Can the Shape of a Planar Pathway Be Estimated Using Proximal Forces of Inserting a Flexible Shaft?

The shape information of flexible endoscopes or other continuum structures, e.g., intro-vascular catheters, is needed for accurate navigation, motion compensation, and haptic feedback in robotic surgical systems. Existing methods rely on optical fiber sensors, electromagnetic sensors, or expensive medical imaging modalities such as X-ray fluoroscopy, magnetic resonance imaging, and ultrasound to obtain the shape information of these flexible medical devices. Here, we propose to estimate the shape/curvature of a continuum structure by measuring the force required to insert a flexible shaft into the internal channel/pathway of the continuum. We found that there is a consistent correlation between the measured insertion force and curvature of the planar continuum pathway. A testbed was built to insert a flexible shaft into a planar continuum pathway with adjustable shapes. The insertion forces, insertion displacement, and the shapes of the pathway were recorded. A neural network model was developed to model this correlation based on the training data collected on the testbed. The trained model, tested on the testing data, can accurately estimate the curvature magnitudes and the accumulated bending angles of the pathway simply based on the measured insertion force at the proximal end of the shaft. The approach may be used to estimate the curvature magnitudes and accumulated bending angles of flexible endoscopic surgical robots or catheters for accurate motion compensation, haptic force feedback, localization, or navigation. The advantage of this approach is that the employed proximal force can be easily obtained outside the pathway or continuum structure without any embedded sensor in the continuum structure. Future work is needed to further investigate the correlation between insertion forces and the pathway and enhance the capability of the model in estimating more complex shapes, e.g., spatial shapes with multiple bends.

Alternating optimization of design and stress for stress-constrained topology optimization

Handling stress constraints is an important topic in topology optimization. In this paper, we introduce an interpretation of stresses as optimization variables, leading to an augmented Lagrangian formulation. This formulation takes two sets of optimization variables, i.e., an auxiliary stress variable per element, in addition to a density variable as in conventional density-based approaches. The auxiliary stress is related to the actual stress (i.e., computed by its definition) by an equality constraint. When the equality constraint is strictly satisfied, an upper bound imposed on the auxiliary stress design variable equivalently applies to the actual stress. The equality constraint is incorporated into the objective function as linear and quadratic terms using an augmented Lagrangian form. We further show that this formulation is separable regarding its two sets of variables. This gives rise to an efficient augmented Lagrangian solver known as the alternating direction method of multipliers (ADMM). In each iteration, the density variables, auxiliary stress variables, and Lagrange multipliers are alternatingly updated. The introduction of auxiliary stress variables enlarges the search space. We demonstrate the effectiveness and efficiency of the proposed formulation and solution strategy using simple truss examples and a dozen of continuum structure optimization settings.

Multilevel Laser Induced Continuum Structure

Laser-induced-continuum-structure (LICS) allows for coherent control techniques to be applied in a Raman type system with an intermediate continuum state. The standard LICS problem involves two bound states coupled to one or more continua. In this paper, we discuss the simplest non-trivial multistate generalization of LICS which couples two bound levels, each composed of two degenerate states through a common continuum state. We reduce the complexity of the system by switching to a rotated basis of the bound states, in which different sub-systems of lower dimension evolve independently. We derive the trapping condition and explore the dynamics of the sub-systems under different initial conditions.

A MATLAB-Based Framework for Designing 3D Topology Optimized Soft Robotic Grippers

Soft robotic grippers are widely used in different industrial applications since they show great advantages in the adaptable grasping of objects with irregular shapes. However, as many soft grippers have a monolithic structure and gain their motion from the elastic deformation, it is difficult to use the conventional rigid-body mechanism theory to synthesize the shape of the soft grippers. To cope with this problem, the topology optimization is frequently employed as synthesis method since it can achieve automatic design of continuum-structure mechanisms. In this paper, we propose a novel 3D topology optimization framework in MATLAB to achieve automatic design of soft robotic grippers. Two design examples are also presented to illustrate the automatic synthesis process. Experimental tests have shown that the 3D topology optimized grippers in the example can successfully grasp objects with different shapes. In future work, the proposed framework can be further developed to synthesize soft robotic grippers with different actuation mechanisms and task-specific grasping behaviors.

A MATLAB-Based Framework for Designing 3D Topology Optimized Soft Robotic Grippers

Soft robotic grippers are widely used in different industrial applications since they show great advantages in the adaptable grasping of objects with irregular shapes. However, as many soft grippers have a monolithic structure and gain their motion from the elastic deformation, it is difficult to use the conventional rigid-body mechanism theory to synthesize the shape of the soft grippers. To cope with this problem, the topology optimization is frequently employed as synthesis method since it can achieve automatic design of continuum-structure mechanisms. In this paper, we propose a novel 3D topology optimization framework in MATLAB to achieve automatic design of soft robotic grippers. Two design examples are also presented to illustrate the automatic synthesis process. Experimental tests have shown that the 3D topology optimized grippers in the example can successfully grasp objects with different shapes. In future work, the proposed framework can be further developed to synthesize soft robotic grippers with different actuation mechanisms and task-specific grasping behaviors.

Kinematic accuracy of picking robot constructed by wire-driven continuum structure

This paper is to explore the motion accuracy of a novel picking robot constructed by wire-driven continuum structure and liner screw platform. The continuum structure features the hollow configuration, by which the object could be transported along its internal space, such as fruits harvesting. For discussing the precision theoretically and driving the presented robot, the kinematic model and workspace were established, also the instantaneous kinematics based on Jacobian matrix was analyzed. Further, the motion precision with different postures in the workspace were analyzed based on dexterity index by decomposing Jacobian matrix. Finally, as a typical tracking, the circular trajectory are deployed for demonstrating the theoretical modelling through the simulations and experiments in different region of workspace. The results of comparison of experiments and planned trajectories showed that the average error of the end point is about 2.538% with the proposed method by adjust the position of robot into a higher precise region, while without adjusting the robot's position, the average error of the end point reached 4.537%. The proposed method could be extended to a control strategy for rigid-flexible coupled robot.