Autonomous outdoor navigation requires reliable multisensory fusion strategies. Desert ants travel widely every day, showing unrivaled navigation performance using only a few thousand neurons. In the desert, pheromones are instantly destroyed by the extreme heat. To navigate safely in this hostile environment, desert ants assess their heading from the polarized pattern of skylight and judge the distance traveled based on both a stride-counting method and the optic flow, i.e., the rate at which the ground moves across the eye. This process is called path integration (PI). Although many methods of endowing mobile robots with outdoor localization have been developed recently, most of them are still prone to considerable drift and uncertainty. We tested several ant-inspired solutions to outdoor homing navigation problems on a legged robot using two optical sensors equipped with just 14 pixels, two of which were dedicated to an insect-inspired compass sensitive to ultraviolet light. When combined with two rotating polarized filters, this compass was equivalent to two costly arrays composed of 374 photosensors, each of which was tuned to a specific polarization angle. The other 12 pixels were dedicated to optic flow measurements. Results show that our ant-inspired methods of navigation give precise performances. The mean homing error recorded during the overall trajectory was as small as 0.67% under lighting conditions similar to those encountered by ants. These findings show that ant-inspired PI strategies can be used to complement classical techniques with a high level of robustness and efficiency.
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.
The human hand is capable of performing countless grasps and gestures that are the basis for social activities. However, which grasps contribute the most to the manipulation skills needed during collaborative tasks, and thus which grasps should be included in a robot companion, is still an open issue. Here, we investigated grasp choice and hand placement on objects during a handover when subsequent tasks are performed by the receiver and when in-hand and bimanual manipulation are not allowed. Our findings suggest that, in this scenario, human passers favor precision grasps during such handovers. Passers also tend to grasp the purposive part of objects and leave “handles” unobstructed to the receivers. Intuitively, this choice allows receivers to comfortably perform subsequent tasks with the objects. In practice, many factors contribute to a choice of grasp, e.g., object and task constraints. However, not all of these factors have had enough emphasis in the implementation of grasping by robots, particularly the constraints introduced by a task, which are critical to the success of a handover. Successful robotic grasping is important if robots are to help humans with tasks. We believe that the results of this work can benefit the wider robotics community, with applications ranging from industrial cooperative manipulation to household collaborative manipulation.
The trend toward smaller mechanism footprints and volumes, while maintaining the ability to perform complex tasks, presents the opportunity for exploration of hypercompact mechanical systems integrated with curved surfaces. Developable surfaces are shapes that a flat sheet can take without tearing or stretching, and they represent a wide range of manufactured surfaces. This work introduces “developable mechanisms” as devices that emerge from or conform to developable surfaces. They are made possible by aligning hinge axes with developable surface ruling lines to enable mobility. Because rigid-link motion depends on the relative orientation of hinge axes and not link geometry, links can take the shape of the corresponding developable surface. Mechanisms are classified by their associated surface type, and these relationships are defined and demonstrated by example. Developable mechanisms show promise for meeting unfilled needs using systems not previously envisioned.
A robotic revolution will allow the world to produce much more food more sustainably.
HBO’s Westworld is located to the right of the uncanny valley, near the archipelago of governments proposing to regulate sexbots and strong artificial intelligence.
Dielectric elastomer actuators (DEAs) are a promising enabling technology for a wide range of emerging applications, including robotics, artificial muscles, and microfluidics. This is due to their large actuation strains, rapid response rate, low cost and low noise, high energy density, and high efficiency when compared with alternative actuators. These properties make DEAs ideal for the actuation of soft submersible devices, although their use has been limited because of three main challenges: (i) developing suitable, compliant electrode materials; (ii) the need to effectively insulate the actuator electrodes from the surrounding fluid; and (iii) the rigid frames typically required to prestrain the dielectric layers. We explored the use of a frameless, submersible DEA design that uses an internal chamber filled with liquid as one of the electrodes and the surrounding environmental liquid as the second electrode, thus simplifying the implementation of soft, actuated submersible devices. We demonstrated the feasibility of this approach with a prototype swimming robot composed of transparent bimorph actuator segments and inspired by transparent eel larvae, leptocephali. This design achieved undulatory swimming with a maximum forward swimming speed of 1.9 millimeters per second and a Froude efficiency of 52%. We also demonstrated the capability for camouflage and display through the body of the robot, which has an average transmittance of 94% across the visible spectrum, similar to a leptocephalus. These results suggest a potential for DEAs with fluid electrodes to serve as artificial muscles for quiet, translucent, swimming soft robots for applications including surveillance and the unobtrusive study of marine life.
The scope of research on exoskeletal cyborg-type robots has progressed far beyond mechanisms for maneuvering piloted mecha as imagined by science fiction, and cybernics—the fusion of humans, robots, and information systems—is shaping the way toward novel methods of medical care.
A barrier to practical use of electrotactile stimulation for haptic feedback has been large variability in perceived sensation intensity because of changes in the impedance of the electrode-skin interface, such as when electrodes peel or users sweat. We show how to significantly reduce this variability by modulating stimulation parameters in response to measurements of impedance. Our method derives from three contributions. First, we created a model between stimulation parameters and impedance at constant perceived sensation intensity by looking at the peak pulse energy and phase charge. Our model fits experimental data better than previous models [mean correlation coefficient (r2) > 0.9] and holds over a larger set of conditions (participants, sessions, magnitudes of sensation, stimulation locations, and electrode sizes). Second, we implemented a controller that regulates perceived sensation intensity by using our model to derive a new current amplitude and pulse duration in response to changes in impedance. Our controller accurately predicts participant-chosen stimulation parameters at constant sensation intensity (mean r2 > 0.9). Third, we demonstrated as a proof of concept on two participants with below-elbow amputations—using a prosthesis with electrotactile touch feedback—that our controller can regulate sensation intensity in response to large impedance changes that occur in activities of daily living. These results make electrotactile stimulation for human-machine interfaces more reliable during activities of daily living.