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In this letter, we consider a distributed submodular maximization problem for multi-robot systems when attacked by adversaries. One of the major challenges for multi-robot systems is to increase resilience against failures or attacks. This is particularly important for distributed systems under attack as there is no central point of command that can detect, mitigate, and recover from attacks. Instead, a distributed multi-robot system must coordinate effectively to overcome adversarial attacks. In this work, our distributed submodular action selection problem models a broad set of scenarios where each robot in a multi-robot system has multiple action selections that may fulfill a global objective, such as exploration or target tracking. To increase resilience in this context, we propose a fully distributed algorithm to guide each robot's action selection when the system is attacked. The proposed algorithm guarantees performance in a worst-case scenario where up to a portion of the robots malfunction due to attacks. Importantly, the proposed algorithm is also consistent, as it is shown to converge to the same solution as a centralized method. Finally, a distributed resilient multi-robot exploration problem is presented to confirm the performance of the proposed algorithm.

We introduce and study the problem of planning a trajectory for an agent to carry out a scouting mission while avoiding being detected by an adversarial opponent. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the opponent. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability. We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We first propose three pruning techniques to reduce the computational time while preserving optimality guarantees. When the agent and the opponent are located far from each other initially, we present a variable resolution technique with longer planning horizon to further reduce computational time. Simulation results show the effectiveness of the proposed strategies in terms of computational time.

DNA origami is typically used to fold a long single-stranded DNA scaffold into nanostructures with complex geometries using many short DNA staple strands. Integration of RNA into nucleic acid nanostructures is also possible, but has been less studied. In this research, we designed and characterized a hybrid RNA-scaffolded origami nanostructure with dimensions of ∼12 nm. We used 12 DNA staple strands to fold a 401 nt RNA scaffold into a ten-helix bundle with a honeycomb cross section. We verified the construction of the nanostructure using gel electrophoresis and atomic force microscopy. The DNA–RNA hybrid origami showed higher resistance to ribonuclease compared to a DNA–RNA duplex control. Our work shows potential use in folding long RNA, such as messenger RNA, into origami nanostructures that can be delivered into targeted cells as medicine or a vaccine., A mini DNA–RNA hybrid origami nanobrick with overall size close to 12 nanometer showed high resistance to nuclease digestion.

Heat loss occurs in the process of energy conversion, which causes the temperature rise and performance degradation of ultrasonic motor. In this article, a novel theoretical model is developed to investigate the temperature field and output characteristics of a standing wave ultrasonic motor. The roughness of contact surface and the interaction between temperature rise and material parameters of stator are taken into account in the proposed model. The frequency–temperature characteristics of stator are studied, and the changes in the dielectric coefficients of piezoelectric materials with respect to the temperature are determined by experimental data. The accuracy and effectiveness of the developed model are validated by correlative experiment. The results show that the developed model can not only predict the temperature variation of motor in continuous operation but also evaluate the influence of surface roughness and various input parameters on output characteristics of motor. These researches will give useful guidelines for optimizing heat source and enhancing reliability of motor.

We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest. In the event of a robot failure, our framework repositions robots in a user-defined local neighborhood of the failed robot to compensate for the coverage loss. If repositioning robots locally fails to attain a user-specified level of desired coverage, the central controller augments the team with additional robots from the pool. The size of the local neighborhood around the failed robot and the desired coverage over the region are two objectives that can be varied to achieve a user-specified balance. We investigate the trade-off between the coverage compensation achieved through local repositioning and the computation required to plan the new robot locations. We also study the relationship between the size of the local neighborhood and the number of additional robots added to the team for a given user-specified level of desired coverage. Through extensive simulations and an experiment with a team of seven quadrotors we verify the effectiveness of our framework. We show that to reach a high level of coverage in a neighborhood with a large robot population, it is more efficient to enlarge the neighborhood size, instead of adding additional robots and repositioning them.

