Robot Geometry Research Articles

Image‐based backbone reconstruction for non‐slender soft robots

AbstractOne advantage of soft robots to conventional robots is their continuous deformation, which makes them highly adaptable to their surroundings. To describe the kinematics of soft robots it is not sufficient to only know the position of the end‐effector. Instead, the steady state of a soft robot can be described by its backbone curvature. An image‐based backbone reconstruction method is used and adopted for a non‐slender soft robot to reconstruct the backbone curve using polynomials to describe an arc length‐dependent curvature. A constant curvature and cubic curvature polynomial have been tested to reconstruct the backbone curve. In the case of a pressurized soft robot without externally applied loads, the constant curvature polynomial can be used to reconstruct the backbone of the soft robot using an image‐based approach, yielding good agreement with the captured images. However, some care has to be put into the consideration of the soft robot geometry in this method, to guide the optimization procedure. From our findings, we can conclude that it is not necessary to apply markers to the soft robot to reconstruct the backbone, nor is it necessary to have a detailed geometric model of the soft robot to be used in a differential rendering process. An image‐based iterative‐closest‐point optimization method, with some carefully placed estimate points, to integrate the volumetric structure of the soft robot can yield good results for the reconstructed backbones.

Locomotion analysis of a crawling wave robot in circular canal

In this paper, we analyze the locomotion of a wave robot crawling in a curved canal between rigid walls. We determine the conditions for crawling, considering the robot geometry, orientation, wall curvature, canal width, and friction coefficients. Initially, we present an analytical model of the different cases of crawling and conditions for advancement. We found four cases of crawling: one where the robot touches only the outer wall, two with one contact point each on the outer and inner walls, and a third where the robot has two contact points with the outer wall and one with the inner wall. Subsequently, we introduce a multibody numerical simulation, which accounts for sliding along the wall and over the ground surface. The simulation allows one to validate our analytical results and analyze the robot's behavior under different conditions. Finally, we present validating results of multiple experiments conducted with our SAW robot. This analysis encompasses all types of crawling robots with internal thrust mechanisms that are not based on contact with the side walls of the pipe or canal.

Amphibious Robots Locomotion Strategies in Unstructured Complex Environments: A Review

In the previous literature, amphibious robots focused mainly on locomotion in underwater and flat land surface manoeuvring. Few amphibious robots focused on unstructured land environments. The amphibious robot designs were more emphasised in academics, leading to more work done in building amphibious robots that mimic biological amphibians, imitating the geometry and overall functionality of the amphibious robots. Developing amphibious robots with propulsive mechanisms for manoeuvring in a water environment received more attention than other functionalities like adaptability on rough natural terrain and obstacle repositioning capability. However, practical applications like reconnaissance and surveying posed challenges in the ground environment, which had unstructured and complex terrain profiles, especially in the transition area. Therefore, reviewing the amphibious robots focused on manoeuvring complex uneven surfaces was essential. The literature had comprehensive review papers on navigation strategies encompassing manoeuvring on flat ground surfaces and underwater locomotion. There was a need for a focused study that highlighted the amphibious robot that manoeuvred in an unstructured land environment. The open challenges and recent solutions by designing new mechanisms and deployment issues were highlighted and reviewed. Hence, the paper addressed a more specific review of amphibious robot locomotion in an unstructured environment. The paper also discussed a case study of an amphibious robot capable of locomotion in unstructured environments. It was envisaged that the review would provide directions and insights to researchers and robotic system designers on developing robust propulsive mechanisms for amphibious robots capable of locomotion in unstructured environments.Keywords: manoeuvrability, mobility, terrain, unified locomotion, mechanism, propulsion

Active collision avoidance for teleoperated multi-segment continuum robots toward minimally invasive surgery

