Terms Of Positioning Accuracy Research Articles

Fault-tolerant controller design based on adaptive backstepping for tower cranes with actuator faults

Due to the widespread application and significant investment required for a single crane, there is an increased emphasis on crane safety and service life. Fault-tolerant control as an effective solution to unexpected faults has been widely studied recently. However, most fault-tolerant control methods require redundant actuators or a complex design process, which is unsuitable for the tower crane. Following these problems, a fault-tolerant controller based on an adaptive backstepping technique is proposed. Firstly, the system states are reconstructed and written as a cascade system. Secondly, a fixed-time convergence optimized backstepping controller is proposed to achieve smooth control of the tower crane without generating sudden or abrupt values. Then, an adaptive approach has been proposed to update fault parameters for the crane system in case of a sudden fault occurrence. Finally, after conducting comparison tests, it has been determined that the proposed controller not only performs exceptionally well in terms of position accuracy and swing elimination, but also maintains a satisfactory control performance when faced with sudden faults.

On the Evaluation of Diverse Vision Systems towards Detecting Human Pose in Collaborative Robot Applications.

Tracking human operators working in the vicinity of collaborative robots can improve the design of safety architecture, ergonomics, and the execution of assembly tasks in a human-robot collaboration scenario. Three commercial spatial computation kits were used along with their Software Development Kits that provide various real-time functionalities to track human poses. The paper explored the possibility of combining the capabilities of different hardware systems and software frameworks that may lead to better performance and accuracy in detecting the human pose in collaborative robotic applications. This study assessed their performance in two different human poses at six depth levels, comparing the raw data and noise-reducing filtered data. In addition, a laser measurement device was employed as a ground truth indicator, together with the average Root Mean Square Error as an error metric. The obtained results were analysed and compared in terms of positional accuracy and repeatability, indicating the dependence of the sensors' performance on the tracking distance. A Kalman-based filter was applied to fuse the human skeleton data and then to reconstruct the operator's poses considering their performance in different distance zones. The results indicated that at a distance less than 3 m, Microsoft Azure Kinect demonstrated better tracking performance, followed by Intel RealSense D455 and Stereolabs ZED2, while at ranges higher than 3 m, ZED2 had superior tracking performance.

Message Passing-Based 9-D Cooperative Localization and Navigation With Embedded Particle Flow

Cooperative localization (CL) is an important technology for innovative services such as location-aware communication networks, modern convenience, and public safety. We consider wireless networks with mobile agents that aim to localize themselves by performing pairwise measurements amongst agents and exchanging their location information. Belief propagation (BP) is a state-of-the-art Bayesian method for CL. In CL, particle-based implementations of BP often are employed that can cope with non-linear measurement models and state dynamics. However, particle-based BP algorithms are known to suffer from particle degeneracy in large and dense networks of mobile agents with high-dimensional states. This paper derives the messages of BP for CL by means of particle flow, leading to the development of a distributed particle-based message-passing algorithm which avoids particle degeneracy. Our combined particle flow-based BP approach allows the calculation of highly accurate proposal distributions for agent states with a minimal number of particles. It outperforms conventional particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we compare the proposed method to a centralized particle flow-based implementation, known as the exact Daum-Huang filter, and to sigma point BP in terms of position accuracy, runtime, and memory requirement versus the network size. We further contrast all methods to the theoretical performance limit provided by the posterior Cramér-Rao lower bound (PCRLB). Based on three different scenarios, we demonstrate the superiority of the proposed method.

