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Structural DNA nanotechnology has enabled the design and construction of complex nanoscale structures with precise geometry and programmable dynamic and mechanical properties. Recent efforts have led to major advances in the capacity to actuate shape changes of DNA origami devices and incorporate DNA origami into larger assemblies, which open the prospect of using DNA to design shape-morphing assemblies as components of micro-scale reconfigurable or sensing materials. Indeed, a few studies have constructed higher order assemblies with reconfigurable devices; however, these demonstrations have utilized structures with relatively simple motion, primarily hinges that open and close. To advance the shape changing capabilities of DNA origami assemblies, we developed a multi-component DNA origami 6-bar mechanism that can be reconfigured into various shapes and can be incorporated into larger assemblies while maintaining capabilities for a variety of shape transformations. We demonstrate the folding of the 6-bar mechanism into four different shapes and demonstrate multiple transitions between these shapes. We also studied the shape preferences of the 6-bar mechanism in competitive folding reactions to gain insight into the relative free energies of the shapes. Furthermore, we polymerized the 6-bar mechanism into tubes with various cross-sections, defined by the shape of the individual mechanism, and we demonstrate the ability to change the shape of the tube cross-section. This expansion of current single-device reconfiguration to higher order scales provides a foundation for nano to micron scale DNA nanotechnology applications such as biosensing or materials with tunable properties.

Efficient predictive models for large deflection of beams are critical for the design and analysis of compliant mechanisms. Machine learning (ML) models for predicting large tip deflection and shape of 2D cantilever beams are proposed for the analysis of compliant mechanisms in this paper. First, the training data is collected by running massive finite element simulations of cantilever beams subject to various loading conditions. Second, these training data are fed into several nonlinear regression learners. The result is a generalized predictive model for any 2D beams subject to a wide range of tip loads. The evaluation results show the maximum error is about 3.8%. The machine learning models with high prediction speed and accuracy are obtained. Two extreme loading conditions are studied to evaluate our machine learning model of deflections. Our results show that the error of our ML model is less than that of the conventional PRB 3R model, and the speed is 98 times that of the FEM in these two studies. A numerical example of 4-bar linkages with a 2D beam is applied to demonstrate the use of the machine learning model of the beam shape. The result shows the very close shape and high efficiency of our model.

In this paper, we introduce the key elements of a computational tool and a comprehensive methodology for enabling simulation-informed design of robotic ankle prostheses. Our approach is based on trajectory optimization methods for predicting human walking gait applied to a model of a bilateral lower leg amputee with robotic ankle prostheses. The goal of this simulation tool and design methodology is to provide a means to evaluate the performance of a prosthetic design and its effect on adapting human walking gait before involving human subject studies. The gait prediction problem is formulated as a multi-objective trajectory optimization of a multibody dynamic system within the existing framework of the open-source biomechanics package, OpenSim. Our approach models the effects of a planar human model with legs and torso, Hill-type muscle actuators, a closed-loop linkage for the robotic prosthetic device, and a DC motor dynamics model. To demonstrate the gait prediction process in use for design evaluation, we include a pair of example design evaluation simulations. In the first example, the gait prediction method shows that the initially proposed prosthesis design fails to improve the human effort estimated by cubic muscle excitations. In the second example, after the selection of a different actuator, the gait prediction method shows that the modified design has the potential to relieve some of the effort of walking required from the amputee. These examples combine to demonstrate how our methodology may be useful for design.

Recently, DNA has been used to make nanodevices for a myriad of applications across fields including medicine, nanomanufacturing, synthetic biology, biosensing and biophysics. However, current DNA nanodevices rely primarily on geometric design, and it remains challenging to rationally design functional properties such as force-response or actuation behaviour. Here we report an iterative design pipeline for DNA assemblies that integrates computer-aided engineering based on coarse-grained molecular dynamics with a versatile computer-aided design approach that combines top-down automation with bottom-up control over geometry. This intuitive framework allows for rapid construction of large, multicomponent assemblies from three-dimensional models with finer control over the geometrical, mechanical and dynamical properties of the DNA structures in an automated manner. This approach expands the scope of structural complexity and enhances mechanical and dynamic design of DNA assemblies. An approach integrating molecular dynamics-based computer-aided engineering with computer-aided design allows for the rapid construction of large three-dimensional DNA assemblies and control over their geometry, mechanics and dynamics.