Collision avoidance presents a challenging problem for multi-segment continuum robots owing to their flexible structure, limited workspaces, and restricted visual feedback, particularly when they are used in teleoperated minimally invasive surgery. This study proposes a comprehensive control framework that allows these continuum robots to automatically avoid collision and self-collision without interfering with the surgeon’s control of the end effector’s movement. The framework implements the early detection of collisions and active avoidance strategies by expressing the body geometry of the multi-segment continuum robot and the differential kinematics of any cross-section using screw theory. With the robot’s parameterized shape and selected checkpoints on the obstacle’s surface, we can determine the minimum distance between the robot and arbitrary obstacle, and locate the nearest point on the robot. Furthermore, we expand the null-space-based control method to accommodate redundant, non-redundant, and multiple continuum robots. An assessment of the avoidance capability is provided through an instantaneous and global criterion based on ellipsoids and possible movement ranges. Simulations and physical experiments involving continuum robots of different degrees of freedom performing various tasks were conducted to thoroughly validate the proposed framework. The results demonstrated its feasibility and effectiveness in minimizing the risk of collisions while maintaining the surgeon’s control over the end effector.

CUSTOMIZING TEXTILE AND TACTILE SKINS FOR INTERACTIVE INDUSTRIAL ROBOTS

Abstract Textiles are a promising material for building tactile skins because they are lightweight, flexible, and can conform to complex surfaces. They enhance robot-human interaction by localizing contact points and measuring contact forces. This paper presents a solution for rapidly fabricating, calibrating, and deploying these skins on industrial robot arms. The novel automated skin calibration procedure maps skin locations to robot geometry and calibrates contact force. Through experiments on a FANUC LR Mate 200id/7L industrial robot, we demonstrate that tactile skins made from textiles can be effectively used for human-robot interaction in industrial environments. We demonstrated the following: 1) modifying trajectories using human contact signals and 2) improving force interactions with humans using tactile skins This work presents a comprehensive solution for rapidly fabricating, calibrating, and deploying textile and tactile skins for interactive industrial robots. Our contributions are as follows: 1) we introduce a method for determining skin parameters based on robot geometry; 2) we propose an automated skin calibration process that calibrates both the position and force reading from the skin to the robot; and 3) we demonstrate how the skins can be integrated into robot control and learning to improve the safety and interactivity of industrial robots through experiments on a FANUC LR Mate 200id/7L industrial robot arm.

Increasing step size of the 4-link robotic simulator in stable state

In this research, the 4-Link Robotic Simulator program in MATLAB is utilized to find the relation between the length of the links and the length of the two feet. While a few previous research focused on the zero-moment point (ZMP) of the robot geometry, this neglects the change of the position of ZMP in the processes of locomotion. The simulation model is introduced to present an integrated locomotion process of the robot. The testing method was developed based on the comparison between the simulation results from MATLAB. Three different scenarios are analyzed and compared to the original outcome. The main finding of this research is concluded as the length of L5 and L6 should not be smaller than the length of L2 and L3. The critical length of the L5 and L6 was determined as 3.0065.

Pose Determination System for a Serial Robot Manipulator Based on Artificial Neural Networks

Achieving the highest levels of repeatability and precision, especially in robot manipulators applied in automation manufacturing, is a practical pose-recognition problem in robotics. Deviations from nominal robot geometry could produce substantial errors at the end effector, which can be more than 0.5 inches for a 6 ft robot arm. In this research, a pose-recognition system is developed for estimating the position of each robot joint and end-effector pose using image processing. To generate the joint angle, the system is developed via the modeling of a pose obtained by combining a convolutional neural network (CNN) and a multi-layer perceptron network (MLP). The CNN categorizes the input image generated by a remote monocular camera and generates a classification probability vector. The MLP generates a multiple linear regression model based on the probability vector generated by a CNN and describes the values of each joint angle. The proposed model is compared with the P-n-Perspective problem-solving method, which is based on marker tracking using ArUco markers and the encoder values. The system was verified using a robot manipulator with four degrees of freedom. Additionally, the proposed method exhibits superior performance in terms of joint-by-joint error, with an absolute error that is three units less than that of the computer vision method. Furthermore, when evaluating the end-effector pose, the proposed method showed a lower average standard deviation of 9mm compared with the computer vision method, which had a standard deviation of 13 mm.