In this paper, we design algorithms to protect swarm-robotics applications against sensor denial-of-service (DoS) attacks on robots. We focus on applications requiring the robots to jointly select actions, e.g., which trajectory to follow, among a set of available ones. Such applications are central in large-scale robotic applications, such as multi-robot motion planning for target tracking. But the current attack-robust algorithms are centralized. In this paper, we propose a general-purpose distributed algorithm towards robust optimization at scale, with local communications only. We name it Distributed Robust Maximization (DRM). DRM proposes a divide-and-conquer approach that distributively partitions the problem among cliques of robots. Then, the cliques optimize in parallel, independently of each other. We prove DRM achieves a close-to-optimal performance. We demonstrate DRM's performance in both Gazebo and MATLAB simulations, in scenarios of active target tracking with swarms of robots. In the simulations, DRM achieves computational speed-ups, being 1-2 orders faster than the centralized algorithms; yet, it nearly matches the tracking performance of the centralized counterparts. Since, DRM overestimates the number of attacks in each clique, in this paper we also introduce an Improved Distributed Robust Maximization (IDRM) algorithm. IDRM infers the number of attacks in each clique less conservatively than DRM by leveraging 3-hop neighboring communications. We verify IDRM improves DRM's performance in simulations.

Viral detection is critical for controlling disease spread and progression. Recent emerging viral threats including Zika, Ebola, and the current COVID-19 outbreak highlight the cost and difficulty in responding rapidly. To address these challenges, we develop a platform for low-cost and rapid detection of viral RNA with DNA nanoswitches designed to mechanically reconfigure in response to specific viruses. Using Zika virus as a model system, we show non-enzymatic detection of viral RNA to the attomole level, with selective and multiplexed detection between related viruses and viral strains. For clinical-level sensitivity in biological fluids, we paired the assay with a sample preparation step using either RNA extraction or isothermal pre-amplification. Our assay can be performed with minimal or no lab infrastructure, and is readily adaptable to detect other viruses. We demonstrate the adaptability of our method by quickly developing and testing DNA nanoswitches for detecting a fragment of SARS-CoV-2 RNA in human saliva. Given this versatility, we expect that further development and field implementation will improve our ability to detect emergent viral threats and ultimately limit their impact.

We study the problem of incorporating risk while making combinatorial decisions under uncertainty. We formulate a discrete submodular maximization problem for selecting a set using Conditional-Value-at-Risk (CVaR), a risk metric commonly used in financial analysis. While CVaR has recently been used in optimization of linear cost functions in robotics, we take the first stages towards extending this to discrete submodular optimization and provide several positive results. Specifically, we propose the Sequential Greedy Algorithm that provides an approximation guarantee on finding the maxima of CVaR cost function under a matroidal constraint. The approximation guarantee shows that the solution produced by our algorithm is within a constant factor of the optimal and an additive term that depends on the optimal. Our analysis uses the curvature of the submodular set function, and proves that the algorithm runs in polynomial time. This formulates a number of combinatorial optimization problems that appear in robotics. We use two such problems, vehicle assignment under uncertainty for mobility-on-demand and sensor selection with failures for environmental monitoring, as case studies to demonstrate the efficacy of our formulation.

Random forest and partial least squares have proved wide applicability in numerous contexts. However, the combination of these versatile tools has seldom been studied. Inspired by a relatively new decision tree ensemble called rotation forest, we introduce a new survival ensemble algorithm using partial least squares regression and the Buckley-James estimator within the framework of random forest. First, the approach taken to cope with the high dimensionality is to reduce the dimension by a random subspace method. Then, censored survival times are imputed by the Buckley-James estimator. After dimension reduction and time imputation, partial least squares regression is applied to extract the features. Similar to rotation forest, all extracted components are used as covariates in a bagged survival tree to predict the survival probabilities. Experimental results on a variety of simulation and real datasets demonstrate that the proposed approach is a strong competitor to other popular survival prediction models under high or ultra-high dimensional setting.

