Development Of Robotic System Research Articles

GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction outcomes. To apply this technique, a set of selected atoms is initialized by setting fuzzy threshold. Subsequently, inappropriate atoms are excluded, and an iterative procedure is initiated to update the set so as to approximate the signal sparsity in a stepwise fashion. In comparison with commonly used algorithms, the proposed algorithm achieved the lowest signal recovery and reconstruction error. Findings from this study indicate that our proposed hybrid paradigm may lead to positive advancement towards the development of intelligent robotic vision systems for industrial applications.

RoboEthics in COVID-19: A Case Study in Dentistry.

The COVID-19 pandemic has caused dramatic effects on the healthcare system, businesses, and education. In many countries, businesses were shut down, universities and schools had to cancel in-person classes, and many workers had to work remotely and socially distance in order to prevent the spread of the virus. These measures opened the door for technologies such as robotics and artificial intelligence to play an important role in minimizing the negative effects of such closures. There have been many efforts in the design and development of robotic systems for applications such as disinfection and eldercare. Healthcare education has seen a lot of potential in simulation robots, which offer valuable opportunities for remote learning during the pandemic. However, there are ethical considerations that need to be deliberated in the design and development of such systems. In this paper, we discuss the principles of roboethics and how these can be applied in the new era of COVID-19. We focus on identifying the most relevant ethical principles and apply them to a case study in dentistry education. DenTeach was developed as a portable device that uses sensors and computer simulation to make dental education more efficient. DenTeach makes remote instruction possible by allowing students to learn and practice dental procedures from home. We evaluate DenTeach on the principles of data, common good, and safety, and highlight the importance of roboethics in Canada. The principles identified in this paper can inform researchers and educational institutions considering implementing robots in their curriculum.

An Active Steering Hand-held Robotic System for Minimally Invasive Orthopaedic Surgery Using a Continuum Manipulator.

This paper presents the development and experimental evaluation of an active steering hand-held robotic system for milling and curved drilling in minimally invasive orthopaedic interventions. The system comprises a cable-driven continuum dexterous manipulator (CDM), an actuation unit with a handpiece, and a flexible, rotary cutting tool. Compared to conventional rigid drills, the proposed system enhances dexterity and reach in confined spaces in surgery, while providing direct control to the surgeon with sufficient stability while cutting/milling hard tissue. Of note, for cases that require precise motion, the system is able to be mounted on a positioning robot for additional controllability. A proportional-derivative (PD) controller for regulating drive cable tension is proposed for the stable steering of the CDM during cutting operations. The robotic system is characterized and tested with various tool rotational speeds and cable tensions, demonstrating successful cutting of three-dimensional and curvilinear tool paths in simulated cancellous bone and bone phantom. Material removal rates (MRRs) of up to 571 mm3/s are achieved for stable cutting, demonstrating great improvement over previous related works.

INCREASE IN THE ELECTROMAGNETIC MOMENT OF A SUPERMINIATURE ELECTRIC MOTOR EXCITED BY RARE EARTH PERMANENT MAGNETS

The article is devoted to improving the energy and performance characteristics of superminiature electric motors, which are widely used in modern devices of robotics and mechanotronics. With the development of digital and Autonomous robotic systems, the tasks of improving the efficiency of Executive micromechanisms that affect the functionality and duration of work in offline mode have become particularly relevant. Traditional design and technological solutions used in higher-power electric machines are not scalable to the field of superminiature electric machines. Domestic and foreign developers offer various design options and manufacturing technologies. The key design feature of the electric motor under consideration is a glass stator made by polycapillary fiber technology and an excitation system from rare-earth permanent magnets. In the wall of the glass case, holes are evenly distributed around the circumference, in which the control winding is laid. The motor excitation system is a two-pole permanent magnet located on the rotating rotor shaft. The purpose of the research is to determine the effect of changing the design of the excitation system by changing the location of the magnetic poles. The research uses software that simulates the electromagnetic field using the finite element method. In the course of research, it was found that a decrease in the body of a permanent magnet leads to a decrease in the electromagnetic moment, which is not compensated by a decrease in edge effects at the boundary of the poles of the magnet. However, an increase in the value of the maximum magnetic induction in the air gap allows us to conclude that edge effects at the pole boundary have a significant effect on reducing the energy characteristics of superminiature micromachines. Thus, the solutions proposed in this paper are not sufficient to increase the efficiency of the engine, but the data obtained indicate the need to reduce the edge effects of permanent magnets.