SUMMARYRobots of next-generation physically interact with the world rather than be caged in a controlled area, and they need to make contact with the open-ended environment to perform their task. Compliant robot links offer intrinsic mechanical compliance for addressing the safety issue for physical human–robot interactions (pHRI). However, many important research questions are yet to be answered. For instance, how do system parameters, for example, mechanical compliance, motor torque, impact velocities, and so on, affect the impact force? how to formulate system impact dynamics of compliant robots, and how to size their geometric dimensions to maximize impact force reduction. In this paper, we present a parametric study of compliant link (CL) design for safe pHRI. We first present a theoretical model of the pHRI system that is comprised of robot dynamics, an impact contact model, and dummy head dynamics. After experimentally validating the theoretical model, we then systematically study the effects of CL parameters on the impact force in more detail. Specifically, we explore how the design and actuation parameters affect the impact force of pHRI system. Based on the parametric studies of the CL design, we propose a step-by-step process and a list of concrete guidelines for designing CL with safety constraints in pHRI. We further conduct a simulation case study to validate this design process and design guidelines.

This article presents a novel soft robotic gripper with a high payload capacity based on the layer jamming technology. Soft robots have a high adaptability, however suffer a low payload capacity. To overcome these conflicting challenges, here we introduce a 3D printed multi-material gripper that integrates jamming layers for enhancing payload capacity. By inflating the internal air chamber with positive pressure, the finger can be actuated to a large bending angle for adapting complex shapes. Layers of jamming sheets are bounded on the finger structure and are then sealed inside a vacuum bag. When a high payload is desired, air inside the vacuum bag is drawn out and a negative air pressure is applied to the jamming layers, which leads to the gripper locked at the actuated shape. To evaluate the performance of the gripper, we conducted extensive tests including actuation, stiffness variation, typical payload capacity, and adaptability. The results show that our gripper is not only highly adaptable just like most soft grippers but also more importantly capable of grasping heavy (about 6–10 kg) objects comparable to rigid-body counterparts.

The spatiotemporal distribution of drivers’ attention to preview was inferred from their steering movements while tracking a winding roadway in a laboratory setting. For most subjects, the average driving attentional distribution over six daily sessions was relatively stable and generalized across different control devices. However, there was considerable day-to-day variability in the attentional distributions. This variability was modeled as a strong interaction between two dynamic processes, the attentional emphasis of selected regions and inhibition of surrounding regions. The model combines a novel application of a reaction-diffusion model of biological pattern formation with an optimal control model of attention to preview. The combined model treats attentional dynamics as an example of the biological spacing of a limited cognitive resource, which is also shaped by the demands of action.

In this article, we present a continuously tunable stiffness arm for safe physical human–robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human–robot interactions via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human–robot interactions are much less studied. This article details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D-printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26% given a morphing angle of 90 deg. The variable stiffness arm design developed in this study could be a promising approach to address safety concerns for safe physical human–robot interactions.