MCM-41 supported layered double hydroxides (LDH) composite materials (ML) were synthesized and studied for removal of Acid Red G (ARG), an anionic dye, with the adsorption method. ML was prepared using in situ synthesis procedure for the low supersaturation coprecipitation method, and ML10 and ML20 presented promising application towards ARG dye adsorption capacity in industrial wastewater. Powder samples were characterized by Xray diffraction, FTIR spectroscopy, scanning electron microscopy and energy dispersive spectrometry. The effects of different reaction time, initial solution pH and temperature on the dye adsorption capacity were investigated. Adsorption process was well described by pseudo-second-order kinetic model and Langmuir isotherm model. The fitting curves showed that ML10 and ML20 had higher adsorption rates and maintained a certain theoretical saturated adsorption capacity (92.19807mg/g and 96.41947mg/g, respectively) compared with LDH.

The ultrasonic motor is an uncertain time-varying nonlinear system because of the nonlinearity of the piezoelectric material, the friction and the temperature. For example, the operating time of the mechanical switch actuated by the ultrasonic motor in regular stroke is highly dispersed. Unfortunately, it is difficult to establish accurate mathematical model. In this paper, an analytical autoregressive process model (AR) is employed to identify and control the ultrasonic motor. First of all, dispersed operating time of the mechanical switch actuated by the ultrasonic motor is investigated. Then, the AR model is established to predict the operating time of the ultrasonic motor on the basis of the statistical data to reduce the nonlinear behavior of the ultrasonic motor, and to improve the accuracy and obtain a good time response of the switch. The simulation results are agreed with experimental results, confirming the effectiveness of proposed model. Furthermore, we adopt the predicted result of the AR model to control the mechanical switch actuated by the ultrasonic motor. The analytical investigation is fulfilled with two target operating time ranges, namely 12 ms and 24 ms. Comparison of the results obtained from the AR model and the experimentation reveal that the standard deviations are less than 95.3 μs and 102.7 μs with maximum errors equal to 0.41 % and 0.44 % respectively. Thereby, the proposed dispersed operating time control is performed. Findings indicate that the maximum errors for the operating time of the mechanical switch are less than 140 μs and 110 μs with ±0.85 % and ±0.42 % respectively.

ZSM-5 type zeolite with mesoporous structure was prepared and then amine-functionalized with tetraethylenepentamine (TEPA) by wet impregnation method to form a series of CO2 adsorbents (ZTx). The structural properties of ZSM-5 and ZTx were characterized by XRD, FTIR, TGA/DTG, nitrogen adsorption/desorption, SEM and EDX techniques. The adsorption capacity of the adsorbents with different amine loading was measured at a temperature from 40 to 100 °C and the adsorption capacity of ZT7 was 1.80 mmol/g at 100 °C. The adsorption process and mechanism were studied by fitting the experimental data used the three adsorption kinetic models, and a complex physical and chemical mixing process was produced as the amine entered the surface and pore size of the zeolite. The high adsorption selectivity at 10% CO2 concentration and the stability of the five adsorption desorption cycles indicated that ZT7 is a suitable and promising CO2 adsorbent for the purification of industrial flue gas.

Due to the fast learning speed, simplicity of implementation and minimal human intervention, extreme learning machine has received considerable attentions recently, mostly from the machine learning community. Generally, extreme learning machine and its various variants focus on classification and regression problems. Its potential application in analyzing censored time-to-event data is yet to be verified. In this study, we present an extreme learning machine ensemble to model right-censored survival data by combining the Buckley-James transformation and the random forest framework. According to experimental and statistical analysis results, we show that the proposed model outperforms popular survival models such as random survival forest, Cox proportional hazard models on well-known low-dimensional and high-dimensional benchmark datasets in terms of both prediction accuracy and time efficiency.

