Robotic Sensor Research Articles

A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care

Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.

Whisker-inspired and self-powered triboelectric sensor for underwater obstacle detection and collision avoidance

As key devices for underwater robot to perceive underwater environments, tactile sensors play an important role in seabed exploration. Inspired by the structure of marine mammal whiskers, we designed a bio-inspired whisker sensor (BWS) based on triboelectric nanogenerators to assist underwater robots in sensing underwater environments. The proposed device generated electrical signals through triboelectric and electrostatic induction between a fluorinated ethylene propylene film and ink. With the assistance of a structural design inspired by biology, the artificial BWS could estimate the external stimulation area. We demonstrated specific functions of the sensor, such as LED lights control and real-time external load monitoring to demonstrate its utility. Moreover, the BWS installed in underwater robots identify the direction, position, and frequency of external loads by extracting the characteristics of electrical signals, particularly in cases when optical and acoustic devices cannot be used, such as in turbid water. The results showed that the BWS can serve as an underwater whisker sensor for underwater robots for perceiving underwater environment and avoiding reactive obstacles.

Towards Plug'n Play Task-Level Autonomy for Robotics Using POMDPs and Generative Models

To enable robots to achieve high level objectives, engineers typically write scripts that apply existing specialized skills, such as navigation, object detection and manipulation to achieve these goals. Writing good scripts is challenging since they must intelligently balance the inherent stochasticity of a physical robot's actions and sensors, and the limited information it has. In principle, AI planning can be used to address this challenge and generate good behavior policies automatically. But this requires passing three hurdles. First, the AI must understand each skill's impact on the world. Second, we must bridge the gap between the more abstract level at which we understand what a skill does and the low-level state variables used within its code. Third, much integration effort is required to tie together all components. We describe an approach for integrating robot skills into a working autonomous robot controller that schedules its skills to achieve a specified task and carries four key advantages. 1) Our Generative Skill Documentation Language (GSDL) makes code documentation simpler, compact, and more expressive using ideas from probabilistic programming languages. 2) An expressive abstraction mapping (AM) bridges the gap between low-level robot code and the abstract AI planning model. 3) Any properly documented skill can be used by the controller without any additional programming effort, providing a Plug'n Play experience. 4) A POMDP solver schedules skill execution while properly balancing partial observability, stochastic behavior, and noisy sensing.

Conformable and Compact Multiaxis Tactile Sensor for Human and Robotic Grasping via Anisotropic Waveguides

AbstractRich and accurate tactile perceptive capability is important for both humans and robots. Soft materials exhibit unique characteristics to construct high‐performance tactile sensors such as high sensitivity and high resistance to overload. In this work, a multiaxis tactile sensor based on a soft anisotropic waveguide that can distinguish normal force and shear force, which can greatly expand its potential uses in practice, is reported. First, the anisotropy of the waveguide sensor's response to vector forces is validated numerically and experimentally, and then two of those anisotropic units are embedded into one cladding with a crossed‐over layout, to form a multiaxis sensor. Then, a calibration algorithm is implemented on this sensor and the reconstruction of vector forces is achieved at an average accuracy of 28.0 mN for normal forces and 81.1 mN for shear forces, both with the sensing range of 1 N. Using this device, three demonstrations are shown to give outlooks of its potential application in human and robotic grasping tasks: a wearable tactile sensor for collecting human's haptic data during operation, a uniaxial gyroscope, and a robotic gripper's tactile sensor for assisting an unlocking task with a key. This work is a step toward more functional soft waveguide‐based force sensors.

A Triboelectric Joint Sensor Imitating Soft Robot for Human Joint Rehabilitation Monitoring

Human bone joint monitoring is essential for medical treatment, especially in the recovery phase of injury. It is worth noting that bending sensors based on soft robots have developed rapidly in recent years. Due to their flexibility, soft robots have a limitless number of degrees of freedom, but they also confront severe sensing and control issues. Meanwhile, triboelectric nanogenerators (TENGs) are projected to be self-powered sensors for soft robotics owing to their great sensitivity, versatility and simplicity. In this work, we first propose a triboelectric joint sensor (TJS) based on the TENG device. The TJS can provide the response on the dynamic, and the TJS’s output voltage is proportional to the local bending angle. According to the results, the voltage of TJS device rises linearly from 3.6[Formula: see text]V to 45[Formula: see text]V as the bending angle increases from 0∘ to 60∘. Furthermore, the TJS device can be used to monitor the postoperative rehabilitation of hand joints through the voltage signal of TJS device installed on the finger. This study will promote the application of triboelectric sensor simulating soft robot device in the field of medical monitoring.