This paper presents an efficient graphical representation geometric algebra approach for the forward displacement analysis (FDA) of 3-PPS parallel mechanisms (PMs). Our approach based on conformal geometric algebra (CGA) reveals interesting intuitions and insight of the modelling and solution for FDA of this type PMs and reduces considerably computational burden and time. In this work, we first deduce the position of an arbitrary joint among three spherical joints on the moving platform through motors and represent it in terms of only one variable. Then, we formulate the position of either one joint of two remained spherical joints using the intersection of a sphere and two planes under the CGA framework. To obtain the geometric formulation of the third spherical joint position, we construct the outer product of two spatial balls with centers at two formulated spherical joints centers and two known planes. The positions of three spherical joints are all explicit geometric formulations of geometric entities of the parallel mechanisms. We achieve a coordinate-invariant equation expressed in terms of geometric entities via inner product of the last joint by itself. The symbolic 8th-degree univariate polynomial input-output equation is first proposed and all 8 sets of closed-form solutions can be obtained. The algorithm is the geometric algebra computation in a clear and coordinates-free way that avoids the use of rotation matrices, and complex algebraic modelling and nonlinear and multivariable elimination computations as most current approaches do. Finally, two numerical examples have been applied to demonstrate the efficiency of the algorithm comparing with the Dixon resultant method. The results show that this algorithm can simplify complexity of the problem and reduce computation time dramatically with strong geometrical intuition. The achieved symbolic univariate polynomial input-output equation can be applied directly to solve the DPA for all types of 3-PPS PMs instead of using any other methods. This work provides a novel geometric modeling and solution approach for the forward displacement analysis of spatial parallel mechanism and be of greatly useful for roboticists or engineers to design and develop 3-PPS PMs for kinds of applications.

Significant advances have been made towards the design, fabrication, and actuation of dynamic DNA nanorobots including the development of DNA origami mechanisms. These DNA origami mechanisms integrate relatively stiff links made of bundles of double-stranded DNA and relatively flexible joints made of single-stranded DNA to mimic the design of macroscopic machines and robots. Despite reproducing the complex configurations of macroscopic machines, these DNA origami mechanisms exhibit significant deviations from their intended motion behavior since nanoscale mechanisms are subject to significant thermal fluctuations that lead to variations in the geometry of the underlying DNA origami components. Understanding these fluctuations is critical to assess and improve the performance of DNA origami mechanisms and to enable precise nanoscale robotic functions. Here, we report a hybrid computational framework combining coarse-grained modeling with kinematic variance analysis to predict uncertainties in the motion pathway of a multi-component DNA origami mechanism. Coarse-grained modeling was used to evaluate the variation in geometry of individual components due to thermal fluctuations. This variation was incorporated in kinematic analyses to predict the motion pathway uncertainty of the entire mechanism, which agreed well with experimental characterization of motion. We further demonstrated the ability to predict the probability density of DNA origami mechanism conformations based on analysis of mechanical properties of individual joints. This integration of computational analysis, modeling tools, and experimental methods establish the foundation to predict and manage motion uncertainties of general DNA origami mechanisms to guide the design of DNA-based nanoscale machines and robots.

In this paper, we introduce non-dimensional mass property parameters to the classical PRB model for accurately predicting the dynamics of the compliant mechanisms. Given extensive work on PRB models accurately modeling the statics and kinematics of compliant mechanisms, few works have investigated on their accuracy in predicting dynamics. Here we study the fundamental natural frequency of two typical mechanisms: fixed-root and pinned-root compliant parallel-guiding mechanisms (CPGM). First, analytical expressions of the natural frequencies of the continuum models are derived according to the vibration mechanics. Second, theoretical expressions of the natural frequencies of the parameterized PRB model are obtained from their dynamics equations. Third, the mass property parameters are optimized to minimize the error between the continuum models and the parameterized PRB model. We conclude that the optimized PRB models can well predict the dynamics especially the low input signal frequency or mass of beams are not negligible. These new PRB models can significantly improve computational efficiency in dynamics simulation of compliant mechanisms.

When an alien species is introduced in a new area, the new population may be genetically and phenotypically different from the parent population because of the population bottleneck, increased inbreeding rate and adaptation to the new environment. In this study we investigated the variation in shape and size of the mandible among and within three populations of the invasive Pallas’s squirrel introduced in Italy, Belgium and France. Significant differences in both size and shape of the mandible were found among all population pairs, with France being the most distinct. French squirrels showed a larger and slenderer mandible with a broad angular process, a restricted condyle, and a backward-oriented coronoid process. The Italian and the Belgian population also differed significantly but to a lesser extent, the Italian squirrels having a lower coronoid process, a broader angular apophysis, and a restricted condyle. Size explained 15% of the total shape variation, but the slope of allometric trajectories did not reveal any significant difference among populations. A significantly high fluctuating and directional asymmetries were found respectively in the French and the Italian squirrels. Results are discussed in terms of different selective pressures in the invaded areas and possible effects of developmental instability.