Microengineered Materials with Self‐Healing Features for Soft Robotics

Recent advancements in soft robotics have led to the development of compliant robots that can exhibit complex motions driven by living cells, chemical reactions, or electronics. Further innovations are, however, needed to create the next generation of soft robots that can carry out advanced functions and exhibit complex locomotion. Material designs that incorporate “smart” functional properties can contribute to the development of robotic systems with in‐built mechanical responsiveness and functions. Herein, a simple material design that integrates stimuli‐responsive self‐healing and microarchitectural features to control locomotion of soft robots is reported. By employing these material designs along with hyperelastic soft actuators to control propellant dispersion and direction, a circuitry of pneumatic and microfluidic logic is created within a dragonfly‐shaped body that enables the robot to undergo user‐ and environment‐controlled locomotion over water surface. In addition to steering the robot to skim, the material properties are also leveraged to detect water acidification, temperature changes, and hydrophobic impurities such as oil. The design, fabrication, and integration strategies demonstrated herein pave a way for developing futuristic multifunctional soft robots, biomedical devices, and environmental monitoring probe.

Advances in Forest Robotics: A State-of-the-Art Survey

The development of robotic systems to operate in forest environments is of great relevance for the public and private sectors. In this sense, this article reviews several scientific papers, research projects and commercial products related to robotic applications for environmental preservation, monitoring, wildfire firefighting, inventory operations, planting, pruning and harvesting. After conducting critical analysis, the main characteristics observed were: (a) the locomotion system is directly affected by the type of environmental monitoring to be performed; (b) different reasons for pruning result in different locomotion and cutting systems; (c) each type of forest, in each season and each type of soil can directly interfere with the navigation technique used; and (d) the integration of the concept of swarm of robots with robots of different types of locomotion systems (land, air or sea) can compensate for the time of executing tasks in unstructured environments. Two major areas are proposed for future research works: Internet of Things (IoT)-based smart forest and navigation systems. It is expected that, with the various characteristics exposed in this paper, the current robotic forest systems will be improved, so that forest exploitation becomes more efficient and sustainable.

Natural Walking With Musculoskeletal Models Using Deep Reinforcement Learning

Human gait optimality has been investigated recently, with the development of detailed musculoskeletal models, through trajectory optimization approaches or deep reinforcement learning (DRL). Trajectory optimization studies are limited by the trajectory length and can only generate open-loop solutions. While existing DRL solutions provide closed-loop control policies without trajectory length limit, they either do not evaluate the naturalness of the behaviour, or directly impose experimental tracking data. In this letter, a DRL-based approach is proposed with a nature-inspired curriculum learning (CL) scheme and a neuromechanically-inspired reward function. This approach generates close-to-natural human walking without the aid of experimental data. Our CL scheme is realized by an evolving reward function, targeting simpler behaviours such as standing and stepping first, then gradually refining the gait. The emerged gait from the closed-loop stochastic policy demonstrated a strong correlation with human gait kinematics, with Pearson correlations of 0.95 and 0.83 at the hip and knee, respectively, and higher gait symmetry than two other DRL-based control policies without CL. Our approach was also found to have efficient convergence to walking-capable policy. This approach can facilitate the development of assistive robotic systems by providing a “human” controller, and could enable decentralized adaptation between the agent and the assistive robotic devices.

Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications

The rising interest in soft robotics, combined to the increasing applications in the space industry, leads to the development of novel lightweight and deployable robotic systems, that could be easily contained in a relatively small package to be deployed when required. The main challenges for soft robotic systems are the low force exertion and the control complexity. In this manuscript, a soft manipulator concept, having inflatable links, is introduced to face these issues. A prototype of the inflatable link is manufactured and statically characterized using a pseudo-rigid body model on varying inflation pressure. Moreover, the full robot model and algorithms for the load and pose estimation are presented. Finally, a control strategy, using inverse kinematics and an elastostatic approach, is developed. Experimental results provide input data for the control algorithm, and its validity domain is discussed on the basis of a simulation model. This preliminary analysis puts the basis of future advancements in building the robot prototype and developing dynamic models and robust control.