Make it Dense: Self-Supervised Geometric Scan Completion of Sparse 3D LiDAR Scans in Large Outdoor Environments

Mapping systems that turn sensor data into a model of the environment are standard components in mobile robotics. Outdoor robots are often equipped with 3D LiDAR sensors to obtain accurate range measurements at a high frame rate. The price for a robotic LiDAR sensor scales roughly linearly with the number of beams and thus the vertical resolution of the scanner. In general, the cheaper the sensors, the sparser the point cloud. In this letter, we address the problem of building dense models from sparse range data. Instead of requiring the vehicle to move slowly through the environment or to traverse the scene multiple times to cover the space densely, we investigate geometric scan completion through a learning-based approach. We revisit the traditional volumetric fusion pipeline based on truncated signed distance fields (TSDF) and propose a neural network to aid the 3D reconstruction on a frame-to-frame basis by completing each scan towards a dense TSDF volume. We propose a geometric scan completion network that is trained in a self-supervised fashion without labels. Our experiments illustrate that such frame-wise completion leads to maps that are on-par or even better compared to maps generated using a higher resolution LiDAR sensor. We additionally show that our system can be used to improve the performance of SLAM systems.

Optimal Placement of Vibration Sensors for Industrial Robots Based on Bayesian Theory

This paper presents an optimal sensor placement method for vibration signal acquisition in the field of industrial robot health monitoring and fault diagnosis. Based on the general formula of Bayes and relative entropy, the evaluation function of sensor placement is deduced, and the modal confidence matrix is used to express the redundancy of sensor placement. The optimal placement of vibration sensors is described as a discrete variable optimization problem, which is defined as whether the existing sensor layout can obtain joint state information efficiently. The initial layout of the sensor was obtained from the structural simulation results of the industrial robots, and the initial layout was optimized by the derived objective function. The efficiency of the optimized layout in capturing joint state information is proven by the validation experiment with a simulation model. The problem of popularizing the optimization method in engineering is solved by a verification experiment without a simulation model. The optimal sensor placement method provides a theoretical basis for industrial robots to acquire vibration data effectively.

Ultra-Thin Graphene Foam Based Flexible Piezoresistive Pressure Sensors for Robotics

– Over recent year, robotics has made a drastic impact in a variety of different markets. Although having many advantages from, safer workspace to speed and efficiency there are several drawbacks all ranging from their lack of ability to execute functions and tasks easily performed by humans. This is mainly due to their lack of ability to implement touch and haptic feedback. In this work, we show the use and applicability of ultra-thin graphene foam (GRF), with polydimethylsiloxane (PDMS) embedded into and over the structure, as an active layer in piezoresistive based pressure sensors for use in robotic touch sensing applications. It has been demonstrated in this work that thin GRF/PDMS-GRF consisting of a few layers of graphene is able to present sensitivity to pressures within the range of 0 to >100kPa. Although pressure sensitivities are not yet comparable to those of current work, it must be noted that the GRF used in this work is much thinner in comparison, consisting of only several layers of graphene.

Design of Pipe-inspection Robot Based on YOLOv3

In this paper, a pipe-inspection robot system which uses STM32F103 as control core is designed. The robot system adopts YOLOv3 network model to detect and locate defects of sewage pipeline. The robot uses closed-circuit television system for detection, which is composed of hardware and defect detection. The hardware is mainly composed of robot body, winding coil and upper computer. The motor drive module, body attitude sensor of robot and camera are placed in the pipeline robot. The router, lithium battery and length measurer constitute the winding coil, which is connected with the robot through cable for data transmission and power supply. the upper computer communicates with the cable coil through WIFI, and the staff can send various instructions through the upper computer to control robot operation, and view the status of the pipeline through video. The defect detection of pipeline is mainly realized by YOLOv3 network model, which is used to automatically detect the types of defects in sewage pipeline and mark the location of defects. Finally, the feasibility and effectiveness of defect detection based on YOLOv3 network model are verified by simulation experiments.