As the structures of multiarm robots are serially arranged, the packaging and transportation of these robots are often inconvenient. The ability of these robots to operate objects must also be improved. Addressing this issue, this paper presents a type of multiarm robot that can be adequately folded into a designed area. The robot can achieve different operation modes by combining different arms and objects. First, deployable kinematic chains (DKCs) are designed, which can be folded into a designated area and be used as an arm structure in the multiarm robot mechanism. The strategy of a platform for storing DKCs is proposed. Based on the restrictions in the storage area and the characteristics of parallel mechanisms, a class of DKCs, called base assembly library, is obtained. Subsequently, an assembly method for the synthesis of the multiarm robot mechanism is proposed, which can be formed by the connection of a multiarm robot mechanism with an operation object based on a parallel mechanism structure. The formed parallel mechanism can achieve a reconfigurable characteristic when different DKCs connect to the operation object. Using this method, two types of multiarm robot mechanisms with four DKCs that can switch operation modes to perform different tasks through autonomous combination and release operation is proposed. The obtained mechanisms have observable advantages when compared with the traditional mechanisms, including optimizing the occupied volume during transportation and using parallel mechanism theory to analyze the switching of operation modes.

In this paper, we present a continuously tunable stiffness arm for safe physical human-robot interactions. Compliant joints and compliant links are two typical solutions to address safety issues for physical human-robot interaction via introducing mechanical compliance to robotic systems. While extensive studies explore variable stiffness joints/actuators, variable stiffness links for safe physical human-robot interactions are much less studied. This paper details the design and modeling of a compliant robotic arm whose stiffness can be continuously tuned via cable-driven mechanisms actuated by a single servo motor. Specifically, a 3D printed compliant robotic arm is prototyped and tested by static experiments, and an analytical model of the variable stiffness arm is derived and validated by testing. The results show that the lateral stiffness of the robot arm can achieve a variety of 221.26% given a morphing angle of 90°. The study demonstrates that the compliant link design could be a promising approach to address safety concerns for safe physical human-robot interactions.

Designers of powered ankle prosthetics face the challenge of synthesizing a suitable means of actuating the ankle joint while keeping prosthetic mass low. The mass of the motor can be reduced by decreasing the amount of torque it must provide which can be accomplished by the design of an effective driving mechanism. In this paper, we outline a method for designing a compliant four-bar mechanism for minimizing the maximum torque required of the motor. We demonstrate a workflow for using human subject walking data to create a prosthetic drive mechanism tailored to the specific bio-mechanics of the dataset, and that the algorithm can be applied across a variety of gaits to create a prosthetic appropriate for both walking and running.

The specific detection in clinical samples of analytes with dimensions in the tens to hundreds of nanometers, such as viruses and large proteins, would improve disease diagnosis. Detection of these "mesoscale" analytes (as opposed to their nanoscale components), however, is challenging as it requires the simultaneous binding of multiple recognition sites often spaced over tens of nanometers. In response, we have adapted DNA origami, with its unparalleled customizability to precisely display multiple target-binding sites over the relevant length scale, to an electrochemical biosensor platform. Our proof-of-concept employs triangular origami covalently attached to a gold electrode and functionalized with redox reporters. Electrochemical interrogation of this platform successfully monitors mesoscale, target-binding-induced changes in electron transfer in a manner consistent with coarse-grained molecular dynamics simulations. Our approach enables the specific detection of analytes displaying recognition sites that are separated by ∼40 nm, a spacing significantly greater than that achieved in similar sensor architectures employing either antibodies or aptamers.