An aerial robotic system for inventory of stockpile warehouses

AbstractThis article describes the development and evaluation of an aerial robotic system for smart inventory of stockpile warehouses. The system was developed to automatically measure piles of different bulk materials, such as phosphorus and potassium compounds, which are stored in bays of different sizes inside a warehouse. This warehouse configuration is very common among fertilizer and animal food industries in Brazil. While an inventory can be executed by a human technician, the insalubrious environment, the imprecision of the manual volume estimation, and the time spent by the technician to access the information motivate the automation of the process. The proposed system uses a multirotor electrical drone that navigates autonomously inside the warehouse while it acquires light detection and ranging (LIDAR) point cloud data. This data is used to build a three‐dimensional (3D) model of the environment, which is then processed to identify the stockpiles of material and calculate their volumes. Since the environment is GPS‐denied and its characteristics, including symmetry, illumination and texture, do not favor visual‐ or LIDAR‐based localization, a drone navigation strategy that relies on relative positioning with respect to simple structures of the warehouse was developed. This article also presents our approach for autonomous stockpile volume estimation, which was numerically evaluated both in simulation and with real data, yielding in accuracy and precision of about 98%. The results presented in the article show that the aerial system is able to substitute the previously adopted manual procedure, highly increasing its accuracy, repeatably and safety, and drastically reducing its time of execution and cost.

On the Impact of Gravity Compensation on Reinforcement Learning in Goal-Reaching Tasks for Robotic Manipulators

Advances in machine learning technologies in recent years have facilitated developments in autonomous robotic systems. Designing these autonomous systems typically requires manually specified models of the robotic system and world when using classical control-based strategies, or time consuming and computationally expensive data-driven training when using learning-based strategies. Combination of classical control and learning-based strategies may mitigate both requirements. However, the performance of the combined control system is not obvious given that there are two separate controllers. This paper focuses on one such combination, which uses gravity-compensation together with reinforcement learning (RL). We present a study of the effects of gravity compensation on the performance of two reinforcement learning algorithms when solving reaching tasks using a simulated seven-degree-of-freedom robotic arm. The results of our study demonstrate that gravity compensation coupled with RL can reduce the training required in reaching tasks involving elevated target locations, but not all target locations.

Design and voice‐based control of a nasal endoscopic surgical robot

In traditional nasal surgery, surgeons are prone to fatigue and jitter by holding the endoscope for a long-time. Some complex operations require assistant surgeon to assist with holding the endoscope. To address the above problems, the authors design a remote centre of motion based nasal robot, and propose a voice-based robot control method. First, through the operation space analysis of nasal surgery, the design scheme of the robot based on RCM mechanism is proposed. On this basis, the design parameters of the robot are analysed to complete the entire design of robot. Then, considering that the surgeon's hands are occupied by surgical instruments during complex surgical operations, a voice-based robot control method is proposed. This method obtains direction instructions from surgeons by analysing the movement of the endoscopic image. Afterward, a commercial speech recognition interface is used to realise the offline grammar controlwords lib compatible with both Chinese and English, and the overall strategy of robot control is proposed. Finally, an experimental platform for virtual robot control is established, and the voice-based robot control experiment is performed. The results show that the proposed voice-based control method is feasible, and it provides guidance for the subsequent development and control of the actual robot system.

Development of gantry radial arm robotic system for corrosion inspection on wall of reactor tank TRIGA

This paper focuses on the design upgrade of existing scanner of gantry radial arm robotic system for corrosion inspection on wall reactor of a TRIGA tank. The scanner design will be used for under water inspection environment verification. This inspection can be performed by using ultrasonic C-scan that will be designed in this project. This gantry radial arm system is a modular design that allows upgrading, easy dimensional matching and, specifications related to kinematic and technique. The gantry radial arm system is expected to be user-friendly. SolidWorks software was used to design the prototype of the gantry radial arm. Most of the components are made from Aluminium which is much sturdy and low cost. Meanwhile for the rod holder system, spring mechanism was used to prevent rod from bending when it moves down into the water. The test results show that the system designed is safe to be used, as it has been proved by simulation and calculation.

WITHDRAWN: Design and development of rescue robot in borewell environment

Abstract This work majorly focused on development of rescue robot systems for children falling into bore well environment. The countries like India there have been several accidents and dead are happening particularly children below 3 years due to bore well environment which is left uncovered. The lifesaving team even army members spend hours to days but 70% of the attempts are unsuccessful to save these little kids because of complicated process. Our robot system mainly involves following process; Approaching the children, Supervision the children, Taking children out. And also our work focused on Safe Handling of the children. Therefore our work deals with concepts of rescue robot systems in bore well environment with semi-automated mechanisms.