МАГНИТОМЕТРИЧЕСКИЕ СЕНСОРНЫЕ СИСТЕМЫ ДЛЯ МОРСКИХ МОБИЛЬНЫХ РОБОТОВ

The paper proposes the use of marine mobile robots to counter mining, detection, classificationand localization of mines. In accordance with the purpose, search and reconnaissance deviceshave been identified for the survey of water areas that are capable of operating autonomously orin remote control mode with decision-making support. The analysis of existing types of sensorsystems for the survey of water areas is carried out. The main results of theoretical and experimentalstudies of ways to improve magnetometric sensor systems of marine robots are presented.It is proposed, based on the criterion of ensuring the greatest capabilities of marine robots fordetecting and the pace of searching in offline mode with known weight and size limitations, theconstruction of a magnetometric sensor system with automated recognition of explosive objects.For the purposes of automated classification of search objects, it is proposed to take advantage ofneural networks, which, unlike traditional machine learning, provide the possibility of high-levelabstract expression of the semantics of internal connections between data by choosing architecturalsolutions. The structure of the neural network is obtained based on the linear classificationof explosive objects according to two parameters of the training sample. Based on a proven trainingsample and a classifying function by two parameters, for a multichannel magnetometric system,an implementation of the neural network structure has been developed that takes into account,in addition to the ferromagnetic mass and depth of occurrence, the parameters of the geometricshape of real explosive objects. The directions of improving and increasing the range offerrosonde sensors as the most suitable for the construction of magnetometric detection systemsfor marine mobile robots are determined. A method is proposed to increase the sensitivity offerrosonde magnetometric sensors of marine robots through the use of new magnetic materialsand circuit solutions. To create highly sensitive ferrosonde magnetometric sensors, the use ofcores made of amorphous cobalt-based alloys of the AMAG-170 type is proposed, providing apotential opportunity to increase the conversion coefficient (sensitivity) of the sensor system byincreasing the excitation frequency of the core of the ferrosonde. The functional diagram of thelayout of the developed magnetometric ferrosonde sensor system based on two rod cores made ofamorphous alloy AMAG–170 is presented.

INDOOR MAPPING: EXPERIENCES WITH LIDAR SLAM

Abstract. Indoor mapping is gaining more interest in both research as well as in emerging applications. Building information systems (BIM) and indoor navigation are probably the driving force behind this trend. For accurate mapping, the platform trajectory reconstruction, or in other words sensor orientation, is essential to reduce or even eliminate for extensive ground control. Simultaneous localization and mapping (SLAM) is the computation problem of how to simultaneously estimate the platform/sensor trajectory while reconstructing the object space; usually, a real-time operation is assumed. Here we investigate the performance of two LiDAR SLAM tools based on using indoor data, acquired by a remotely controlled robot sensor platform. All comparisons were performed on similar datasets using appropriate metrics and encouraging results were obtained as a consequence of initial test studies yet further research is needed to analyse these tools and their accuracy comprehensively.

Wearable Power Assistant Robot Sensor Signal Prediction Algorithm and Controller Design.

The wearable power-assisted robot is a typical auxiliary rehabilitation robot. It is an exoskeleton power-assisted device that helps people to expand their lower limb movement capabilities. Its basic principle is to obtain the motion intention information of the human body through the perception system. Control the DC servo motor installed at the hip joint and the knee joint to drive the movement of the link, so as to achieve the purpose of providing assistance to the human body. In order to improve the dynamic response frequency of the wearable robotic perception system, a sensor signal based on time series analysis is proposed. The online prediction algorithm, which can perform single-step or multistep prediction under the premise of ensuring certain accuracy, can multiply the dynamic response frequency of the wearable-assisted robot sensing system to ensure the real-time performance of the whole system. In order to realize the sensor signal prediction algorithm, we design the corresponding software and hardware system to realize the prediction algorithm. The whole sensor signal prediction algorithm implementation system can be divided into two parts: lower computer and upper computer. The lower computer includes amplification circuit, signal conditioning circuit, and acquisition. The signal processing software part of the circuit and the corresponding MCU and the upper computer mainly include the data acquisition and prediction algorithm implementation, and the upper computer adopts the mixed programming technology of Vc++ and MATLAB to complete the software part of the upper computer. Aiming at the control part of the wearable robotic sensing system, the first generation of DC servo motor embedded motion controller is designed. The motion controller adopts the design concept of embedded motion controller, which has small size, is light weight, and has good expandability. And the motion controller can communicate and debug with the host computer through the serial port, which lays a foundation for the design of the entire embedded control system.

