Workspace Analysis of a Reconfigurable Mechanism Generated from the Network of Bennett Linkages

In this paper, a workspace triangle is introduced to evaluate the workspace of a reconfigurable mechanism generated from the network of Bennett linkages. Three evaluation indexes of workspace including movement locus of the joint, surface swept by the link and helical tube enveloped by the workspace triangle have been discussed. The comparison between the workspace of the reconfigurable mechanism and the sum of five resultant 5 R /6 R linkages including generalized Goldberg 5 R linkage, generalized variant of the L -shape Goldberg 6 R linkage, Waldron’s hybrid 6 R linkage, isomerized generalized L -shape Goldberg 6 R linkage and generalized Wohlhart’s double-Goldberg 6 R linkage is accomplished by using the evaluation indexes and mapping the workspace to the joint space which is defined by a vector whose components are joint variables

Reconfigurable mechanism generated from the network of Bennett linkages

A network of four Bennett linkages is proposed in this paper. Totally five types of overconstrained 5R and 6R linkages, including the generalized Goldberg 5R linkage, generalized variant of the L-shape Goldberg 6R linkage, Waldron's hybrid 6R linkage, isomerized case of the generalized L-shape Goldberg 6R linkage, and generalized Wohlhart's double-Goldberg 6R linkage, can be constructed by modifying this Bennett network. The 8R linkage formed by Bennett network serves as the basic mechanism to realise the reconfiguration among five types of overconstrained linkages by rigidifying some of the eight joints. The work also reveals the in-depth relationship among the Bennett-based linkages, which provides a substantial advancement in the design of reconfigurable mechanisms using overconstrained linkages

A Lumped-Mass Model for Large Deformation Continuum Surfaces Actuated by Continuum Robotic Arms

Currently, flexible surfaces enabled to be actuated by robotic arms are experiencing high interest and demand for robotic applications in various areas such as healthcare, automotive , aerospace, and manufacturing. However, their design and control thus far has largely been based on "trial and error" methods requiring multiple trials and/or high levels of user specialization. Robust methods to realize flexible surfaces with the ability to deform into large curvatures therefore require a reliable, validated model that takes into account many physical and mechanical properties including elasticity, material characteristics, gravity, external forces, and thickness shear effects. The derivation of such a model would then enable the further development of predictive-based control methods for flexible robotic surfaces. This paper presents a lumped-mass model for flexible surfaces undergoing large deformation due to actuation by continuum robotic arms. The resulting model includes mechanical and physical properties for both the surface and actuation elements to predict deformation in multiple curvature directions and actuation configurations. The model is validated against an experimental system where measured displacements between the experimental and modeling results showed considerable agreement with a mean error magnitude of about 1% of the length of the surface at the final deformed shapes

Embodiment design of soft continuum robots

This article presents the results of a multidisciplinary project where mechatronic engineers worked alongside biologists to develop a soft robotic arm that captures key features of octopus anatomy and neurophysiology. The concept of embodiment (the dynamic coupling between sensory-motor control, anatomy, materials and environment that allows for the animal to achieve adaptive behaviours) is used as a starting point for the design process but tempered by current engineering technologies and approaches. In this article, the embodied design requirements are first discussed from a robotic viewpoint by taking into account real-life engineering limitations; then, the motor control schemes inspired by octopus nervous system are investigated. Finally, the mechanical and control design of a prototype is presented that appropriately blends bio-inspiration and engineering limitations. Simulated and experimental results show that the developed continuum robotic arm is able to reproduce octopus-like motions for bending, reaching and grasping

Model-free control for continuum robots based on an adaptive Kalman filter

Continuum robots with structural compliance have promising potential to operate in unstructured environments. However, this structural compliance brings challenges to the controller design due to the existence of considerable uncertainties in the robot and its kinematic model. Typically, a large number of sensors are required to provide the controller the state variables of the robot, including the length of each actuator and position of the robot tip. In this paper, a model-free method based on an adaptive Kalman filter is developed to accomplish path tracking for a continuum robot using only pressures and tip position. As the Kalman filter operates only with a two-step algebraic calculation in every control interval, the low computational load and real-time control capability are guaranteed. By adding an optimal vector to the control law, buckling of the robot can also be avoided. Through simulation analysis and experimental validation, this control method shows good robustness against the system uncertainty and external disturbance, and lowers the number of sensors

Design of a pneumatic muscle based continuum robot with embedded tendons

© 1996-2012 IEEE. Continuum robots have attracted increasing focus in recent years due to their intrinsic compliance that allows for dexterous and safe movements. However, the inherent compliance in such systems reduces the structural stiffness, and therefore leads to the issue of reduced positioning accuracy. This paper presents the design of a continuum robot employing tendon embedded pneumatic muscles. The pneumatic muscles are used to achieve large-scale movements for preliminary positioning, while the tendons are used for fine adjustment of position. Such hybrid actuation offers the potential to improve the accuracy of the robotic system, while maintaining large displacement capabilities. A three-dimensional dynamic model of the robot is presented using a mass-damper-spring-based network, in which elastic deformation, actuating forces, and external forces are taken into account. The design and dynamic model of the robot are then validated experimentally with the help of an electromagnetic tracking system

Reliable and stable fundus image registration based on brain-inspired spatially-varying adaptive pyramid context aggregation network

The task of fundus image registration aims to find matching keypoints between an image pair. Traditional methods detect the keypoint by hand-designed features, which fail to cope with complex application scenarios. Due to the strong feature learning ability of deep neural network, current image registration methods based on deep learning directly learn to align the geometric transformation between the reference image and test image in an end-to-end manner. Another mainstream of this task aims to learn the displacement vector field between the image pair. In this way, the image registration has achieved significant advances. However, due to the complicated vascular morphology of retinal image, such as texture and shape, current widely used image registration methods based on deep learning fail to achieve reliable and stable keypoint detection and registration results. To this end, in this paper, we aim to bridge this gap. Concretely, since the vessel crossing and branching points can reliably and stably characterize the key components of fundus image, we propose to learn to detect and match all the crossing and branching points of the input images based on a single deep neural network. Moreover, in order to accurately locate the keypoints and learn discriminative feature embedding, a brain-inspired spatially-varying adaptive pyramid context aggregation network is proposed to incorporate the contextual cues under the supervision of structured triplet ranking loss. Experimental results show that the proposed method achieves more accurate registration results with significant speed advantage

Dynamic Capture Using a Traplike Soft Gripper With Stiffness Anisotropy

Dynamic capture is a common skill that humans have practiced extensively but is a challenging task for robots in which sensing, planning, and actuation must be tightly coordinated to deal with targets of diverse shapes, sizes, and velocity. In particular, the impact force may cause serious damage to a rigid gripper and even its carrier, e.g., a robotic arm. Existing soft grippers suffer from low speed and force to actively respond to capturing dynamic targets. In this article, we propose a soft gripper capable of efficient capture of dynamic targets, taking inspiration from the biological structures of multitentacled animals or plants. The presented gripper uses a cluster of tentacles to achieve an omnidirectional envelope and high tolerance to dynamic target during the capturing process. In addition, a stiffness anisotropy property is implemented to the tentacle structure to form a “trap” making it easy for the targets to enter yet difficult to escape. We also present an analytical model for the tentacle structure to describe its deformation during the collision with a target. In experiments, we construct a robotic prototype and demonstrate its ability to capture dynamic targets