Stretchable e-skin and transformer enable high-resolution morphological reconstruction for soft robots

Many robotic tasks require knowledge of the exact 3D robot geometry. However, this remains extremely challenging in soft robotics because of the infinite degrees of freedom of soft bodies deriving from their continuum characteristics. Previous studies have achieved only low proprioceptive geometry resolution (PGR), thus suffering from loss of geometric details (for example, local deformation and surface information) and limited applicability. Here we report an intelligent stretchable capacitive e-skin to endow soft robots with high PGR (3,900) bodily awareness. We demonstrate that the proposed e-skin can finely capture a wide range of complex 3D deformations across the entire soft body through multi-position capacitance measurements. The e-skin signals can be directly translated to high-density point clouds portraying the complete geometry via a deep architecture based on transformer. This high PGR proprioception system providing millimetre-scale, local and global geometry reconstruction (2.322 ± 0.687 mm error on a 20 × 20 × 200 mm soft manipulator) can assist in solving fundamental problems in soft robotics, such as precise closed-loop control and digital twin modelling. Developing proprioception systems for flexible structures such as soft robots is a challenge. Hu et al. report a stretchable e-skin for soft robot proprioception. Combined with deep learning, the e-skin enables high-resolution 3D geometry reconstruction of the soft robot and can be applied in many scenarios, such as human–robot interaction.

Occurrence of Complex Behaviors in the Uncontrolled Passive Compass Biped Model

It is widely known that an appropriately built unpowered bipedal robot can walk down an inclined surface with a passive steady gait. The features of such gait are determined by the robot's geometry and inertial properties, as well as the slope angle. The energy needed to keep the biped moving steadily comes from the gravitational potential energy as it descends the inclined surface. The study of such passive natural motions could lead to ideas for managing active walking devices and a better understanding of the human locomotion. The major goal of this study is to further investigate order, chaos and bifurcations and then to demonstrate the complexity of the passive bipedal walk of the compass-gait biped robot by examining different bifurcation diagrams and also by studying the variation of the eigenvalues of the Poincaré map's Jacobian matrix and the variation of the Lyapunov exponents. We reveal also the exhibition of some additional results by changing the inertial and geometrical parameters of the bipedal robot model.

INCREASE OF INDUSTRIAL ROBOT ACCURACY BASED ON KINEMATIC ERRORS COMPENSATION

Industrial robots are used in many technical applications from simple pick-and-place tasks to complex machining, welding and assembly applications. The repeatability of the robots is usually high. However, the accuracy is much lower, and it decreases with the robot size selected. Robot calibration represents a possible way to increase accuracy, and two main approaches have been distinguished. Kinematic calibration deals with geometric errors only, and the robot is considered as a rigid body. By contrast, non-kinematic calibration takes into account further sources of errors. This paper deals with kinematic calibration, where an artifact is attached to a robot flange and its position is measured using a laser tracker. The novelty of the method is based on the consecutive rotation of only a single joint, where the artifact trajectory is circular. Real robot geometry is calculated based on identified circles. Numerical simulations seem promising, as well as verification with Stäubli TX2-90, where the accuracy was increased by more than 43%.

Directional Sensor Planning for Occlusion Avoidance

Directional sensors, such as video cameras, have become ubiquitous to many autonomous robots applications, such as monitoring and surveillance. The performance of these sensors and processing algorithms, however, may be hindered by the presence of objects that block visibility. This article presents a novel approach for planning the path of a mobile directional sensor deployed to observe multiple targets distributed in an environment populated with multiple obstacles and occlusions. Unlike existing art gallery or watchman’s route methods, the visibility theory and motion planners developed in this article account for both line-of-sight visibility and bounded field-of-view constraints, and can provide obstacle avoidance based on robot geometry and kinodynamic constraints. The computational complexity analysis and experiments on a camera-equipped drone demonstrate that, despite the challenging geometric characteristics of directional C-targets, the approach scales to real-world problems. Furthermore, when compared to algorithms inspired by traveling salesman and target coverage approaches, the directional visibility planners presented in this article are significantly more effective both at guaranteeing complete target visibility and at minimizing distance traveled.

Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation.

The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. The tracking system consists of two parts: image preprocessing and machine learning. In the machine learning part, we compare two approaches: fitting the robot pose to the point cloud and fitting the convolutional neural network model to the sparse 3D depth images. The advantage of the presented approach is direct use of the point cloud transformed to the sparse image in the network input and use of sparse convolutional and pooling layers (sparse CNN). The experiments confirm that the robot tracking is performed in real time and with an accuracy comparable to the accuracy of the depth sensor.

Convex Iteration for Distance-Geometric Inverse Kinematics

Inverse kinematics (IK) is the problem of finding robot joint configurations that satisfy constraints on the position or pose of one or more end-effectors. For robots with redundant degrees of freedom, there is often an infinite, nonconvex set of solutions. The IK problem is further complicated when collision avoidance constraints are imposed by obstacles in the workspace. In general, closed-form expressions yielding feasible configurations do not exist, motivating the use of numerical solution methods. However, these approaches rely on local optimization of nonconvex problems, often requiring an accurate initialization or numerous re-initializations to converge to a valid solution. In this work, we first formulate inverse kinematics with complex workspace constraints as a convex feasibility problem whose low-rank feasible points provide exact IK solutions. We then present \texttt{CIDGIK} (Convex Iteration for Distance-Geometric Inverse Kinematics), an algorithm that solves this feasibility problem with a sequence of semidefinite programs whose objectives are designed to encourage low-rank minimizers. Our problem formulation elegantly unifies the configuration space and workspace constraints of a robot: intrinsic robot geometry and obstacle avoidance are both expressed as simple linear matrix equations and inequalities. Our experimental results for a variety of popular manipulator models demonstrate faster and more accurate convergence than a conventional nonlinear optimization-based approach, especially in environments with many obstacles.

Analysis of climbing in circular and rectangular pipes with a reconfigurable sprawling robot

This paper presents an analytical description and experimental results for a reconfigurable field robot that can climb inside circular and rectangular pipes. The robot is fitted with two mechanisms that allow it to change its width and height and shift its center of mass (COM) to adapt itself to the size of the pipe. We start by describing the kinematic model of the robot as a function of its sprawl and four bar extension mechanism (FBEM). Next, we develop a force analysis based on the robot's geometry, its configuration, the position of its center of mass (COM), the diameter of the pipe, and the coefficient of friction (COF). We then develop strategies for driving, climbing and transitioning between the two modes. Although a high COF increases the robot's grip, it reduces its ability to reconfigure its shape, which it needs to transition between its climbing/driving modes. Based on this analysis, we designed a control algorithm comprised of actuation sequences to automatically drive the robot inside pipes, including the transition phases. The results show that the robot successfully executed its climbing tasks (see video).

Kinematic and dynamic analysis of an omnidirectional mobile platform driven by a spherical wheel

Abstract. The increased use of spherical wheels has allowed mobile robots to have a higher degree of maneuverability, less complex path planning and less complex control schemes. The geometry and design of the mobile robot are the principal attributes that guarantee an omnidirectional motion. Furthermore, the platform uses an active spherical wheel and four passive spherical wheels to get the best stability when the robot uses a terminal element (Kärcher). The proposed model has been designed to improve the omnidirectional motion issues, such as vibration into the platform or lack of punctual contact between the wheel and the floor, compared to mobile robots using Mecanum wheels and more than one active wheel; due to the design concept, all the mathematical formulations, kinematics and dynamics presents how the models are validated with computer simulations.