Functional properties of modern engineering products result from merging the geometry and material properties of underlying components into sophisticated overall assemblies. The foundation of this design process is an integration of computer aided design (CAD) tools that allow rapid geometric modifications with robust simulation tools to guide design iterations (i.e. computer-aided engineering, CAE). Recently, DNA has been used to make nanodevices for a myriad of applications across fields including medicine, nanomanufacturing, synthetic biology, biosensing, and biophysics. However, currently these self-assembled DNA nanodevices rely primarily on geometric design, and hence, they have not demonstrated the same sophistication as real-life products. We present an iterative design pipeline for DNA assemblies that integrates CAE based on coarse-grained molecular dynamics with a versatile CAD approach that combines top-down automation with bottom-up control over geometry. This intuitive framework redefines the scope of structural complexity and enhances mechanical and dynamic design of DNA assemblies.

Variable stiffness robots may provide an effective way of trading-off between safety and speed during physical human–robot interaction. In such a compromise, the impact force reduction capability and maximum safe speed are two key performance measures. To quantitatively study how dynamic parameters such as mass, inertia, and stiffness affect these two performance measures, performance indices for impact force reduction capability and maximum speed of variable stiffness robots are proposed based on the impact ellipsoid in this paper. The proposed performance indices consider different impact directions and kinematic configurations in the large. Combining the two performance indices, the global performance of variable stiffness robots is defined. A two-step optimization method is designed to achieve this global performance. A two-link variable stiffness link robot example is provided to show the efficacy of the proposed method.

In this paper, we study the effects of mechanical compliance on safety in physical human–robot interaction (pHRI). More specifically, we compare the effect of joint compliance and link compliance on the impact force assuming a contact occurred between a robot and a human head. We first establish pHRI system models that are composed of robot dynamics, an impact contact model, and head dynamics. These models are validated by Simscape simulation. By comparing impact results with a robotic arm made of a compliant link (CL) and compliant joint (CJ), we conclude that the CL design produces a smaller maximum impact force given the same lateral stiffness as well as other physical and geometric parameters. Furthermore, we compare the variable stiffness joint (VSJ) with the variable stiffness link (VSL) for various actuation parameters and design parameters. While decreasing stiffness of CJs cannot effectively reduce the maximum impact force, CL design is more effective in reducing impact force by varying the link stiffness. We conclude that the CL design potentially outperforms the CJ design in addressing safety in pHRI and can be used as a promising alternative solution to address the safety constraints in pHRI.

Objective The aim of this study is to measure drivers’ attention to preview and their velocity and acceleration tracking error to evaluate two- and three-dimensional displays for following a winding roadway. Background Display perturbation techniques and Fourier analysis of steering movements can be used to infer drivers’ spatio-temporal distribution of attention to preview. Fourier analysis of tracking error time histories provides measures of position, velocity, and acceleration error. Method Participants tracked a winding roadway with 1 s of preview in low-fidelity driving simulations. Position and rate-aided vehicle dynamics were paired with top-down and windshield displays of the roadway. Results For both vehicle dynamics, tracking was smoother with the windshield display. This display emphasizes nearer preview positions and has a closer correspondence to the control-theoretic optimal attentional distributions for these tasks than the top-down display. This correspondence is interpreted as a form of stimulus–response compatibility. The position error and attentional signal-to-noise ratios did not differ between the two displays with position control, but with more complex rate-aided control much higher position error and much lower attentional signal-to-noise ratios occurred with the top-down display. Conclusion Display-driven influences on the distribution of attention may facilitate tracking with preview when they are similar to optimal attentional distributions derived from control theory. Application Display perturbation techniques can be used to assess spatially distributed attention to evaluate displays and secondary tasks in the context of driving. This methodology can supplement eye movement measurements to determine what information is guiding drivers’ actions.

To reduce injury in physical human–robot interactions (pHRIs), a common practice is to introduce compliance to joints or arm of a robotic manipulator. In this paper, we present a robotic arm made of parallel guided beams whose stiffness can be continuously tuned by morphing the shape of the cross section through two four-bar linkages actuated by servo motors. An analytical lateral stiffness model is derived based on the pseudo-rigid-body model and validated by experiments. A physical prototype of a three-armed manipulator is built. Extensive stiffness and impact tests are conducted, and the results show that the stiffness of the robotic arm can be changed up to 3.6 times at a morphing angle of 37 deg. At an impact velocity of 2.2 m/s, the peak acceleration has a decrease of 19.4% and a 28.57% reduction of head injury criteria (HIC) when the arm is tuned from the high stiffness mode to the low stiffness mode. These preliminary results demonstrate the feasibility to reduce impact injury by introducing compliance into the robotic link and that the compliant link solution could be an alternative approach for addressing safety concerns of physical human–robot interactions.