In this paper, a new method called “projection kinematics” is presented for the determination of 3D configurations of DNA Origami Mechanisms (DOM) based on a single 2D projected image. The method enables finding all possible projected configurations of a DOM in space by solving the projection kinematics equations based on the minimum needed information measured from a 2D image. If redundant measurements are available, they can be exploited to eliminate ambiguous solutions. First, projection kinematic analyses were derived for the basic kinematic joints – revolute, prismatic, cylindrical and spherical joints and one degree-of-freedom (DOF) planar four-bar and two DOFs five-bar linkages. Then a generalized procedure for projection kinematic analysis is presented. Finally, a universal joint and a Bennett linkage fabricated by DNA origami self-assembly are chosen as proof-of-concept examples to verify the feasibility of projection kinematic analysis of DOM. This approach not only provides valuable guidance for the design of DOM, but also offers a low cost analysis approach for configuration estimation of spatial mechanisms when images from multiple viewing angles are not accessible, which is generally the case for the most commonly used analysis methods.

We introduce and study the problem of planning a trajectory for an agent to carry out a reconnaissance mission while avoiding being detected by an adversarial guard. This introduces a multi-objective version of classical visibility-based target search and pursuit-evasion problem. In our formulation, the agent receives a positive reward for increasing its visibility (by exploring new regions) and a negative penalty every time it is detected by the guard. The objective is to find a finite-horizon path for the agent that balances the trade off between maximizing visibility and minimizing detectability.We model this problem as a discrete, sequential, two-player, zero-sum game. We use two types of game tree search algorithms to solve this problem: minimax search tree and Monte-Carlo search tree. Both search trees can yield the optimal policy but may require possibly exponential computational time and space. We propose several pruning techniques to reduce the computational cost while still preserving optimality guarantees. Simulation results show that the proposed strategy prunes approximately three orders of magnitude nodes as compared to the brute-force strategy. We also find that the Monte-Carlo search tree saves approximately one order of computational time as compared to the minimax search tree.

Cloud computing enables customers with limited computational resources to outsource their huge computation workloads to the cloud with massive computational power. However, in order to utilize this computing paradigm, it presents various challenges that need to be addressed, especially security. As eigen-decomposition (ED) and singular value decomposition (SVD) of a matrix are widely applied in engineering tasks, we are motivated to design secure, correct, and efficient protocols for outsourcing the ED and SVD of a matrix to a malicious cloud in this paper. In order to achieve security, we employ efficient privacy-preserving transformations to protect both the input and output privacy. In order to check the correctness of the result returned from the cloud, an efficient verification algorithm is employed. A computational complexity analysis shows that our protocols are highly efficient. We also introduce an outsourcing principle component analysis as an application of our two proposed protocols.

The ability to design and control DNA nanodevices with programmed conformational changes has established a foundation for molecular-scale robotics with applications in nanomanufacturing, drug delivery, and controlling enzymatic reactions. The most commonly used approach for actuating these devices, DNA binding and strand displacement, allows devices to respond to molecules in solution, but this approach is limited to response times of minutes or greater. Recent advances have enabled electrical and magnetic control of DNA structures with sub-second response times, but these methods utilize external components with additional fabrication requirements. Here, we present a simple and broadly applicable actuation method based on the avidity of many weak base-pairing interactions that respond to changes in local ionic conditions to drive large-scale conformational transitions in devices on sub-second time scales. To demonstrate such ion-mediated actuation, we modified a DNA origami hinge with short, weakly complementary single-stranded DNA overhangs, whose hybridization is sensitive to cation concentrations in solution. We triggered conformational changes with several different types of ions including mono-, di-, and trivalent ions and also illustrated the ability to engineer the actuation response with design parameters such as number and length of DNA overhangs and hinge torsional stiffness. We developed a statistical mechanical model that agrees with experimental data, enabling effective interpretation and future design of ion-induced actuation. Single-molecule Förster resonance energy-transfer measurements revealed that closing and opening transitions occur on the millisecond time scale, and these transitions can be repeated with time resolution on the scale of one second. Our results advance capabilities for rapid control of DNA nanodevices, expand the range of triggering mechanisms, and demonstrate DNA nanomachines with tunable analog responses to the local environment.