Robotic system design and development for automated dismantling of PCB waste

PurposeThe mass electronics sector is one of the most critical sources of waste, in terms of volume and content with dangerous effects on the environment. The purpose of this study is to provide an automated and accurate dismantling system that can improve the outcome of recycling.Design/methodology/approachFollowing a short introduction, the paper details the implementation layout and highlights the advantages of using a custom architecture for the automated dismantling of printed circuit board waste.FindingsCurrently, the amount of electronic waste is impressive while manual dismantling is a very common and non-efficient approach. Designing an automatic procedure that can be replicated, is one of the tasks for efficient electronic waste recovery. This paper proposes an automated dismantling system for the advanced recovery of particular waste materials from computer and telecommunications equipment. The automated dismantling architecture is built using a robotic system, a custom device and an eye-to-hand configuration for a stereo vision system.Originality/valueThe proposed approach is innovative because of its custom device design. The custom device is built using a programmable screwdriver combined with an innovative rotary dismantling tool. The dismantling torque can be tuned empirically.

Design and development of a servo-controlled target-oriented robotic micro-dose spraying system in precision weed control

The broadcast spraying method using excessive amounts of pesticides is generally preferred for weed control in agriculture. In this study, a mobile robot was developed and tested on artificial weed targets for a micro-dose spraying system to reduce amount of liquid sprayed for weed control. A prototype mobile robot consisting of a robotic platform, machine vision and steerable spraying unit was constructed and controlled by using LabVIEW software and tested to evaluate the applicability of the spraying system. The greenness method and segmentation algorithm were used to extract artificial weeds from the background. The artificial weed samples were treated according to their coordinates by using a servo-based micro-dose spraying needle nozzle. The experiments were carried out at speeds of 0.42, 0.54, 0.66, 0.78 and 0.90 km h-1 to evaluate the performance of the spraying system under laboratory conditions. The tracking and targeting performances of the mobile spraying system were observed visually. Consumption, deposition and coverage rate experiments were carried out by using graduated cups, filter papers and water-sensitive papers to evaluate the spraying efficiency of the system under 200 kPa of spraying pressure. The results showed that the targeted micro-dose spraying method saved approximately 95% of the application volume compared with the broadcast spraying method. Higher spraying efficiency was determined at the middle locations rather than at the edge locations according to the amount of deposition and coverage rate results. The servo-controlled target-oriented weed control system that was developed was tested experimentally and found to be very efficient.

Development of an Integrated IoT-Based Greenhouse Control Three-Device Robotic System

The control of large greenhouse installations, especially those with hydroponics crops, is based on the analysis and use of data recorded by many sensors. At the same time, the size of such installations does not allow for their effective terrestrial surveillance, to detect problems promptly. In recent years, there has been an interest in the development of autonomous agbots equipped with agricultural sensors. Several ground-based AGV (automated guided vehicles) and UAV (unmanned aerial vehicles) systems have been developed for use in open-air plots. A key feature of all these innovative systems is spectroscopy, the development of which has been assisted by the surveillance capabilities and speed of modern-day UAVs (drones). However, there is a lag in the use of spectroscopy inside greenhouses since UAVs do not move freely indoors. In this paper, we propose as a solution a three-device (3DS) system.

On the viscoelastic dynamic beam modelling

There is a renewed interest in the development of underwater robotic fish due to the advancement in control systems. The cross section of real fish broadly consists of solid bone at the core followed by viscoelastic matrix (tissue mass). The precise material development for such systems is still a challenge, as the desired motion requires viscoelastic dynamic response with probably varying axial stiffness. It is thus important to develop physically accurate material model of fish that can be employed in fluid–structure interaction codes. The primary objective of the present work is to develop viscoelastic dynamic material model, where the viscous response is obtained by Kelvin–Voigt model. The detailed formulation of the model is presented followed by its numerical implementation by finite element (space discretization) and Newmark (time discretization) methods. The correctness of the developed MATLAB code is successfully demonstrated by solving several problems having known analytical solutions. The entire code is made available in the supplementary material file, which can be readily deployed in the design and development of robotic fish systems.

Development and Validation of a Novel Calibration Methodology and Control Approach for Robot-Aided Transcranial Magnetic Stimulation (TMS).