A Novel Inertial-Aided Visible Light Positioning System Using Modulated LEDs and Unmodulated Lights as Landmarks

Indoor localization with high accuracy and efficiency has attracted much attention. Due to visible light communication (VLC), the LED lights in buildings, once modulated, hold great potential to be ubiquitous indoor localization infrastructure. However, this entails retrofitting the lighting system and is hence costly in wide adoption. To alleviate this problem, we propose to exploit modulated LEDs and existing unmodulated lights as landmarks. On this basis, we present a novel inertial-aided visible light positioning (VLP) system for lightweight indoor localization on resource-constrained platforms, such as service robots and mobile devices. With blob detection, tracking, and VLC decoding on rolling-shutter camera images, a visual front end extracts two types of blob features, i.e., mapped landmarks (MLs) and opportunistic features (OFs). These are tightly fused with inertial measurements in a stochastic cloning sliding-window extended Kalman filter (EKF) for localization. We evaluate the system by extensive experiments. The results show that it can provide lightweight, accurate, and robust global pose estimates in real time. Compared with our previous ML-only inertial-aided VLP solution, the proposed system has superior performance in terms of positional accuracy and robustness under challenging light configurations, such as sparse ML/OF distribution. Note to Practitioners—This article is motivated by the problem that many existing visible light positioning (VLP) systems require high-cost environmental modifications, i.e., replacing a large portion of original lights with modulated LEDs as beacons. To reduce costs in wide adoption, we seek to use fewer modulated LEDs if possible. Accordingly, we present a novel inertial-aided VLP system that uses both modulated LEDs and unmodulated lights as landmarks. Like in other VLP systems, the successfully decoded LEDs provide absolute pose measurements for global localization. Unmodulated lights and the LEDs with decoding failures provide relative motion constraints, allowing the reduction of pose drift during the outage of modulated LEDs. Due to the tightly coupled sensor fusion by filtering, the system can provide efficient and accurate localization when modulated LEDs are sparse. The system is lightweight to run on resource-constrained platforms. For practical deployment of our system at scale, creating LED maps accurately and efficiently remains a problem. It is desired to develop automated LED mapping solutions in future work.

A novel weighted auxiliary set matching pursuit method for glioma in Cerenkov luminescence tomography reconstruction.

Cerenkov luminescence tomography (CLT) is a promising three-dimensional imaging technology that has been actively investigated in preclinical studies. However, because of the ill-posedness in the inverse problem of CLT reconstruction, the reconstruction performance is still not satisfactory for broad biomedical applications. In this study, a novel weighted auxiliary set matching pursuit (WASMP) method was explored to enhance the accuracy of CLT reconstruction. The numerical simulations and in vivo imaging studies using tumor-bearing mice models were conducted to evaluate the performance of the WASMP method. The results of the above experiments proved that the WASMP method achieved superior reconstruction performance than other approaches in terms of positional accuracy and shape recovery. It further demonstrates that the atom selection strategy proposed in this study has a positive effect on improving the accuracy of atoms. The proposed WASMP improves the accuracy for CLT reconstruction for biomedical applications.

Comparative analysis of the performance of Multi-GNSS RTK: A case study in Turkey

The Real Time Kinematic (RTK) method is widely used in the land surveying. Whereas RTK method has the advantage of practical use, positioning accuracy depends mostly on the baseline length due to the atmospheric errors. In general, RTK measurements are made by using GPS and GLONASS satellite systems. For this reason, the positioning performance of the technique is adversely affected under restricted satellite geometry conditions such as urban canyons. At present, most receivers on the market have the ability to track signals of Galileo and BeiDou satellites. Therefore, in this study, the positioning performance of RTK with different satellite combinations (GPS-only, GPS+GLONASS, GPS+GLONASS+GALILEO+BeiDou) was examined with a comparative approach. A field test was carried out considering approximately 20, 40, 60, and 80 km length of baselines. Three different cut off elevation angles – namely, 10°, 20°, and 30° – were chosen for the field test. The results were investigated in terms of accuracy and precision. Also, the ground truth coordinates of the rovers were obtained by post-processing relative method using GAMIT/GLOBK software. The results showed that multi-GNSS combinations provided better repeatability at the 10° cut off angle option. The accuracy of GPS-only solutions varied between 0.63/2.17 cm and 2.40/4.94 cm for horizontal and vertical components, respectively. However, the multi-GNSS combinations did not have a remarkable superiority in terms of position accuracy even at high satellite cut off angle (30°) compared to the GPS-only RTK.