A DNA nanotechnology approach enables label-free and unamplified detection of cellular microRNAs using gel electrophoresis., MicroRNAs are short noncoding regulatory RNAs that are increasingly used as disease biomarkers. Detection of microRNAs can be arduous and expensive and often requires amplification, labeling, or radioactive probes. Here, we report a single-step, nonenzymatic microRNA detection assay using conformationally responsive DNA nanoswitches. Termed miRacles (microRNA-activated conditional looping of engineered switches), our assay has subattomole sensitivity and single-nucleotide specificity using an agarose gel electrophoresis readout. We detect cellular microRNAs from nanogram-scale RNA extracts of differentiating muscle cells and multiplex our detection for several microRNAs from one biological sample. We demonstrate 1-hour detection without expensive equipment or reagents, making this assay a compelling alternative to quantitative polymerase chain reaction and Northern blotting.

Significant progress in DNA nanotechnology has accelerated the development of molecular machines with functions like macroscale machines. However, the mobility of DNA self-assembled nanorobots is still dramatically limited due to challenges with designing and controlling nanoscale systems with many degrees of freedom. Here, an origami-inspired method to design transformable DNA nanomachines is presented. This approach integrates stiff panels formed by bundles of double-stranded DNA connected with foldable creases formed by single-stranded DNA. To demonstrate the method, a DNA version of the paper origami mechanism called a waterbomb base (WBB) consisting of six panels connected by six joints is constructed. This nanoscale WBB can follow four distinct motion paths to transform between five distinct configurations including a flat square, two triangles, a rectangle, and a fully compacted trapezoidal shape. To achieve this, the sequence specificity of DNA base-pairing is leveraged for the selective actuation of joints and the ion-sensitivity of base-stacking interactions is employed for the flattening of joints. In addition, higher-order assembly of DNA WBBs into reconfigurable arrays is achieved. This work establishes a foundation for origami-inspired design for next generation synthetic molecular robots and reconfigurable nanomaterials enabling more complex and controllable motion.

We study the problem of designing spoofing signals to corrupt and mislead the output of a Kalman filter. Unlike existing works that focus on detection and filtering algorithms for the observer, we study the problem from the attacker's point-of-view. In our model, the attacker can corrupt the measurements by adding spoofing signals. The attacker seeks to create a separation between the estimate of the Kalman filter with and without spoofing signals. We present a number of results on how to generate such spoofing signals, while minimizing the signal magnitude. The resulting algorithms are evaluated through simulations along with theoretical proofs.

The problem of target tracking with multiple robots consists of actively planning the motion of the robots to track the targets. A major challenge for practical deployments is to make the robots resilient to failures. In particular, robots may be attacked in adversarial scenarios, or their sensors may fail or get occluded. In this letter, we introduce planning algorithms for multi-target tracking that are resilient to such failures. In general, resilient target tracking is computationally hard. Contrary to the case where there are no failures, no scalable approximation algorithms are known for resilient target tracking when the targets are indistinguishable, or unknown in number, or with unknown motion model. In this letter, we provide the first such algorithm, which also has the following properties: First, it achieves maximal resiliency, since the algorithm is valid for any number of failures. Second, it is scalable, as our algorithm terminates with the same running time as state-of-the-art algorithms for (non-resilient) target tracking. Third, it provides provable approximation bounds on the tracking performance, since our algorithm guarantees a solution that is guaranteed to be close to the optimal. We quantify our algorithm's approximation performance using a novel notion of curvature for monotone set functions subject to matroid constraints. Finally, we demonstrate the efficacy of our algorithm through MATLAB and Gazebo simulations and a sensitivity analysis; we focus on scenarios that involve a known number of distinguishable targets.