Closed-Loop Kinematic Calibration of Robots Using a Six-Point Measuring Device

The low absolute positioning accuracy of robots has limited their applications for many precision tasks. Although the robot accuracy can be improved through kinematic calibration, traditional calibration approaches need either expensive external measurement devices or tedious manual intervention. To overcome these issues, this work proposes a closed-loop kinematic calibration method for robots using a six-point measuring device. This device consists of six displacement sensors to determine the pose of a gauge block as a reference workpiece mounted on the robot end-effector. A comprehensive error model is derived to calibrate the kinematic parameters including the Denavit-Hartenberg parameters of the robot geometry and the pose of the robot base. Then, the Levenberg-Marquard algorithm is adopted to identify these parameters. An experimental study has been conducted on a UR10 robot, combining a CMM to verify the calibration results. It is found that the maximum and average distance errors of the robot movement could be significantly decreased from over 5.60 mm and 2.50 mm to approximately 0.50 mm and 0.30 mm, respectively. Benefited from the fact that only six displacement sensors are used, the proposed method is cost effective. Also, the proposed calibration process can be completed without tedious manual teaching and thus easy to achieve autonomous calibration.

A Simulation Study of a Planar Cable-Driven Parallel Robot to Transport Supplies for Patients with Contagious Diseases in Health Care Centers

Currently, a large number of investigations are being carried out in the area of robotics focused on proposing solutions in the field of health, and many of them have directed their efforts on issues related to the health emergency due to COVID-19. Considering that one of the ways to reduce the risk of contagion is by avoiding contact and closeness between people when exchanging supplies such as food, medicine, clothing, etc., this work proposes the use of a planar cable-driven parallel robot for the transport of supplies in hospitals whose room distribution has planar architecture. The robot acts in accordance with a procedure proposed for each task to be carried out, which includes the process of disinfection (based on Ultraviolet-C light) of the supplies transported inside the robot’s end effector. The study presents a design proposal for the geometry of the planar cable-driven parallel robots and its end effector, as well as the software simulations that allow evaluating the robot’s movement trajectories and the responses of the position control system based on Fuzzy-PID controllers.

A Survey of Path Planning Algorithms for Mobile Robots

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

Trajectory Planner CDT-RRT* for Car-Like Mobile Robots toward Narrow and Cluttered Environments.

In order to achieve the safe and efficient navigation of mobile robots, it is essential to consider both the environmental geometry and kinodynamic constraints of robots. We propose a trajectory planner for car-like robots on the basis of the Dual-Tree RRT (DT-RRT). DT-RRT utilizes two tree structures in order to generate fast-growing trajectories under the kinodynamic constraints of robots. A local trajectory generator has been newly designed for car-like robots. The proposed scheme of searching a parent node enables the efficient generation of safe trajectories in cluttered environments. The presented simulation results clearly show the usefulness and the advantage of the proposed trajectory planner in various environments.

A Simulator for Testing Planar Upper Extremity Rehabilitation Robot Control Algorithms

In this study, we took advantage of the emergence of accurate biomechanical human hand models to develop a system in which the interaction between a human arm and a rehabilitation robot while performing a planar trajectory tracking task can be simulated. Seven biomechanical arm models were based on the 11-degree-of-freedom Dynamic Arm Simulation model and implemented in OpenSim. The model of the robot was developed in MatlabSimulink and interaction between the arm and robot models was achieved using the OpenSim API. The models were tested by simulating the performance of each model while moving the end effector of a simulated planar robot model through an elliptical trajectory with an eccentricity of 0.94. Without assistance from the robot, the average root-mean-square error (RMSE) for all subjects was 3.98 mm. With the simulated robot providing assistive torque, the average RMSE error reduced to 2.88 mm. The test was repeated after modifying the length of the robot links, and an average RMSE of 2.91 mm recorded. A single-factor ANOVA test revealed that there was no significant difference in the RMSE for the two different robot geometries (p-value = 0.479), revealing that the simulator was not sensitive to robot geometry.