Design and Field Test of a Low-cost Device for Real-time Livestock Tracking Using GPS/LoRa Communication

Highlights Monitoring movement is crucial to improve livestock management. GPS livestock tracking method using LoRa with an effective medium access control mechanism. Adaptive data collection with near real-time monitoring. GPS data were collected for improving home-range management. Low implementation costs with increasing lifespan of the devices. Abstract. Extensive livestock grazing systems are in line with agroecological principles. However, these systems are particularly complex to manage. Developing accurate and reliable monitoring of livestock movements and activities is a promising tool to improve the global performances of these systems. Various livestock management systems have been developed over the past decades, culminating in sophisticated systems in intensive controlled environments that allow the real-time monitoring of movements, behaviors, health and production parameters for livestock individuals/herds (e.g. dairy cattle). However, extensive and semi-extensive systems for which the animals are ranging freely outdoors at least part of the time, are presenting specific challenges for combined tracking data acquisition and communication. In this article, we present a GPS livestock tracking method using LoRa communication with a new medium access control mechanism. The node lifespan and performance of the system were evaluated in real on-farm conditions in three different locations in Thailand and Vietnam. The results showed that our system performed adequately well in terms of position accuracy and timeliness. The accuracy of GPS data collected are compatible with their usage for improved rangeland management, monitoring livestock movements and impacts on the environment, and for back-tracking epidemiological movements and contacts of individuals/herds to control diseases outbreaks. The communication system via LoRa allowed to some extent real-time location of livestock herds by farmers due to instant position updates to the Internet, although the performance depended largely on the relief of the landscape surrounding the rangeland used. Finally, reducing costs and increasing the lifespan of the devices remain critical objectives to foster the adoption of such system by farmers and by certification groups to provide field-based evidence for the traceability and quality control of livestock and animal products raised under healthy and environmental-friendly productions systems. Our current design, if produced in a large quantity of at least 100 units, would cost approximately $65 USD per unit, with an average lifetime of 20 days. Keywords: Cattle, GPS, LoRa, Tracking system.

Galileo OSNMA Public Observation Phase: Signal Testing and Validation

The Public Observation (PO) test phase of the Galileo Open Service Navigation Message Authentication (OSNMA) signal is officially opened since November <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15^{\text {th}}~2021$ </tex-math></inline-formula> , by the European Union Agency for the Space Programme (EUSPA). During this phase, any interested users is allowed to access the Signal in Space (SIS) for testing purposes, while the crypto material needed to process the message and verify its authenticity is made available on the European GNSS Service Centre (GSC) web portal. This paper describes the processing of the Galileo E1-B navigation message, as observed during the first days of PO. The purpose is to analyze the crypto and authenticated data carried by the SIS and apply the OSNMA protocol to authenticate the navigation message. The SIS has been processed with the NGene real time software receiver which verifies the authenticity of the navigation data, before evaluating the position fix. The work is completed by an assessment of OSNMA-ready receiver performance, mainly in terms of position accuracy.

Exploring five indicators for the quality of OpenStreetMap road networks: a case study of Québec, Canada

OpenStreetMap (OSM) is one of the most well-known volunteered geographic information (VGI) projects that aims to produce a free-world map. However, there are serious concerns about its quality. Numerous studies have assessed the quality of OSM by comparing the OSM database with a reference database. Several researchers have proposed the use of quality indicators as variables that can describe OSM quality in regions where no reference data are available. A quality indicator is a variable that has a significant monotonic relationship with quality measures. In this study, a literature review was conducted to identify and define the main quality measures proposed for assessing the quality of linear features. Owing to limited access to current data, only three quality elements—completeness, positional accuracy, and attribute accuracy—were evaluated in this study. These quality measures were then used to assess the quality of the OSM roads in the province of Quebec. Finally, Spearman’s rank correlation coefficient test was applied to determine whether there was a significant correlation between the quality measures related to the three quality elements and the five potential quality indicators: population, average income, density of OSM roads, density of OSM buildings, and number of points of interest (POI). The main contribution of this study is testing the following hypothesis: “There is a significant correlation between the five mentioned variables and the measures related to the three quality elements”. Statistical analysis showed that in terms of completeness, the density of OSM roads and population were the best indicators; in terms of positional accuracy, population and income were the best indicators; and in terms of attribute accuracy, completeness was the best indicator. All five variables have significant correlations with the measures of the three elements of quality, except for the following two pairs (attribute accuracy, density of OSM roads) and (attribute accuracy, density of OSM buildings). This study proposes the density of OSM roads and number of POI as two new quality indicators that have not been found in the literature review.

