Shape Of Robot Research Articles

Influence of the shape and mass of a small robot when thrown to a dummy human head

Social robots have shown some efficacy in assisting children with autism and are now being considered as assistive tools for therapy. The physical proximity of a small companion social robot could become a source of harm to children with autism during aggressive physical interactions. A child exhibiting challenging behaviors could throw a small robot that could harm another child’s head upon impact. In this paper, we investigate the effects of the mass and shape of objects thrown on impact at different velocities on the linear acceleration of a developed dummy head. This dummy head could be the head of another child or a caregiver in the room. A total of 27 main experiments were conducted based on Taguchi’s orthogonal array design. The data were then analyzed using ANOVA and then optimized based on the signal-to-noise ratio. Our results revealed that the two design factors considered (i.e. mass and shape) and the noise factor (i.e. impact velocities) affected the response. Finally, confirmation runs at the optimal identified shape and mass (i.e. mass of 0.3 kg and shape of either cube or wedge) showed an overall reduction in the resultant peak linear acceleration of the dummy head as compared to the other conditions. These results have implications on the design and manufacturing of small social robots whereby minimizing the mass of the robots can aid in mitigating the potential harm to the head due to impacts.

Gravity Compensation Modular Robot: Proposal and Prototyping

If a robot system can take various shapes, then it can play various roles, such as humanoid, dog robot, and robot arm. A modular robot is a robot system in which robots are configured using multiple modules, and it is possible to configure robots of other shapes by varying the combinations of the modules. In conventional modular robots, the shape is restricted by gravity, and configurable shapes are limited. In this study, we propose a gravity compensation modular robot to solve this problem. This paper describes the design and prototyping of the gravity compensation modular robot, and provides examples of robot shapes configured using the gravity compensation modules and motion experiments of the robots. In the experiments, there were motions that the robots could perform and could not perform. We considered the lack in the gravity compensation level and module rigidity as the main factor of the failures. This paper also discusses the solutions to these problems.

Reachability Improvement of a Climbing Robot Based on Large Deformations Induced by Tri-Tube Soft Actuators.

Locomotion of soft-bodied organisms, such as amoeba, worms, and octopuses, is safe, robust, and adaptable and has great promise for applications in complex environments. While such organisms fully exploit the potential provided by their soft structures, engineering solutions commonly constrain soft deformation in favor of controllability. In this study, we study how soft deformations can enhance the climbing capabilities of a robot. We introduce a robot called Longitudinally Extensible Continuum-robot inspired by Hirudinea (LEeCH), which has few shape constraints. Inspired by real leeches, LEeCH has a flexible extensible body and two suction cups at the ends. It is capable of performing 3D climbing locomotion using two suction cups driven by vacuum pumps and tri-tube soft actuators which have only three DC motors. The large deformations occurring in LEeCH extend its workspace compared to robots based on constant curvature models, and we show successful locomotion transition from one surface to another at angles between 0° and 180° in experiment. We develop a model based on multibody dynamics to predict the nonlinear deformations of the robot, which we verify in the experiment. The model reveals a nondimensional morphological parameter, which relates the robot's shape to its mass, stiffness, and size. The workspace of LEeCH as a function of this parameter is studied in simulation and is shown to move beyond that of robots based on constant curvature models.

Quantifying Dynamic Shapes in Soft Morphologies.

Soft materials are driving the development of a new generation of robots that are intelligent, versatile, and adept at overcoming uncertainties in their everyday operation. The resulting soft robots are compliant and deform readily to change shape. In contrast to rigid-bodied robots, the shape of soft robots cannot be described easily. A numerical description is needed to enable the understanding of key features of shape and how they change as the soft body deforms. It can also quantify similarity between shapes. In this article, we use a method based on elliptic Fourier descriptors to describe soft deformable morphologies. We perform eigenshape analysis on the descriptors to extract key features that change during the motion of soft robots, showing the first analysis of this type on dynamic systems. We apply the method to both biological and soft robotic systems, which include the movement of a passive tentacle, the crawling movement of two species of caterpillar (Manduca sexta and Sphacelodes sp.), the motion of body segments in the M. sexta, and a comparison of the motion of a soft robot with that of a microorganism (euglenoid, Eutreptiella sp.). In the case of the tentacle, we show that the method captures differences in movement in varied media. In the caterpillars, the method illuminates a prominent feature of crawling, the extension of the terminal proleg. In the comparison between the robot and euglenoids, our method quantifies the similarity in shape to ∼85%. Furthermore, we present a possible method of extending the analysis to three-dimensional shapes.

