Reconfigurable magnetic microrobot swarm: Multimode transformation, locomotion, and manipulation

Swimming microrobots that are energized by external magnetic fields exhibit a variety of intriguing collective behaviors, ranging from dynamic self-organization to coherent motion; however, achieving multiple, desired collective modes within one colloidal system to emulate high environmental adaptability and enhanced tasking capabilities of natural swarms is challenging. Here, we present a strategy that uses alternating magnetic fields to program hematite colloidal particles into liquid, chain, vortex, and ribbon-like microrobotic swarms and enables fast and reversible transformations between them. The chain is characterized by passing through confined narrow channels, and the herring school–like ribbon procession is capable of large-area synchronized manipulation, whereas the colony-like vortex can aggregate at a high density toward coordinated handling of heavy loads. Using the developed discrete particle simulation methods, we investigated generation mechanisms of these four swarms, as well as the “tank-treading” motion of the chain and vortex merging. In addition, the swarms can be programmed to steer in any direction with excellent maneuverability, and the vortex’s chirality can be rapidly switched with high pattern stability. This reconfigurable microrobot swarm can provide versatile collective modes to address environmental variations or multitasking requirements; it has potential to investigate fundamentals in living systems and to serve as a functional bio-microrobot system for biomedicine.

Source: Sciencemag.org – Science Robotics Latest Content

Robots mediating interactions between animals for interspecies collective behaviors

Self-organized collective behavior has been analyzed in diverse types of gregarious animals. Such collective intelligence emerges from the synergy between individuals, which behave at their own time and spatial scales and without global rules. Recently, robots have been developed to collaborate with animal groups in the pursuit of better understanding their decision-making processes. These biohybrid systems make cooperative relationships between artificial systems and animals possible, which can yield new capabilities in the resulting mixed group. However, robots are currently tailor-made to successfully engage with one animal species at a time. This limits the possibilities of introducing distinct species-dependent perceptual capabilities and types of behaviors in the same system. Here, we show that robots socially integrated into animal groups of honeybees and zebrafish, each one located in a different city, allowing these two species to interact. This interspecific information transfer is demonstrated by collective decisions that emerge between the two autonomous robotic systems and the two animal groups. The robots enable this biohybrid system to function at any distance and operates in water and air with multiple sensorimotor properties across species barriers and ecosystems. These results demonstrate the feasibility of generating and controlling behavioral patterns in biohybrid groups of multiple species. Such interspecies connections between diverse robotic systems and animal species may open the door for new forms of artificial collective intelligence, where the unrivaled perceptual capabilities of the animals and their brains can be used to enhance autonomous decision-making, which could find applications in selective “rewiring” of ecosystems.

Source: Sciencemag.org – Science Robotics Latest Content

Intracellular manipulation and measurement with multipole magnetic tweezers

The capability to directly interrogate intracellular structures inside a single cell for measurement and manipulation is important for understanding subcellular and suborganelle activities, diagnosing diseases, and developing new therapeutic approaches. Compared with measurements of single cells, physical measurement and manipulation of subcellular structures and organelles remain underexplored. To improve intracellular physical measurement and manipulation, we have developed a multipole magnetic tweezers system for micromanipulation involving submicrometer position control and piconewton force control of a submicrometer magnetic bead inside a single cell for measurement in different locations (spatial) and different time points (temporal). The bead was three-dimensionally positioned in the cell using a generalized predictive controller that addresses the control challenge caused by the low bandwidth of visual feedback from high-resolution confocal imaging. The average positioning error was quantified to be 0.4 μm, slightly larger than the Brownian motion–imposed constraint (0.31 μm). The system is also capable of applying a force up to 60 pN with a resolution of 4 pN for a period of time longer than 30 min. The measurement results revealed that significantly higher stiffness exists in the nucleus’ major axis than in the minor axis. This stiffness polarity is likely attributed to the aligned actin filament. We also showed that the nucleus stiffens upon the application of an intracellularly applied force, which can be attributed to the response of structural protein lamin A/C and the intracellular stress fiber actin filaments.

Source: Sciencemag.org – Science Robotics Latest Content

A review of collective robotic construction

The increasing need for safe, inexpensive, and sustainable construction, combined with novel technological enablers, has made large-scale construction by robot teams an active research area. Collective robotic construction (CRC) specifically concerns embodied, autonomous, multirobot systems that modify a shared environment according to high-level user-specified goals. CRC tightly integrates architectural design, the construction process, mechanisms, and control to achieve scalability and adaptability. This review gives a comprehensive overview of research trends, open questions, and performance metrics.

Source: Sciencemag.org – Science Robotics Latest Content

Perching and resting–A paradigm for UAV maneuvering with modularized landing gears

Perching helps small unmanned aerial vehicles (UAVs) extend their time of operation by saving battery power. However, most strategies for UAV perching require complex maneuvering and rely on specific structures, such as rough walls for attaching or tree branches for grasping. Many strategies to perching neglect the UAV’s mission such that saving battery power interrupts the mission. We suggest enabling UAVs with the capability of making and stabilizing contacts with the environment, which will allow the UAV to consume less energy while retaining its altitude, in addition to the perching capability that has been proposed before. This new capability is termed “resting.” For this, we propose a modularized and actuated landing gear framework that allows stabilizing the UAV on a wide range of different structures by perching and resting. Modularization allows our framework to adapt to specific structures for resting through rapid prototyping with additive manufacturing. Actuation allows switching between different modes of perching and resting during flight and additionally enables perching by grasping. Our results show that this framework can be used to perform UAV perching and resting on a set of common structures, such as street lights and edges or corners of buildings. We show that the design is effective in reducing power consumption, promotes increased pose stability, and preserves large vision ranges while perching or resting at heights. In addition, we discuss the potential applications facilitated by our design, as well as the potential issues to be addressed for deployment in practice.