Simulation and Characterization of Wind Impacts on sUAS Flight Performance for Crash Scene Reconstruction

Small unmanned aircraft systems (sUASs) have emerged as promising platforms for the purpose of crash scene reconstruction through structure-from-motion (SfM) photogrammetry. However, auto crashes tend to occur under adverse weather conditions that usually pose increased risks of sUAS operation in the sky. Wind is a typical environmental factor that can cause adverse weather, and sUAS responses to various wind conditions have been understudied in the past. To bridge this gap, commercial and open source sUAS flight simulation software is employed in this study to analyze the impacts of wind speed, direction, and turbulence on the ability of sUAS to track the pre-planned path and endurance of the flight mission. This simulation uses typical flight capabilities of quadcopter sUAS platforms that have been increasingly used for traffic incident management. Incremental increases in wind speed, direction, and turbulence are conducted. Average 3D error, standard deviation, battery use, and flight time are used as statistical metrics to characterize the wind impacts on flight stability and endurance. Both statistical and visual analytics are performed. Simulation results suggest operating the simulated quadcopter type when wind speed is less than 11 m/s under light to moderate turbulence levels for optimal flight performance in crash scene reconstruction missions, measured in terms of positional accuracy, required flight time, and battery use. Major lessons learned for real-world quadcopter sUAS flight design in windy conditions for crash scene mapping are also documented.

A Real-Time GNSS/PDR Navigation System for Mobile Devices

In this article, a smart pedestrian navigation system is developed to be implemented in a common smartphone. The main phases that characterize a pedestrian navigation system that is based on dead reckoning are introduced. A suitable Phase-Locked Loop is designed and the algorithm to estimate the direction of the user’s motion between one step and the next is developed. Finally, a suitable multi-rate Kalman filter (KF) is considered to merge the information from the pedestrian dead reckoning (PDR) navigation with the data provided by the global navigation satellite systems (GNSS). The proposed GNSS/PDR navigation system is implemented in Simulink as a finite-state machine and allows to define a trade-off between energy-saving and performance improvement in terms of position accuracy. The presented pedestrian navigation system is independent of the body-worn location of the smartphone and implements a compensation strategy of the systematic errors that are committed on the step-length estimation and the determination of the motion direction. Moreover, several tests are performed by walking in urban and suburban environments: the results show that a suitable trade-off between energy-saving and position accuracy can be reached by switching the GNSS receiver on and off.

CE-Net: A Coordinate Embedding Network for Mismatching Removal

Mismatching removal is at the core yet still a challenging problem in the photogrammetry and computer vision field. In this paper, we propose a coordinate embedding network (named CE-Net). We consider the mismatching problem as a graph node classification problem, and generate node descriptors by embedding point coordinates and aggregating geometric information from neighboring nodes based on self-attention and cross-attention mechanism. Finally, a binary classifier is used to separate node descriptors into two classes, namely matching inliers and outliers. Benefiting from the attention mechanism, firstly the node descriptors can get geometric information from “good neighbors” (i.e., matching inliers) and keep away from “bad neighbors” (i.e., matching outliers), improving the exactness of the descriptors; secondly the node descriptors can contain the information from both intra-graph and inter-graph, improving their distinctiveness. Experiments in testing datasets show that our proposed CE-Net achieves the state-of-the-art performance with a precision of 0.972, an outlier recall of 0.984, and an inlier recall of 0.963. Furthermore, CE-Net also outperforms the compared methods in real mismatching removal tasks in terms of positional accuracy, dispersion, and number of remaining point pairs, showing great potentials in practical applications. Our codes and data are available on https://github.com/csyhy1986 .