Manipulability analysis of a snake robot without lateral constraint for head position control

AbstractTwo dynamic manipulability criteria of a snake robot with sideways slipping are proposed with the application to head trajectory tracking control in mind. The singular posture, which is crucial in head tracking control, is characterized by the manipulability and examined for families of typical robot shapes. Differences in the singular postures from those of the robot with lateral constraints, which have not been clear in previous studies, are clarified in the analysis. In addition to the examination of local properties using the concept of manipulability, we discuss the effect of isotropic friction as a global property. It is well known that, at least empirically, a snake robot needs anisotropy in friction to move by serpentine locomotion if there are no objects for it to push around. From the point of view of integrability, we show one of the necessary conditions for uncontrollability is satisfied if the friction is isotropic.

A comprehensive static model of cable-driven multi-section continuum robots considering friction effect

Continuum robots featured by super dexterity and flexibility are excellent candidates for operations in confined environments. However, accurate modeling of continuum robots has been a challenge due to their large structural elasticity, attracting many research interests recently. This paper presents a comprehensive static model for continuum robots, considering most of the common forces and moments applied to the robot by both its own actuators and the external environments. Experimental results based on 68 tests show that the proposed model has high accuracy in describing the in-plane robot profile, but have certain errors for out-of-plane deformations. The average root-mean-square-error of the proposed model is 1.26 mm, which accounts for 1.82% of the total robot length. Moreover, the friction effect is investigated. Results show that actuating frictions could obviously change the robot deflection, and the static continuum robot shape is relevant to the historical cable tension status. Consequently, friction is vital to improve the static modeling accuracy of continuum robots.

Optimal shape design of an autonomous underwater vehicle based on multi-objective particle swarm optimization

Optimization of autonomous underwater vehicle’s shape is usually a multi-objective optimization problem, which is essential for autonomous underwater navigation and manipulation. To overcome the inefficiency of computational fluid dynamics software during the optimization process and the limitations of traditional single-objective optimization, a novel strategy combining genetic expression programming and crowding distance based multi-objective particle swarm algorithm is presented. Its central idea is as follows, several underwater vehicle shapes are analysed to obtain their water resistances and determine the best underwater robot shape. Shape factor of the bow and shape factor of the stern are employed as design variables, and sample points are selected by the optimal latin hypercube design. Then gene expression programming method is used to establish the surrogate model of resistance and surrounded volume. After that, the surrogate model based on the gene expression programming method is compared with that based on the surface respond method. The results show the superiority of the GEP method. Then the resistance and surrounded volume are set as two optimized variables and Pareto optimal solutions are obtained by using multi-objective particle swarm algorithm. Finally, the optimization results are compared with the hydrodynamic calculations, which shows the method proposed in the paper can greatly reduce the cost of computation and improve the efficiency of optimal shape design for underwater vehicle.

A Geometrically Exact Model for Soft Continuum Robots: The Finite Element Deformation Space Formulation.

Mathematical modeling of soft robots is complicated by the description of the continuously deformable three-dimensional shape that they assume when subjected to external loads. In this article we present the deformation space formulation for soft robots dynamics, developed using a finite element approach. Starting from the Cosserat rod theory formulated on a Lie group, we derive a discrete model using a helicoidal shape function for the spatial discretization and a geometric scheme for the time integration of the robot shape configuration. The main motivation behind this work is the derivation of accurate and computational efficient models for soft robots. The model takes into account bending, torsion, shear, and axial deformations due to general external loading conditions. It is validated through analytic and experimental benchmark. The results demonstrate that the model matches experimental positions with errors <1% of the robot length. The computer implementation of the model results in SimSOFT, a dynamic simulation environment for design, analysis, and control of soft robots.