Source: Sciencemag.org – Science Robotics Latest Content

A closed-loop hand prosthesis with simultaneous intraneural tactile and position feedback

Current myoelectric prostheses allow transradial amputees to regain voluntary motor control of their artificial limb by exploiting residual muscle function in the forearm. However, the overreliance on visual cues resulting from a lack of sensory feedback is a common complaint. Recently, several groups have provided tactile feedback in upper limb amputees using implanted electrodes, surface nerve stimulation, or sensory substitution. These approaches have led to improved function and prosthesis embodiment. Nevertheless, the provided information remains limited to a subset of the rich sensory cues available to healthy individuals. More specifically, proprioception, the sense of limb position and movement, is predominantly absent from current systems. Here, we show that sensory substitution based on intraneural stimulation can deliver position feedback in real time and in conjunction with somatotopic tactile feedback. This approach allowed two transradial amputees to regain high and close-to-natural remapped proprioceptive acuity, with a median joint angle reproduction precision of 9.1° and a median threshold to detection of passive movements of 9.5°, which was comparable with results obtained in healthy participants. The simultaneous delivery of position information and somatotopic tactile feedback allowed both amputees to discriminate the size and compliance of four objects with high levels of performance (75.5%). These results demonstrate that tactile information delivered via somatotopic neural stimulation and position information delivered via sensory substitution can be exploited simultaneously and efficiently by transradial amputees. This study paves a way to more sophisticated bidirectional bionic limbs conveying richer, multimodal sensations.

Source: Sciencemag.org – Science Robotics Latest Content

When a robot teaches humans: Automated feedback selection accelerates motor learning

A multitude of robotic systems have been developed to foster motor learning. Some of these robotic systems featured augmented visual or haptic feedback, which was automatically adjusted to the trainee’s performance. However, selecting the type of feedback to achieve the training goal usually remained up to a human trainer. We automated this feedback selection within a robotic rowing simulator: Four spatial errors and one velocity error were considered, all related to trunk-arm sweep rowing set as the training goal to be learned. In an alternating sequence of assessments without augmented feedback and training sessions with augmented, concurrent feedback, the experimental group received feedback, thus addressing the main shortcoming of the previous assessment. With this approach, each participant of the experimental group received an individual sequence of 10 training sessions with feedback. The training sequences from participants in the experimental group were consecutively applied for participants in the control group. Both groups were able to reduce spatial and velocity errors due to training. The learning rate of the requested velocity profile was significantly higher for the experimental group compared with the control group. Thus, our robotic rowing simulator accelerated motor learning by automated feedback selection. This demonstration of a working, closed-loop selection of types of feedback, i.e., training conditions, could serve as the basis for other robotic trainers incorporating further human expertise and artificial intelligence.

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Restoring tactile sensations via neural interfaces for real-time force-and-slippage closed-loop control of bionic hands

Despite previous studies on the restoration of tactile sensation to the fingers and the hand, there are no examples of use of the routed sensory information to finely control a prosthestic hand in complex grasp and manipulation tasks. Here, it is shown that force and slippage sensations can be elicited in an amputee by means of biologically inspired slippage detection and encoding algorithms, supported by a stick-slip model of the performed grasp. A combination of cuff and intraneural electrodes was implanted for 11 weeks in a young woman with hand amputation and was shown to provide close-to-natural force and slippage sensations, paramount for substantially improving manipulative skills with the prosthesis. Evidence is provided about the improvement of the participant’s grasping and manipulation capabilities over time resulting from neural feedback. The elicited tactile sensations enabled the successful fulfillment of fine grasp and manipulation tasks with increasing complexity. Grasp performance was quantitatively assessed by means of instrumented objects and a purposely developed metrics. Closed-loop control capabilities enabled by the neural feedback were compared with those achieved without feedback. Further, the work demonstrates that the described amelioration of motor performance in dexterous tasks had as central neurophysiological correlates changes in motor cortical plasticity and that such changes were not of purely motor origin, but were the effect of a strong and persistent drive of the sensory feedback.

Source: Sciencemag.org – Science Robotics Latest Content

Toward adaptive robotic sampling of phytoplankton in the coastal ocean

Currents, wind, bathymetry, and freshwater runoff are some of the factors that make coastal waters heterogeneous, patchy, and scientifically interesting—where it is challenging to resolve the spatiotemporal variation within the water column. We present methods and results from field experiments using an autonomous underwater vehicle (AUV) with embedded algorithms that focus sampling on features in three dimensions. This was achieved by combining Gaussian process (GP) modeling with onboard robotic autonomy, allowing volumetric measurements to be made at fine scales. Special focus was given to the patchiness of phytoplankton biomass, measured as chlorophyll a (Chla), an important factor for understanding biogeochemical processes, such as primary productivity, in the coastal ocean. During multiple field tests in Runde, Norway, the method was successfully used to identify, map, and track the subsurface chlorophyll a maxima (SCM). Results show that the algorithm was able to estimate the SCM volumetrically, enabling the AUV to track the maximum concentration depth within the volume. These data were subsequently verified and supplemented with remote sensing, time series from a buoy and ship-based measurements from a fast repetition rate fluorometer (FRRf), particle imaging systems, as well as discrete water samples, covering both the large and small scales of the microbial community shaped by coastal dynamics. By bringing together diverse methods from statistics, autonomous control, imaging, and oceanography, the work offers an interdisciplinary perspective in robotic observation of our changing oceans.

Source: Sciencemag.org – Science Robotics Latest Content