MODIS Sensor Capability to Burned Area Mapping-Assessment of Performance and Improvements Provided by the Latest Standard Products in Boreal Regions.

This paper presents an accuracy assessment of the main global scale Burned Area (BA) products, derived from daily images of the Moderate-Resolution Imaging Spectroradiometer (MODIS) Fire_CCI 5.1 and MCD64A1 C6, as well as the previous versions of both products (Fire_CCI 4.1 and MCD45A1 C5). The exercise was conducted on the boreal region of Alaska during the period 2000–2017. All the BA polygons registered by the Alaska Fire Service were used as reference data. Both new versions doubled the annual BA estimate compared to the previous versions (66% for Fire_CCI 5.1 versus 35% for v4.1, and 63% for MCD64A1 C6 versus 28% for C5), reducing the omission error (OE) by almost one half (39% versus 67% for Fire_CCI and 48% versus 74% for MCD) and slightly increasing the commission error (CE) (7.5% versus 7% for Fire_CCI and 18% versus 7% for MCD). The Fire_CCI 5.1 product (CE = 7.5%, OE = 39%) presented the best results in terms of positional accuracy with respect to MCD64A1 C6 (CE = 18%, OE = 48%). These results suggest that Fire_CCI 5.1 could be suitable for those users who employ BA standard products in geoinformatics analysis techniques for wildfire management, especially in Boreal regions. The Pareto boundary analysis, performed on an annual basis, showed that there is still a potential theoretical capacity to improve the MODIS sensor-based BA algorithms.

Positional Accuracy Assessment for Updating Authoritative Geospatial Datasets Based on Open Source Data and Remotely Sensed Images

OpenStreetMap (OSM) represents the most common example of online volunteered mapping applications. Most of these platforms are open source spatial data collected by non-experts volunteers using different data collection methods. OSM project aims to provide a free digital map for all the world. The heterogeneity in data collection methods made OSM project databases accuracy is unreliable and must be dealt with caution for any engineering application. This study aims to assess the horizontal positional accuracy of three spatial data sources are OSM road network database, high-resolution Satellite Image (SI), and high-resolution Aerial Photo (AP) of Baghdad city with respect to an analogue formal road network dataset obtained from the Mayoralty of Baghdad (MB). The methodology of, U.S. National Standard Spatial Data Accuracy (NSSDA) was applied to measure the degree of agreement between each data source and the formal dataset (MB) in terms of horizontal positional accuracy by computing RMSE and NSSDA values. The study concluded that each of the three data sources does not agree with the MB dataset in both study sites AL-Aadhamiyah and AL-Kadhumiyah in terms of positional accuracy.

Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

In this work, we propose and demonstrate a received-signal-strength (RSS) based visible-light-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions.

Modular robotic platform for autonomous machining

Product miniaturisation is one of the key aspects of modern manufacturing technology. One of the ways to fabricate miniaturised product is micromachining using sophisticated computer numerically controlled (CNC) machine tools. However, conventional CNC machines are bulky, stationary, and unable to carry out parallel operations. This research aims to develop a modular robotic platform which would be able to carry out machining operation in mesoscale. Hexapod robots are legged mobile robots which are used for verities of applications. Here, we have implemented a hexapod robotic platform to support and move the cutting tool (in this case, a drilling tool). The robot was controlled from the host computer through serial communication. A graphical user interface (GUI) was designed and implemented to operate the robot and the drilling spindle. Several machining operations were carried out with the system to assess its performance. An innovative compensation algorithm has been proposed to improve the positional accuracy of the robot movement. The proposed algorithm takes into account spindle speed and linear velocity to mitigate the positional error. The positional accuracy was improved by more than 60% after implementing the error compensation scheme. In this research we managed to achieve sub-10 μm repeatability (≤ 10 μm) at the lowest spindle and point to point linear speed of 2500 RPM and 200 mm/min, respectively. The performance (in terms of positional accuracy) of the robot was also compared with that of an existing commercial micromachining system where the robot was found to be almost ~ 2× time poorer to that of the commercial machine. Finally, the machined holes’ quality was measured in terms of circularity and taperness. It was observed that at the best machining parameters circularity deviation was as low as 29.4 μm while taperness was 0.54 degree.

Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain

Abstract: While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.

An Assessment of the Reliability of the NIGNET Data

The Nigerian Geodetic Reference Frame is defined by a number of Continuously Operating Reference Stations (CORS) that constitute the Nigerian GNSS Network (NIGNET). NIGNET is essential for planning and national development with the main goal of ensuring consistency in the geodetic framework both nationally and internationally. Currently, the strength of the network in terms of data reliability has not been adequately studied due to the fact that research into CORS in Nigeria is just evolving, which constitutes a limitation in its applications. Therefore, the aim of this research is to explore the reliability of the 3-dimensional coordinates of NIGNET to inform usability and adequacy for both scientific and practical applications. In particular, this study examines if the 3-dimensional coordinates of NIGNET are equally reliable in terms of positional accuracy. Accordingly, this study utilised GNSS data collected over a period of six years (2011 – 2016) from the network to compute the daily geocentric coordinates of the stations. Exploratory and statistical data analysis techniques were used to understand the magnitude of the errors and the accuracy level in the 3-dimensional coordinates. For this purpose, accuracy metrics such as standard deviation (𝜎), standard error (𝑆𝐸) and root mean square error (RMSE) were computed. While One-way ANOVA was conducted to explore the coordinate differences. The results obtained showed that SE and RMSE ranged from 13.00 − 56.50𝑚𝑚 and 14.38 − 73.16𝑚𝑚 respectively, which signifies high accuracy. Overall, while 88% of the network showed a high level of positional accuracy, the reliability has been compromised due to excessive gaps in the data archiving. Therefore, due attention must be given to NIGNET to achieve its purpose in the provision of accurate information for various geospatial applications. Also, any efforts directed at understanding the practical implications of NIGNET must be well-embraced for the realization of its set objectives.

Beyond Performance/Cost Tradeoffs in Motion Control: A Multirate Feedforward Design With Application to a Dual-Stage Wafer System

Motion systems with multiple control loops often run at a single sampling rate for simplicity of implementation and controller design. The achievable performance in terms of position accuracy is determined by the data acquisition hardware, such as sensors, actuators, and analog-to-digital/digital-to-analog converters, which is typically limited due to economic cost considerations. The aim of this paper is to develop a multirate approach to go beyond this traditional performance/cost tradeoff, i.e., to use different sampling rates in different control loops to optimally use hardware resources. The approach appropriately deals with the inherent time-varying behavior that is introduced by multirate sampling. A multirate feedforward control design framework is presented to optimize the tracking of a dual-stage multirate system. The application of the proposed approach to an industrial dual-stage wafer system demonstrates the advantages of multirate control, both in simulations and experiments.

Low-cost variable stiffness joint design using translational variable radius pulleys

Robot joints are expected to be safe, compliant, compact, simple and low-cost. Gravity compensation, zero backlash, energy efficiency and stiffness adjustability are some desired features in the robotic joints. The variable radius pulleys (VRPs) provide a simple, compact and low-cost solution to the stiffness adjustment problem. VRP mechanisms maintain a preconfigured nonlinear force-elongation curve utilizing off-the-shelf torsional spring and pulley profile. In this paper, three synthesis algorithms are presented for VRP mechanisms to obtain desired force-elongation curve. In addition, a feasibility condition is proposed to determine the torsional spring coefficient. Using the synthesis methods and the feasibility condition, a variable stiffness mechanism is designed and manufactured which uses two VRPs in an antagonistic cable driven structure. Afterwards, the outputs of three synthesis methods are compared to force-elongation characteristics in the tensile testing experiment. A custom testbed is manufactured to measure the pulley rotation, cable elongation and tensile force at the same time. Using the experiment as the baseline, the best algorithm achieved to reproduce the desired curve with a root-mean-square (RMS) error of 13.3%. Furthermore, VRP-VSJ is implemented with a linear controller to reveal the performance of the mechanism in terms of position accuracy and stiffness adjustability.