Human-robot collaboration in manufacturing: Quantitative evaluation of predictable, convergent joint action

New industrial robotic systems that operate in the same physical space as people highlight the emerging need for robots that can integrate seamlessly into human group dynamics. In this paper we build on our prior investigation, which evaluates the convergence of a robot computational teaming model and a human teammate's mental model, by computing the entropy rate of the Markov chain. We present and analyze the six out of thirty-six human trials where the human participant switched execution strategies while working with the robot. We conduct a post-hoc analysis of this dataset and show that the entropy rate appears to be sensitive to changes in the human strategy and reflects the resulting increase in uncertainty about the human next actions. We propose that these results provide first support that entropy rate may be used as a component of dynamic risk assessment, to generate risk-aware robot motions and action selections

A two-phase gripper to reorient and grasp

This paper introduces the design of novel two-phase fingers to passively reorient objects while picking them up. Two-phase refers to a change in the finger-object contact geometry, from a free spinning point contact to a firm multipoint contact, as the gripping force increases. We exploit the two phases to passively reorient prismatic objects from a horizontal resting pose to an upright secure grasp. This problem is particularly relevant to industrial assembly applications where parts often are presented lying on trays or conveyor belts and need to be assembled vertically. Each two-phase finger is composed of a small hard contact point attached to an elastic strip mounted over a V-groove cavity. When grasped between two parallel fingers with low gripping force, the object pivots about the axis between the contact points on the strips, and aligns upright with gravity. A subsequent increase in the gripping force makes the elastic strips recede into the cavities letting the part seat in the V-grooves to secure the grasp. The design is compatible with any type of parallel-jaw gripper, and can be reconfigured to specific objects by changing the geometry of the cavity. The two-phase gripper provides robots with the capability to accurately position and manipulate parts, reducing the need for dedicated part feeders or time-demanding regrasp procedures.National Science Foundation (U.S.). National Robotics Initiative (NSF-IIS-1427050

High probability neurotransmitter release sites represent an energy efficient design

At most synapses, the probability of neurotransmitter release from an active zone (PAZ) is low, a design thought to confer many advantages. Yet, high PAZ can also be found at synapses. Speculating that high PAZ confers high energy efficiency, we examined energy efficiency at terminals of two Drosophila motor neurons (MNs) synapsing on the same muscle fiber, but with contrasting average PAZ. Through electrophysiological and ultrastructural measurements we calculated average PAZ for MNSNb/d-Is and MN6-Ib terminals (0.33±0.10 and 0.11±0.02 respectively). Using a miles-per-gallon analogy, we calculated efficiency as the number of glutamate molecules released for each ATP molecule that powers the release and recycling of glutamate and the removal of calcium (Ca2+) and sodium (Na+). Ca2+ and Na+ entry were calculated by microfluorimetry and morphological measurements respectively. Terminals with the highest PAZ release more glutamate but admit less Ca2+ and Na+, supporting the hypothesis that high PAZ confers greater energy efficiency than low PAZ (0.13±0.02 and 0.06±0.01 respectively). In an analytical treatment of parameters that influence efficiency we found that efficiency could be optimized in either terminal by increasing PAZ. Terminals with highest PAZ operate closest to this optimum but are less active and less able to sustain high release rates. Adopting an evolutionary biological perspective, we interpret the persistence of low PAZ release sites at more active terminals to be the result of selection pressures for sustainable neurotransmitter release dominating selection pressures for high energy efficiency

Toward safe close-proximity human-robot interaction with standard industrial robots

Allowing humans and robots to interact in close proximity to each other has great potential for increasing the effectiveness of human-robot teams across a large variety of domains. However, as we move toward enabling humans and robots to interact at ever-decreasing distances of separation, effective safety technologies must also be developed. While new, inherently human-safe robot designs have been established, millions of industrial robots are already deployed worldwide, which makes it attractive to develop technologies that can turn these standard industrial robots into human-safe platforms. In this work, we present a real-time safety system capable of allowing safe human-robot interaction at very low distances of separation, without the need for robot hardware modification or replacement. By leveraging known robot joint angle values and accurate measurements of human positioning in the workspace, we can achieve precise robot speed adjustment by utilizing real-time measurements of separation distance. This, in turn, allows for collision prevention in a manner comfortable for the human user.We demonstrate our system achieves latencies below 9.64 ms with 95% probability, 11.10 ms with 99% probability, and 14.08 ms with 99.99% probability, resulting in robust real-time performance.ABB Grou

Expression of multiple transgenes from a single construct using viral 2A peptides in Drosophila.

Expression of multiple reporter or effector transgenes in the same cell from a single construct is increasingly necessary in various experimental paradigms. The discovery of short, virus-derived peptide sequences that mediate a ribosome-skipping event enables generation of multiple separate peptide products from one mRNA. Here we describe methods and vectors to facilitate easy production of polycistronic-like sequences utilizing these 2A peptides tailored for expression in Drosophila both in vitro and in vivo. We tested the separation efficiency of different viral 2A peptides in cultured Drosophila cells and in vivo and found that the 2A peptides from porcine teschovirus-1 (P2A) and Thosea asigna virus (T2A) worked best. To demonstrate the utility of this approach, we used the P2A peptide to co-express the red fluorescent protein tdTomato and the genetically-encoded calcium indicator GCaMP5G in larval motorneurons. This technique enabled ratiometric calcium imaging with motion correction allowing us to record synaptic activity at the neuromuscular junction in an intact larval preparation through the cuticle. The tools presented here should greatly facilitate the generation of 2A peptide-mediated expression of multiple transgenes in Drosophila

Spatially-optimized urban greening for reduction of population exposure to land surface temperature extremes

The population experiencing high temperatures in cities is rising due to anthropogenic climate change, settlement expansion, and population growth. Yet, efficient tools to evaluate potential intervention strategies to reduce population exposure to Land Surface Temperature (LST) extremes are still lacking. Here, we implement a spatial regression model based on remote sensing data that is able to assess the population exposure to LST extremes in urban environments across 200 cities based on surface properties like vegetation cover and distance to water bodies. We define exposure as the number of days per year where LST exceeds a given threshold multiplied by the total urban population exposed, in person ⋅ day. Our findings reveal that urban vegetation plays a considerable role in decreasing the exposure of the urban population to LST extremes. We show that targeting high-exposure areas reduces vegetation needed for the same decrease in exposure compared to uniform treatment

A spatial regression model to measure the urban population exposure to extreme heat

Temperatures are rising and the frequency of heat waves is increasing due to anthropogenic climate change. At the same time, the population in urban areas is rapidly growing. As a result, an ever-larger part of humankind will be exposed to even greater heat stress from heat waves in urban areas in the future. In this research, we focus on studying the determinants of land surface temperature (LST) gradients in urban environments. We implement a spatial regression model that is able to predict with high accuracy (R2 > 0.9 in the test phase of k-fold cross-validation) the LST of urban environments across 200 cities based on land surface properties like vegetation, built-up areas, and distance to water bodies, without any additional climate information. We show that, on average, by increasing the overall urban vegetation by 3%, it would be possible to reduce by 50% the exposure of the urban population that lives in the warmest areas of the cities for the average of the three summer months, achieving a reduction of 1 K in LST. By coupling the model information with the population layer, we show that an 11% increase in urban vegetation is necessary in order to obtain a reduction of 1 K in the most populated areas, where at least 50% of the population live. We finally discuss the challenges and the limitations of greening interventions in the context of available surfaces in urban areas