This article presents the development and validation of a new robotic system for Transcranial Magnetic Stimulation (TMS), characterized by a new control approach, and an ad-hoc calibration methodology, specifically devised for the TMS application. The robotic TMS platform is composed of a 7 dof manipulator, controlled by an impedance control, and a camera-based neuronavigation system. The proposed calibration method was optimized on the workspace useful for the specific TMS application (spherical shell around the subject's head), and tested on three different hand-eye and robot-world calibration algorithms. The platform functionality was tested on six healthy subjects during a real TMS procedure, over the left primary motor cortex. employing our method significantly decreases ( ) the calibration error by 34% for the position and 19% for the orientation. The robotic TMS platform achieved greater orientation accuracy than the expert operators, significantly reducing orientation errors by 46% ( ). No significant differences were found in the position errors and in the amplitude of the motor evoked potentials (MEPs) between the robot-aided TMS and the expert operators. The proposed calibration represents a valid method to significantly reduce the calibration errors in robot-aided TMS applications. Results showed the efficacy of the proposed platform (including the control algorithm) in administering a real TMS procedure, achieving better coil positioning than expert operators, and similar results in terms of MEPs. This article spotlights how to improve the performance of a robotic TMS platform, providing a reproducible and low-cost alternative to the few devices commercially available.

Development of an autonomous robotic system for beard shaving assistance of disabled people based on an adaptive force tracking impedance control

Recently, people with upper arm disabilities due to neurological disorders, stroke or old age are receiving robotic assistance to perform several activities such as shaving, eating, brushing and drinking. Although the full potential of robotic assistance lies in the use of fully autonomous robotic systems, these systems are limited in design due to the complexities and the associated risks. Hence, rather than the shared controlled or active robotic systems used for such tasks around the head, an adaptive compliance control scheme-based autonomous robotic system for beard shaving assistance is proposed. The system includes an autonomous online face detection and tracking as well as selected geometrical features-based beard region estimation using the Kinect RGB-D camera. Online trajectory planning for achieving the shaving task is enabled; with the capability of online re-planning trajectories in case of unintended head pose movement and occlusion. Based on the dynamics of the UR-10 6-DOF manipulator using ADAMS and MATLAB, an adaptive force tracking impedance controller whose parameters are tuned using Genetic Algorithm (GA) with force/torque constraints is developed. This controller can regulate the contact force under head pose changing and varying shaving region stiffness by adjusting the target stiffness of the controller. Simulation results demonstrate the system capability to achieve beard shaving autonomously with varying environmental parameters that can be extended for achieving other tasks around the head such as feeding, drinking and brushing.

Development and piloting of a perturbation stationary bicycle robotic system that provides unexpected lateral perturbations during bicycling (the PerStBiRo system)

BackgroundBalance control, and specifically balance reactive responses that contribute to maintaining balance when balance is lost unexpectedly, is impaired in older people. This leads to an increased fall risk and injurious falls. Improving balance reactive responses is one of the goals in fall-prevention training programs. Perturbation training during standing or treadmill walking that specifically challenges the balance reactive responses has shown very promising results; however, only older people who are able to perform treadmill walking can participate in these training regimes. Thus, we aimed to develop, build, and pilot a mechatronic Perturbation Stationary Bicycle Robotic system (i.e., PerStBiRo) that can challenge balance while sitting on a stationary bicycle, with the aim of improving balance proactive and reactive control.MethodsThis paper describes the development, and building of the PerStBiRo using stationary bicycles. In addition, we conducted a pilot randomized control trial (RCT) with 13 older people who were allocated to PerStBiRo training (N = 7) versus a control group, riding stationary bicycles (N = 6). The Postural Sway Test, Berg Balance Test (BBS), and 6-min Walk Test were measured before and after 3 months i.e., 20 training sessions.ResultsThe PerStBiRo System provides programmed controlled unannounced lateral balance perturbations during stationary bicycling. Its software is able to identify a trainee’s proactive and reactive balance responses using the Microsoft Kinect™ system. After a perturbation, when identifying a trainee’s trunk and arm reactive balance response, the software controls the motor of the PerStBiRo system to stop the perturbation. The pilot RCT shows that, older people who participated in the PerStBiRo training significantly improved the BBS (54 to 56, p = 0.026) and Postural Sway velocity (20.3 m/s to 18.3 m/s, p = 0.018), while control group subject did not (51.0 vs. 50.5, p = 0.581 and 15 m/s vs. 13.8 m/s, p = 0.893, respectively), 6MWT tended to improve in both groups.ConclusionsOur participants were able to perform correct balance proactive and reactive responses, indicating that older people are able to learn balance trunk and arm reactive responses during stationary bicycling. The pilot study shows that these improvements in balance proactive and reactive responses are generalized to performance-based measures of balance (BBS and Postural Sway measures).