Continuum robot shape estimation using permanent magnets and magnetic sensors

Continuum robots have attracted more attention owing to their safety and flexibility for confined and complex environment. In order to control the flexible continuum robot more accurately, it is necessary to track the shape of the continuum robot itself in real time. The feedback of the control system is precisely controlled to ensure the safety of the motion of the robot in the unknown environment. This paper proposes to integrate small permanent magnets and magnetic sensors into continuum robot to estimate the shape change. The sensors detect the posture change of magnets, the computer collects data through serial ports. By calculating the relative bending angle of magnets, the data can be imported into Matlab to fit the Quadratic Bézier curve to reconstruct the shape in 2D plane. Considering the interference of multiple magnets, the simulation software Maxwell is used to help arrange the magnets and magnetic sensors reasonably to decrease the significant interference in the sensed magnetic flux density. The experimental results show that the method is applicable to bending angles of 0°–90°. In this range, the maximum and minimum error of the terminal position is 9.339 mm and 0.227 mm respectively, which verifies certain accuracy and feasibility of this method.

Modeling Tube Clearance and Bounding the Effect of Friction in Concentric Tube Robot Kinematics.

The shape of a concentric tube robot depends not only on the relative rotations and translations of its constituent tubes, but also on the history of relative tube displacements. Existing mechanics-based models neglect all history-dependent phenomena with the result that when calibrated on experimental data collected over a robot's workspace, the maximum tip position error can exceed 8 mm for a 200-mm-long robot. In this paper, we develop a model that computes the bounding kinematic solutions in which Coulomb friction is acting either to maximize or minimize the relative twisting between each pair of contacting tubes. The path histories associated with these limiting cases correspond to first performing all tube translations and then performing relative tube rotations of sufficient angle so that the maximum Coulomb friction force is obtained along the interface of each contacting tube pair. The robot tip configurations produced by these path histories are shown experimentally to bound position error with respect to the estimated frictionless model compared to path histories comprised of translation or mixed translation and rotation. Intertube friction forces and torques are computed as proportional to the intertube contact forces. To compute these contact forces, the standard zero-clearance assumption that constrains the concentrically combined tubes to possess the same centerline is relaxed. The effects of clearance and friction are explored through numerical and physical experiments and it is shown that friction can explain much of the prediction error observed in existing models. This model is not intended for real-time control, but rather for path planning-to provide error bounds and to inform how the ordering of tube rotations and translations can be used to reduce the effect of friction.

Design of an Interactive Control System for a Multisection Continuum Robot

Continuum robots exhibit unique advantages of flexibility and maneuverability in an unstructured environment. This paper presents the development of a human-robot interactive control system that includes two operational modes for controlling end-effector position and the entire robot shape with image feedback. To control the end-effector position, all the desired positions of the end effector fall within a reachable workspace constructed with an approximate boundary. To control the robot shape, a shape correspondence between the robot shape and the desired curve is determined to assist in examining whether the robot can follow the desired path with an acceptable collision with the environment. This correspondence can also assist users in robot motion planning. The proposed interactive control system ensures that the robot end effector follows the desired path, while the robot shape is regulated based on visual feedback. Experiments are performed to demonstrate the effectiveness of the proposed approach.

Vine-Inspired Continuum Tendril Robots and Circumnutations

Smooth-backboned “continuum” robot structures offer novel ways to create robot shapes and movements. In this paper, we show how circumnutation, a motion strategy commonly employed by plants, can be implemented and usefully exploited with continuum robots. We discuss how the kinematics of circumnutation, which combines local backbone growth with periodic backbone bending, can be created using extensible continuum robot hardware. The underlying kinematics are generated by adapting kinematic models of plant growth. We illustrate the effectiveness of that approach with experimental results with a tendril-like robot exploring a congested environment.

