Angle Estimation for Robotic Arms on Floating Base Using Low-Cost IMUS

GNSS (global navigation satellite system) compass is a low-cost, high-precision, and temporally stable north-finding technique. While the nonlinear baseline length constraint is widely known to be important in ambiguity resolution of GNSS compass, its direct effect on yaw angle estimation is theoretically analyzed in this work. Four different methods are considered with different ways in which the length constraint is made use of as follows: one without considering the constraints, one with simple scaling, one with indirect statistical scaling, and one with direct statistical scaling. It is found that simple scaling does not have any effect on yaw estimation; indirect and direct statistical scalings are equivalent to each other with both being able to increase the precision. The analysis and the conclusion developed in this work can go in parallel for the case of the tilt angle estimation.

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Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

10.20944/preprints201804.0255.v1 ◽

2018 ◽

Author(s):

Javier Garcia-Guzman ◽

Lisardo Prieto González ◽

Jonatan Pajares Redondo ◽

Mat Max Montalvo Martinez ◽

María Jesús López Boada

Keyword(s):

Neural Networks ◽

Real Time ◽

Low Cost ◽

Roll Angle ◽

Measurement Unit ◽

Raspberry Pi ◽

State Variables ◽

Angle Estimation ◽

Experimental Environment ◽

Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

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Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation

Sensors ◽

10.3390/s21196477 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6477

Author(s):

Túlio Fernandes de Almeida ◽

Edgard Morya ◽

Abner Cardoso Rodrigues ◽

André Felipe Oliveira de Azevedo Dantas

Keyword(s):

Open Source ◽

Measurement System ◽

Joint Angle ◽

Low Cost ◽

Ground Truth ◽

Percentage Error ◽

Angle Estimation ◽

Adult Individual ◽

Average Percentage ◽

Real Scenario

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.

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Low-Cost Prototype to Automate the 3D Digitization of Pieces: An Application Example and Comparison

Sensors ◽

10.3390/s21082580 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2580

Author(s):

Ramón González-Merino ◽

Elena Sánchez-López ◽

Pablo E. Romero ◽

Jesús Rodero ◽

Rafael E. Hidalgo-Fernández

Keyword(s):

Low Cost ◽

Cost Effective ◽

3D Models ◽

3D Scanner ◽

3D Design ◽

Robotic Arms ◽

Effective System ◽

3D Printers ◽

Preliminary Study ◽

3D Digitization

This work is aimed at describing the design of a mechanical and programmable 3D capturing system to be used by either 3D scanner or DSLR camera through photogrammetry. Both methods are widely used in diverse areas, from engineering, architecture or archaeology, up to the field of medicine; but they also entail certain disadvantages, such as the high costs of certain equipment, such as scanners with some precision, and the need to resort to specialized operatives, among others. The purpose of this design is to create a robust, precise and cost-effective system that improves the limitations of the present equipment on the market, such as robotic arms or rotary tables. For this reason, a preliminary study has been conducted to analyse the needs of improvement, later, we have focused on the 3D design and prototyping. For its construction, there have been used the FDM additive technology and structural components that are easy to find in the market. With regards to electronic components, basic electronics and Arduino-based 3D printers firmware have been selected. For system testing, the capture equipment consists of a Spider Artec 3D Scanner and a Nikon 5100 SLR Camera. Finally, 3D models have been developed by comparing the 3D meshes obtained by the two methods, obtaining satisfactory results.

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Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

BioMed Research International ◽

10.1155/2017/5708937 ◽

2017 ◽

Vol 2017 ◽

pp. 1-17 ◽

Cited By ~ 15

Author(s):

Alkinoos Athanasiou ◽

Ioannis Xygonakis ◽

Niki Pandria ◽

Panagiotis Kartsidis ◽

George Arfaras ◽

...

Keyword(s):

Brain Connectivity ◽

Low Cost ◽

Rehabilitation Robotics ◽

Assistive Technologies ◽

Human Robot Interaction ◽

Qualitative Assessment ◽

Special Importance ◽

Robotic Arms ◽

Pilot Investigation ◽

Injured Patients

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals’ BCI performance, and a pilot investigation on spinal cord injured patients’ BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

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Sparse Array Angle Estimation Using Reduced-Dimension ESPRIT-MUSIC in MIMO Radar

The Scientific World JOURNAL ◽

10.1155/2013/784267 ◽

2013 ◽

Vol 2013 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Chaozhu Zhang ◽

Yucai Pang

Keyword(s):

Low Cost ◽

Direction Cosine ◽

Mimo Radar ◽

Angle Estimation ◽

Linear Arrays ◽

Reduced Dimension ◽

Esprit Algorithm ◽

Receive Array ◽

True Direction ◽

High Computational Complexity

Sparse linear arrays provide better performance than the filled linear arrays in terms of angle estimation and resolution with reduced size and low cost. However, they are subject to manifold ambiguity. In this paper, both the transmit array and receive array are sparse linear arrays in the bistatic MIMO radar. Firstly, we present an ESPRIT-MUSIC method in which ESPRIT algorithm is used to obtain ambiguous angle estimates. The disambiguation algorithm uses MUSIC-based procedure to identify the true direction cosine estimate from a set of ambiguous candidate estimates. The paired transmit angle and receive angle can be estimated and the manifold ambiguity can be solved. However, the proposed algorithm has high computational complexity due to the requirement of two-dimension search. Further, the Reduced-Dimension ESPRIT-MUSIC (RD-ESPRIT-MUSIC) is proposed to reduce the complexity of the algorithm. And the RD-ESPRIT-MUSIC only demands one-dimension search. Simulation results demonstrate the effectiveness of the method.

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Design and Implementation of a Robotic Arm Assistant with Voice Interaction Using Machine Vision

Automation ◽

10.3390/automation2040015 ◽

2021 ◽

Vol 2 (4) ◽

pp. 238-251

Author(s):

George Nantzios ◽

Nikolaos Baras ◽

Minas Dasygenis

Keyword(s):

Quality Of Life ◽

Low Cost ◽

Kinematic Model ◽

High Accuracy ◽

Robotic Arm ◽

High Definition ◽

Robotic Arms ◽

Voice Interaction ◽

Robotic Assistant

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy.

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