ground allows the robot to negotiate extremely confined spaces, underneath obstacles, and to precise locations. We describe the new design in detail and present results demonstrating these capabilities, as well as hovering flight and controlled landing.
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Date Added: Nov 1, 2021
Date Added: Nov 1, 2021
Sensors that can detect external stimuli and perceive the surrounding areas could offer an ability for soft biomimetic robots to use the sensory feedback for closed-loop control of locomotion. Although various types of biomimetic robots have been developed, few systems have included integrated stretchable sensors and interconnectors with miniaturized electronics. Here, we introduce a soft, stretchable nanocomposite system with built-in wireless electronics with an aim for feedback–loop motion control of a robotic earthworm. The nanostructured strain sensor, based on a carbon nanomaterial and a low-modulus silicone elastomer, allows for seamless integration with the body of the soft robot that can accommodate large strains caused by bending, stretching, and physical interactions with obstacles. A scalable, cost-effective, and screen-printing method manufactures an array of the strain sensors that are conductive and stretchable over 100% with a gauge factor over 38. An array of nanomembrane interconnectors enables a reliable connection between soft sensors and wireless electronics while tolerating the robot’s multimodal movements. A set of computational and experimental studies of soft materials, stretchable mechanics, and hybrid packaging provides the key design factors for a reliable, nanocomposite sensor system. The miniaturized wireless circuit, embedded in the robot joint, offers real-time monitoring of strain changes during the motions of a robotic segment. Collectively, the soft sensor system presented in this work shows great potential to be integrated with other flexible, stretchable electronics for applications in soft robotics, wearable devices, and human-machine interfaces.
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