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Truly collaborative scientific field data collection between human scientists and autonomous robot systems requires a shared understanding of the search objectives and tradeoffs faced when making decisions. Therefore, critical to developing intelligent robots to aid human experts is an understanding of how scientists make such decisions and how they adapt their data collection strategies when presented with new informationin situ. In this study, we examined the dynamic data collection decisions of 108 expert geoscience researchers using a simulated field scenario. Human data collection behaviors suggested two distinct objectives: an information-based objective to maximize information coverage and a discrepancy-based objective to maximize hypothesis verification. We developed a highly simplified quantitative decision model that allows the robot to predict potential human data collection locations based on the two observed human data collection objectives. Predictions from the simple model revealed a transition from information-based to discrepancy-based objective as the level of information increased. The findings will allow robotic teammates to connect experts’ dynamic science objectives with the adaptation of their sampling behaviors and, in the long term, enable the development of more cognitively compatible robotic field assistants.

 

Moving down a hillslope from ridge to valley, soil develops and becomes increasingly weathered. Downslope variation in clay content, organic matter, and porosity should produce concomitant changes in soil strength that influence slope stability and erosion. This has yet to be demonstrated, however, because in situ measurements of soil rheology are challenging and rare. Here we employ a robotic leg as a mechanically sensitive and time‐efficient penetrometer to map soil strength along a canonical temperate hillslope profile. We observe a systematic downslope weakening, and increasing heterogeneity, of soil strength associated with a transition from sand‐rich ridge materials to cohesive valley bottom soil aggregates. Weathering‐induced changes in soil composition lead to physically distinct mechanical behaviors in cohesive soils that depart from the behavior observed for sand. We also demonstrate the promise that legged robots may use their limbs to sense and improve mobility in complex environments, with implications for planetary exploration.

 

Abstract How do scientists generate and weight candidate queries for hypothesis testing, and how does learning from observations or experimental data impact query selection? Field sciences offer a compelling context to ask these questions because query selection and adaptation involves consideration of the spatiotemporal arrangement of data, and therefore closely parallels classic search and foraging behavior. Here we conduct a novel simulated data foraging study—and a complementary real-world case study—to determine how spatiotemporal data collection decisions are made in field sciences, and how search is adapted in response to in-situ data. Expert geoscientists evaluated a hypothesis by collecting environmental data using a mobile robot. At any point, participants were able to stop the robot and change their search strategy or make a conclusion about the hypothesis. We identified spatiotemporal reasoning heuristics, to which scientists strongly anchored, displaying limited adaptation to new data. We analyzed two key decision factors: variable-space coverage, and fitting error to the hypothesis. We found that, despite varied search strategies, the majority of scientists made a conclusion as the fitting error converged. Scientists who made premature conclusions, due to insufficient variable-space coverage or before the fitting error stabilized, were more prone to incorrect conclusions. We found that novice undergraduates used the same heuristics as expert geoscientists in a simplified version of the scenario. We believe the findings from this study could be used to improve field science training in data foraging, and aid in the development of technologies to support data collection decisions. 

Natural environments are often filled with obstacles and disturbances. Traditional navigation and planning approaches normally depend on finding a traversable “free space” for robots to avoid unexpected contact or collision. We hypothesize that with a better understanding of the robot–obstacle interactions, these collisions and disturbances can be exploited as opportunities to improve robot locomotion in complex environments. In this article, we propose a novel obstacle disturbance selection (ODS) framework with the aim of allowing robots to actively select disturbances to achieve environment-aided locomotion. Using an empirically characterized relationship between leg–obstacle contact position and robot trajectory deviation, we simplify the representation of the obstacle-filled physical environment to a horizontal-plane disturbance force field. We then treat each robot leg as a “disturbance force selector” for prediction of obstacle-modulated robot dynamics. Combining the two representations provides analytical insights into the effects of gaits on legged traversal in cluttered environments. We illustrate the predictive power of the ODS framework by studying the horizontal-plane dynamics of a quadrupedal robot traversing an array of evenly-spaced cylindrical obstacles with both bounding and trotting gaits. Experiments corroborate numerical simulations that reveal the emergence of a stable equilibrium orientation in the face of repeated obstacle disturbances. The ODS reduction yields closed-form analytical predictions of the equilibrium position for different robot body aspect ratios, gait patterns, and obstacle spacings. We conclude with speculative remarks bearing on the prospects for novel ODS-based gait control schemes for shaping robot navigation in perturbation-rich environments. 

Predicting the susceptibility of soil to wind erosion is difficult because it is a multivariate function of grain size, soil moisture, compaction, and biological growth. Erosive agents like plowing and grazing also differ in mechanism from entrainment by fluid shear; it is unclear if and how erosion thresholds for each process are related. Here we demonstrate the potential to rapidly assemble empirical maps of erodibility while also examining what controls it, using a novel “plowing” test of surface‐soil shear resistance (τ_r) performed by a semi‐autonomous robot. Field work at White Sands National Monument, New Mexico, United States, examined gradients in erodibility at two scales: (i) soil moisture changes from dry dune crest to wet interdune (tens of meters) and (ii) downwind‐increasing dune stabilization associated with growth of plants and salt and biological crusts (kilometers). We found that soil moisture changes of a few percent corresponded to a doubling ofτ_r, a result confirmed by laboratory experiments, and that soil crusts conferred stability that was comparable to moisture effects. We then compared different mechanisms of mechanical perturbation in a controlled laboratory setting. A new “kick‐out” test determines peak shear resistance of the surface soil as a proxy for yield strength. Kick‐out resistance exhibited a relation with soil moisture that was distinct from the plowing test and that was correlated with the independently measured threshold‐fluid stress for wind erosion. Results show that our new method maps soil erodibility in arid environments and provides an understanding of environmental controls on variations in soil erodibility.