Motion planning under uncertainty in graduated fidelity lattices

We present a new approach to motion planning in mobile robotics under sensing and motion uncertainty based on state lattices with graduated fidelity. Uncertainty is predicted at planning time and used to estimate the safety of the paths. Our approach takes into account the real shape of the robot, introducing a deterministic sampling based method to estimate the probability of collision. Anytime Dynamic A*, an informed search algorithm, is used to find safe and optimal paths in the lattice. Moreover, due to the anytime search capabilities of this algorithm our planner is able to retrieve a solution very fast and refine it iteratively until the optimal one is found. We present a novel graduated fidelity approach to build a lattice whose complexity adapts to the obstacles in the environment, along with a multi-resolution heuristic based on the same idea. Thus, the running time of the planner is drastically reduced while maintaining its performance. Experimental results show the potential of the approach in several scenarios, with different robot shapes, motion models and under different uncertainty conditions. The impact of the graduated fidelity approach and the multi-resolution heuristic in the efficiency and performance of the planner is also detailed.

(Invited) Dynamic Evolution for Soft Matter Robot: Integrated Fabrication of Geometric and Motion of Gel-like Robot with Embedded Virtual Link Structure

The current 3D printing technology can easily fabricate a body of soft robot composed of various materials. The body of these soft robots has many advantages from the viewpoint of physical interference due to contact using the soft material. Soft robot research in the field of robotics has been reporting a control method to efficiently drive a mechanical structure capable of high freedom movement by combining rigid mechanical parts. However, to transfer these technologies to soft matter science, we are still facing on many problems. As one of the problems, in the academic field of chemistry and robotics, the design concept of soft robot material, geometric structure and movement is not unified. In particular, heterogeneous hybrid materials and link mechanisms are technically separated and discussed. In this study, from the viewpoint of informatics, we integrate the technologies of both by embedding a virtual link structure in the body of soft material. In addition it is an object to create a numerical simulation model for implementing geometry design and physical computation of the soft robot to gradually and simultaneously adapt to external environment. This is because geometry and kinematics for soft robots are not yet established. We have adopted a voxel-based physics calculation library for simulation model. It is open source called Voxelyze. The geometric shape of the soft robot's physical model is created by neural evolution which develops optimization by neural network and evolutionary calculation. When the optimized neural network inputs the coordinate information of the three-dimensional design space, it outputs a three-dimensional physical model composed of voxels of dissimilar materials. Each constitution voxel of the model includes those having an actuator function. It is dominated by the link structure between the corresponding voxels having this actuator function. Our simulation model enables 3D modeling considering the motion of robot which needs time information. In addition, we can find guidelines for simultaneously acquiring structural stability and task performance of soft robot.

Three-Dimensional Printed Wearable Sensors with Liquid Metals for Detecting the Pose of Snakelike Soft Robots.

Liquid metal (LM)-based flexible sensors, which utilize advanced liquid conductive materials to serve as sensitive elements, are emerging as a promising solution to measure large deformations. Nowadays, one of the biggest challenges for precise control of soft robots is the detection of their real-time positions. Existing fabrication methods are unable to fabricate flexible sensors that match the shape of soft robots. In this report, we first described a novel 3D printed multifunction inductance flexible and stretchable sensor with LMs, which is capable of measuring both axial tension and curvature. This sensor is fabricated with a developed coaxial LM 3D printer by coprinting of silicone rubber and LMs. Because of the solenoid shape, this sensor can be easily installed on snakelike soft robots and can accurately distinguish different degrees of tensile and bending deformations. We determined the structural parameters of the sensor and proved its excellent stability and reliability. As a demonstration, we used this sensor to measure the curvature of a finger and feedback the position of an endoscope, a typical snakelike structure. Because of its bending deformation form consistent with the actual working status of the soft robot and unique shape, this sensor has better practical application prospects in the pose detection.