We study the problem of reducing the amount of communication in a distributed target tracking problem. We focus on the scenario where a team of robots are allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past that guarantee that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on reducing the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information from the neighbor. We prove that this strategy converges to an optimal formation. We compare the two approaches (constant communication and self-triggered communication) through simulations of tracking stationary and mobile targets.

Deoxyribose nucleic acid (DNA) origami nanotechnology is a recently developed self-assembly process for design and fabrication of complex three-dimensional (3D) nanostructures using DNA as a functional material. This paper reviews our recent progress in applying DNA origami to design kinematic mechanisms at the nanometer scale. These nanomechanisms, which we call DNA origami mechanisms (DOM), are made of relatively stiff bundles of double-stranded DNA (dsDNA), which function as rigid links, connected by highly compliant single-stranded DNA (ssDNA) strands, which function as kinematic joints. The design of kinematic joints including revolute, prismatic, cylindrical, universal, and spherical is presented. The steps as well as necessary software or experimental tools for designing DOM with DNA origami links and joints are detailed. To demonstrate the designs, we presented the designs of Bennett four-bar and crank–slider linkages. Finally, a list of technical challenges such as design automation and computational modeling are presented. These challenges could also be opportunities for mechanism and robotics community to apply well-developed kinematic theories and computational tools to the design of nanorobots and nanomachines.

We study the problem of reducing the amount of communication in decentralized target tracking. We focus on the scenario where a team of robots are allowed to move on the boundary of the environment. Their goal is to seek a formation so as to best track a target moving in the interior of the environment. The robots are capable of measuring distances to the target. Decentralized control strategies have been proposed in the past that guarantee that the robots asymptotically converge to the optimal formation. However, existing methods require that the robots exchange information with their neighbors at all time steps. Instead, we focus on decentralized strategies to reduce the amount of communication among robots. We propose a self-triggered communication strategy that decides when a particular robot should seek up-to-date information from its neighbors and when it is safe to operate with possibly outdated information. We prove that this strategy converges asymptotically to a desired formation when the target is stationary. For the case of a mobile target, we propose an extension whereby each robot decides its optimal partner to share its measurements with using observability as a criterion. We evaluate all the approaches (constant communication and self-triggered communication with centralized and decentralized sensor fusion) through simulations., Comment: Our paper has been accepted as a regular paper and is going to be published in IEEE Transactions on Automation Science and Engineering. We update the final version of our paper. The main change from previous arXiv version is the discussion about the limited communication and sensing range(Section V-B, page 6) and more details about the calculation of the omega_max in the appendix

We study two sensor assignment problems for multi-target tracking with the goal of improving the observability of the underlying estimator. We consider various measures of the observability matrix as the assignment value function. We first study the general version where the sensors must form teams to track individual targets. If the value function is monotonically increasing and submodular then a greedy algorithm yields a 1/2-approximation. We then study a restricted version where exactly two sensors must be assigned to each target. We present a 1/3-approximation algorithm for this problem which holds for arbitrary value functions (not necessarily submodular or monotone). In addition to approximation algorithms, we also present various properties of observability measures. We show that the inverse of the condition number of the observability matrix is neither monotone nor submodular, but present other measures which are. Specifically, we show that the trace and rank of the symmetric observability matrix are monotone and submodular and the log determinant of the symmetric observability matrix is monotone and submodular when the matrix is non-singular. If the target's motion model is not known, the inverse cannot be computed exactly. Instead, we present a lower bound for distance sensors. In addition to theoretical results, we evaluate our results empirically through simulations., Comment: Second version submission. Accidental submission as new article. Please see arXiv:1706.00871

In this paper, a thermal-mechanical-electric coupling dynamic model is developed for a V-shape linear ultrasonic motor. The impact of surface roughness of stator/slider on the motor temperature rise as well as the temperature dependence of the stator material parameters are considered in this model. The relationship between the resonant frequency of the stator and the temperature is investigated, and the variation relation of motor parameters with regard to the temperature is discussed. The validity of the model is verified by numerical simulation and experimental results. Based on the proposed model, the influences of surface roughness of stator/slider, driving voltage, driving frequency and pre-pressure on motor temperature and velocity are studied and analyzed, and the comparison between the measuring data and the previous reported model with constant parameters are conducted. The results denote that the established model under different input parameters can be more accurately to predict the dynamic changes of temperature and velocity of the motor, which will be a valuable guide for the improvement of precise control and long-term stable operation of the motor.

