Grasping and manipulating uncooperative objects in space is an emerging challenge for robotic systems. Many traditional robotic grasping techniques used on Earth are infeasible in space. Vacuum grippers require an atmosphere, sticky attachments fail in the harsh environment of space, and handlike opposed grippers are not suited for large, smooth space debris. We present a robotic gripper that can gently grasp, manipulate, and release both flat and curved uncooperative objects as large as a meter in diameter while in microgravity. This is enabled by (i) space-qualified gecko-inspired dry adhesives that are selectively turned on and off by the application of shear forces, (ii) a load-sharing system that scales small patches of these adhesives to large areas, and (iii) a nonlinear passive wrist that is stiff during manipulation yet compliant when overloaded. We also introduce and experimentally verify a model for determining the force and moment limits of such an adhesive system. Tests in microgravity show that robotic grippers based on dry adhesion are a viable option for eliminating space debris in low Earth orbit and for enhancing missions in space.

A novel silver-mediated highly selective oxidative C-H/C-H functionalization of 1,3-dicarbonyl compounds with terminal alkynes for the creation of polysubstituted furans and pyrroles in one step has been demonstrated. Promoted by the crucial silver species, perfect selectivity and good to excellent yields could be achieved. This protocol represents an extremely simple and atom-economic way to construct polysubstituted furans and pyrroles from basic starting materials under mild conditions.

A novel silver-mediated highly selective C-H/N-H oxidative cross-coupling/cyclization between 2-aminopyridines and terminal alkynes has been demonstrated. This approach provided a simple way to construct heteroaromatic imidazo[1,2-a]pyridines. By using this protocol, the marketed drug zolimidine (antiulcer) could be synthesized easily.

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Micro-aerial vehicles (MAVs) face limited flight times, which adversely impacts their efficacy for scenarios such as first response and disaster recovery, where it might be useful to deploy persistent radio relays and quadrotors for monitoring or sampling. Thus, it is important to enable micro-aerial vehicles to land and perch on different surfaces to save energy by cutting power to motors. We are motivated to use a downward-facing gripper for perching, as opposed to a side-mounted gripper, since it could also be used to carry payloads. In this paper, we predict and verify the performance of a custom gripper designed for perching on smooth surfaces. We also present control and planning algorithms, enabling an underactuated quadrotor with a downward-facing gripper to perch on inclined surfaces while satisfying constraints on actuation and sensing. Experimental results demonstrate the proposed techniques through successful perching on a glass surface at various inclinations, including vertical.

Most robotic grasping research focuses on objects that are either not large in comparison to the gripper or have small graspable features; however, there are important applications that involve large flat or gently curved surfaces. Examples include robots that grasp the solar panels of space craft, handle large panels in manufacturing, or climb or perch on surfaces. We present a solution for grasping such surfaces consisting of groups of tiles coated with a controllable geckoinspired adhesive. The tiles are loaded with two sets of tendons: one for distributing the forces evenly while grasping and the other for release. The gripper is passive and can attach and detach with little effort so that it does not disturb either the robot or the object to be grasped. The maximum gripping force in the normal direction can be over 1000 times greater than the required detaching force. The gripper is also fast, allowing a flying quadrotor to attach to a surface milliseconds after the tiles make contact. We present a model of the gripping mechanism and use the model to design the layout of the tiles to best support anticipated normal and tangential loads.

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In this paper, we consider augmented Lagrangian (AL) algorithms for solving large-scale nonlinear optimization problems that execute adaptive strategies for updating the penalty parameter. Our work is motivated by the recently proposed adaptive AL trust region method by Curtis, Jiang, and Robinson [Math. Prog., DOI: 10.1007/s10107-014-0784-y, 2013]. The first focal point of this paper is a new variant of the approach that employs a line search rather than a trust region strategy, where a critical algorithmic feature for the line search strategy is the use of convexified piecewise quadratic models of the AL function for computing the search directions. We prove global convergence guarantees for our line search algorithm that are on par with those for the previously proposed trust region method. A second focal point of this paper is the practical performance of the line search and trust region algorithm variants in Matlab software, as well as that of an adaptive penalty parameter updating strategy incorporated into the Lancelot software. We test these methods on problems from the CUTEst and COPS collections, as well as on challenging test problems related to optimal power flow. Our numerical experience suggests that the adaptive algorithms outperform traditional AL methods in terms of efficiency and reliability. As with traditional AL algorithms, the adaptive methods are matrix-free and thus represent a viable option for solving extreme-scale problems.