Advanced driver assistance systems (ADAS) are a subject of increasing interest as they are being implemented on production vehicles and also continue to be developed and researched. These systems need to work cooperatively with human drivers to increase vehicle driving safety and performance. Such cooperation requires the ADAS to work with the specific driver with some knowledge of the human driver's driving behavior. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined lateral and longitudinal driver model developed based on human subject driving simulator experiments that is able to identify different driver behaviors through driver model parameter identification. The lateral driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. The longitudinal driver model works with the lateral driver model by using the same desired path parameters to model the driver's velocity control based on the relative velocity and relative distance to the preceding vehicle. A feedforward component is added to the feedback longitudinal driver model by considering the driver's ability to regulate his/her velocity based on the curvature of his/her desired path. This interconnection between the longitudinal and lateral driver models allows for fewer driver model parameters and an increased modeling accuracy. It has been shown that the proposed driver model can replicate individual driver's steering wheel angle and velocity for a variety of highway maneuvers.

Flexure mechanisms are the central part of precision instruments and devices for numerous science and engineering applications. Currently, design of flexure mechanisms often heavily relies on finite element analysis. However, the modeling complexity and low computational efficiency make it not suitable for the early design stage when many concepts need to be evaluated in a short period of time. To reduce the overhead in the conceptual design stage, we present a multi-segment energy minimization framework that integrates linear elastic theory for kinetostatic analysis of spatial flexure mechanisms. Compliance matrices for commonly used flexure elements are presented and their accuracy was studied and verified in detail. While deformation of each individual segment depends on the linear elastic theory, the multi-segment model allows accurate calculation of large deformations with high computational efficiency. To facilitate modeling of spatial flexure mechanisms, we have also implemented rich Graphical User Interfaces (GUI) in MATLAB environment. The proposed framework and software tool are tested with spatial mechanisms in which nonlinear kinematic constraints and combined loading are present. The examples showed that the proposed multi-segment framework can accurately capture large kinematic motion subjected to complex loading.

In recent years, significant emphases and efforts have been placed on developing and implementing advanced driver assistance systems (ADAS). These systems need to work with human drivers to increase vehicle occupant safety, control, and performance in both ordinary and emergency driving situations. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined driver model that is able to not only identify different individual driver behaviors, but also predict a driver's behavior in rare vehicle maneuvers such as collision avoidance (CA) based on his/her daily driving data. The driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. It has been shown that the proposed driver model can replicate each driver's steering wheel angle signal for a variety of highway and in-city maneuvers. The utility of the proposed driver model is its ability to predict a driver's steering wheel angle signal for a CA maneuver from only daily nonemergency driving data. The driver model is then validated by comparing two different drivers’ model parameter sets to the group average to show that each driver has a unique set of parameters. Finally, the driver model is validated by showing that its daily driving parameters differ from its predicted CA parameters.

In the past decades, numerous mechanics models and mathematical formulations have been developed for kinetostatic analysis of compliant mechanisms. However, it is rather tedious and error-prone to derive analysis equations based on these models. In this work, we present a general kinetostatic analysis framework for planar compliant mechanisms in which 2D beams can be represented by multiple segments of three commonly used models: beam-constraint-model (BCM), linear Euler–Bernoulli beam and pseudo-rigid-body models (PRBM). The framework is developed such that any beam model with a closed-form energy equation can be integrated without the deep understanding of the proposed scheme. The static equilibrium equations are automatically derived based on kinematic vector loop and solved based on minimization of total potential energy. Since the PRBM only returns the tip deflection, we have developed a procedure for calculating strain energy, actual beam shape and bending stresses from the tip deflection. This framework has been implemented DAS2D, an open-source object oriented software.

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.