Prediction capability of a classifier usually improves when it is built from an extended variable space by adding new variables from randomly combination of two or more original variables. However, its usefulness in survival analysis of censored time-to-event data is yet to be verified. In this research, we investigate the plausibility of space extension technique, originally proposed for classification purpose, to survival analysis. By combing random subspace, bagging and extended space techniques, we develop a random survival forest with space extensions algorithm. According to statistical analysis results, we show that the proposed model outperforms or at least comparable to popular survival models such as random survival forest, rotation survival forest, Cox proportional hazard and boosting survival models on well-known benchmark datasets.

In this paper, we introduce a strategy for the design and computational analysis of compliant DNA origami mechanisms (CDOMs), which are compliant nanomechanisms fabricated via DNA origami self-assembly. The rigid, compliant, and flexible parts are constructed by bundles of many double-stranded DNA (dsDNA) helices, bundles of a few dsDNA helices or a single dsDNA helix, and single-stranded DNA (ssDNA) strands, respectively. Similar to its macroscopic counterparts, a CDOM generates its motion via deformation of at least one structural member. During the motion, strain energy is stored and released in the compliant components. Therefore, these CDOMs have the advantage of suppressing thermal fluctuations due to the internal mechanical energy barrier for motion. Here, we show that classic pseudorigid-body (PRB) models for compliant mechanism are successfully employed to the analysis of these DNA origami nanomechanisms and can serve to guide the design and analysis method. An example of compliant joint and a bistable four-bar CDOM fabricated with DNA origami are presented.

DNA origami enables the precise fabrication of nanoscale geometries. We demonstrate an approach to engineer complex and reversible motion of nanoscale DNA origami machine elements. We first design, fabricate, and characterize the mechanical behavior of flexible DNA origami rotational and linear joints that integrate stiff double-stranded DNA components and flexible single-stranded DNA components to constrain motion along a single degree of freedom and demonstrate the ability to tune the flexibility and range of motion. Multiple joints with simple 1D motion were then integrated into higher order mechanisms. One mechanism is a crank-slider that couples rotational and linear motion, and the other is a Bennett linkage that moves between a compacted bundle and an expanded frame configuration with a constrained 3D motion path. Finally, we demonstrate distributed actuation of the linkage using DNA input strands to achieve reversible conformational changes of the entire structure on ∼ minute timescales. Our results demonstrate programmable motion of 2D and 3D DNA origami mechanisms constructed following a macroscopic machine design approach.

Molecular self-assembly has become a well-established technique to design complex nanostructures and hierarchical mesoscale assemblies. The typical approach is to design binding complementarity into nucleotide or amino acid sequences to achieve the desired final geometry. However, with an increasing interest in dynamic nanodevices, the need to design structures with motion has necessitated the development of multi-component structures. While this has been achieved through hierarchical assembly of similar structural units, here we focus on the assembly of topologically complex structures, specifically with concentric components, where post-folding assembly is not feasible. We exploit the ability to direct folding pathways to program the sequence of assembly and present a novel approach of designing the strand topology of intermediate folding states to program the topology of the final structure, in this case a DNA origami slider structure that functions much like a piston-cylinder assembly in an engine. The ability to program the sequence and control orientation and topology of multi-component DNA origami nanostructures provides a foundation for a new class of structures with internal and external moving parts and complex scaffold topology. Furthermore, this work provides critical insight to guide the design of intermediate states along a DNA origami folding pathway and to further understand the details of DNA origami self-assembly to more broadly control folding states and landscapes.