Although many soft robots, capable of crawling or climbing, have been well developed, integrating multimodal locomotion into a soft robot for transitioning between crawling and climbing still remains elusive. In this work, we present a class of inchworm-inspired multimodal soft crawling-climbing robots (SCCRs) that can achieve crawling, climbing, and transitioning between horizontal and vertical planes. Inspired by the inchworm’s multimodal locomotion, which depends on the “ $\Omega$ ” deformation of the body and controllable friction force of feet, we develop the SCCR by 1) three pneumatic artificial muscles based body designed to produce “ $\Omega$ ” deformation; 2) two negative pressure suckers adopted to generate controllable friction forces. Then a simplified kinematic model is developed to characterize the kinematic features of the SCCRs. Lastly, a control strategy is proposed to synchronously control the “ $\Omega$ ” deformation and sucker friction forces for multimodal locomotion. The experimental results demonstrate that the SCCR can move at a maximum speed of 21 mm/s (0.11 body length/s) on horizontal planes and 15 mm/s (0.079 body length/s) on vertical walls. Furthermore, the SCCR can work in confined spaces, carry a payload of 500 g (about 15 times the self-weight) on horizontal planes or 20 g on vertical walls, and move in aquatic environments.

Summary: In the exploitation of subsurface hydrocarbon and geothermal energy, more than 50% of total expenses are typically attributed to drilling and well completion. Coupled triaxial vibrations in the drilling system, excited by various mechanical, hydraulic, and geological sources, are major causes of premature downhole tool failures, excessive bit damage, compromised hole quality, reduced rate of penetration (ROP), and increased nonproductive time. Drillstring dynamic models, shock absorption tools, and controllers have been developed since the 1960s to understand and mitigate detrimental vibrations in the drilling system, aiming for smoother and more efficient operations. With advancements in measurement/logging while drilling technologies, rig equipment, mud pulse/wired pipe telemetry, and the digitalization of drilling engineering, a foundation has been established for proactive drilling vibration detection, analysis, and control to achieve efficient and safe drilling. In this study, a reduced-order axial-torsional drillstring dynamic model with soft string lateral contacts is first developed. Nonlinear drilling boundary conditions and inputs, such as bit-rock interactions (BRIs) with torque-on-bit (TOB) velocity-weakening effects, wellbore-drillstring contacts/friction, drillstring gravity, mud buoyancy, and more, are defined within the modeling framework. Equilibrium states and linearization of the model are derived for any given weight-on-bit (WOB) and rotational speed (RPM) setpoints, 3D well trajectories, and drillstring dimensions. A linear-quadratic-integral (LQI) controller is developed and tested for drillstring vibration suppression using a 6,562 ft (2000 m) drillstring model. In comparison to commercially available controllers for stick/slip mitigation, which are designed based on impedance matching and only address the decoupled drillstring torsional dynamics, the proposed state-feedback LQI controller emphasizes the coupling between the axial and torsional drillstring dynamics by simultaneously controlling WOB and RPM. Simulation results demonstrate that the LQI controller can suppress stick/slip and stabilize downhole WOB within 7 seconds, while a commercial drilling controller takes more than 35 seconds to achieve the same. Additionally, valuable insights and potential future directions for nonlinear drillstring vibration mitigation and drilling controller design are provided. With its fast drilling dysfunction stabilization time, small downhole RPM/WOB overshoots, minimal rig control inputs, robustness against drilling nonlinearities, and high efficiency, the proposed multi-input-multi-output state-feedback drilling controller holds great potential for application in proactive drilling vibration control, drilling automation, and real-time drilling optimization.

Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process.