Deciding Shapes and Motions of a Robot based on Personal Preferences

User preferences for robots differ according to the robot shape and how it moves. This paper proposes a method for generating motions that will be pleasing to a user of a modular robot that can be configured arbitrarily. We propose a preference estimator that uses supervised learning to estimate how well users will like a combination of a motion pattern and robot shape. We also propose a motion selector and a motion generator that select and generate motions in consideration of the balance of the robot's shape and its motion. These methods were tested with a prototype modular robot system and a group of human test subjects who configured the robot as they wished. The proposed motion selector and generator, trained on a preprogrammed set of motion patterns, then presented a ranked set of motions to the subjects. The motion selector proved effective but the motion generator did not. Overall, the proposed system was received favorably by the subjects.

Studying slippage on pushing applications with snake robots

In this paper, a framework for analyzing the motion resulting from the interaction between a snake robot and an object is shown. Metrics are derived to study the motion of the object and robot, showing that the addition of passive wheels to the snake robot helps to minimize slippage. However, the passive wheels do not have a significant impact on the force exerted onto the object. This puts snake robots in a similar framework as robotic arms, while considering special properties exclusive to snake robots (e.g., lack of a fixed-base, interaction with the environment through friction). It is also shown that the configuration (shape) of the snake robot, parameterized with the polar coordinates of the robot’s COM, plays an important role in the interaction with the object. Two examples, a snake robot with two joints and another with three joints, are studied to show the applicability of the model.

Novel Integrated Helical Design of Single Optic Fiber for Shape Sensing of Flexible Robot

With the evolution of technology, it is now possible to build more flexible surgical instruments with multiple degrees of freedom that can be deployed in natural orifice translumenal endoscopic surgery, further reducing tissue damage, blood loss, and recovery time. However, the limits of flexible robots are mainly represented by the lack of accurate position feedback during the surgical procedure. The most critical problems of shape sensors are size and quantity limits, as well as sterilizability. This paper presents a novel helical configuration of single optic fiber with multiple fiber Bragg grating sensors to measure the shape of flexible robots. Detailed design of helix parameters, simulation and verification experiments were performed. We have shown the feasibility of helical configuration to reduce the effect of temperature, and detect the torsion and bending of the flexible shaft. The experimental results demonstrated the potential to provide a shape detection for flexible robot.

Probabilistic Automata Model of a Soft Robot for the Planning of Manipulation Tasks

Soft robots must be able to structure an automation problem into a sequence of actions that lead to a desired state, before they can fulfill a meaningful role in automation applications. This, however, can only be successful if the robot can predict the outcome of an action. The theory of rigid industrial robots is not applicable without major changes, because kinematic chains do not adequately describe the continuous deformation of the complex, often biologically inspired shapes of soft robots. Analytic solutions have not been found yet. Numerical solutions based on finite elements are slow, technically challenging, and only suitable for one specific robot. It is, however, possible to observe the outcome of an action, and use these observations to plan a sequence of actions that let the robot accomplish an automation task. In this paper, we analyze a probabilistic automaton that computes the optimal sequence of actions to bring the robot into a desired state. An earlier article explained the functioning of the method in a toy example. In this paper, we analyze if it is feasible to apply the method to a planning problem inspired by a real soft robot. We show the results and document the planning process. We identify the analog of an impulse response, although it is not closed form due to the nonparametric nature of the method. Note to Practitioners —A soft robotic sorting table has a computer-controlled soft surface that can move delicate objects without damaging them. There are currently no closed-loop control systems for such robots, because it is unclear how to relate the control signals to the behavior of the table, or which actions to choose in order to solve a manipulation task. In this paper, we propose a probabilistic automaton to plan the best action sequence on average. The sequence brings the workpieces on top of a soft table into a desired condition. It is a machine learning solution that is based on observations of the input signals and their effect, rather than a detailed analytical or numerical modeling of the robot. We show that it is feasible to model an existing soft robotic table. We demonstrate that the planning is successful by solving complex maze tasks. Our results are based on experiments and simulations.

Online Gait Learning for Modular Robots with Arbitrary Shapes and Sizes.

Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.