Functional Safety Analysis and Design of Sensors in Robot Joint Drive System

The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the functional safety issues of the robot sensors themselves. In view of this, based on IEC61508, a functional safety analysis and design method for sensors of RJDs is proposed in this paper. Firstly, the hazard analysis and risk assessment clarified the goals that the safety protection function of the RJD’s sensor should achieve. Then, by establishing the motor drive model and transmission model, a model-based sensor fault diagnosis and isolation strategy is proposed. Considering the fault-tolerant operation of system, a fail-operational hardware architecture of the safety-related system is designed. Markov analysis shows that the safety integrity level (SIL) of safety-related systems can reach SIL3. Finally, experiments are designed to validate the proposed fault diagnosis and fault tolerance strategy. The results show that the safety-related system can effectively locate sensor failures, realize fault-tolerant control when a single sensor fails and perform safe torque off (STO) protection when multiple sensors fail.

PROGRAM IMPLEMENTATION OF MOBILE PHYTOMONITORING WORK

Software for monitoring the technological parameters in an industrial greenhouse has been developed. The algorithm implemented by this program provides reading and storing information about plant conditions and the values of technological parameters of the environment in the greenhouse. The robot sensor system includes two subsystems of motion sensors and a technical vision system for determining and accumulating information about the coordinates of the robot. The software components that were used to create the interface of the robot control system and the principles of interaction of the robot with the existing control system of technological parameters in the industrial greenhouse are given.

Deep Learning-Assisted Smart Process Planning, Robotic Wireless Sensor Networks, and Geospatial Big Data Management Algorithms in the Internet of Manufacturing Things

The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.

A Robot–Sensor Network Security Architecture for Monitoring Applications

This paper presents SNSR (Sensor Network Security using Robots), a novel, open, and flexible architecture that improves security in static sensor networks by benefiting from robot-sensor network cooperation. In SNSR, the robot performs sensor node authentication and radio-based localization (enabling centralized topology computation and route establishment) and directly interacts with nodes to send them configurations or receive status and anomaly reports without intermediaries. SNSR operation is divided into stages set in a feedback iterative structure, which enables repeating the execution of stages to adapt to changes, respond to attacks, or detect and correct errors. By exploiting the robot capabilities, SNSR provides high security levels and adaptability without requiring complex mechanisms. This paper presents SNSR, analyzes its security against common attacks, and experimentally validates its performance.

Human-Exploratory-Procedure-Based Hybrid Measurement Fusion for Material Recognition

While various biomimetic robotic haptic sensors are often utilized to measure multifarious physical interactions to identify material properties under human exploratory procedures (EPs), traditional methods are unable to fuse well multimodal measurements under EPs. In order to solve this problem, an innovative hybrid joint group kernel sparse coding model for material recognition under EPs is proposed. First, a series of feature representations according to the characteristics of different measures are introduced. Second, we propose an innovative hybrid fusion framework based on statistical learning with kernel sparsity to effectively fuse the measurements from robotic EPs. Third, a matched optimization algorithm is constructed to deal with the innate optimization problem in the proposed framework. Finally, we develop an evaluation based on three different classification levels (coarse, medium, and fine) with the public dataset containing 184 material classes to verify the proposed method and compare with some other similar kernel methods. The evaluation results show that the accuracy of coarse, medium, and fine classification of the proposed hybrid fusion framework is 98.4%, 96.2%, and 98.4%, respectively. The framework can be extended easily to other fusion tasks with multiple measurements.

IMPACT OF ARTIFICIAL INTELLIGENCE (AI) ON EDUCATION: CHANGING PARADIGMS AND APPROACHES

Artificial Intelligence (AI) technology is to make human life easy and trouble-free and contribute to the advancement of human development. AI is a driving technological force of the twenty-first century and it has been a centre of discussion in technological innovations for its unlimited potential to alter the scenario of social interaction through resolving social challenges and virtually transform every industry. Education is the top priority of present society because it is a fundamental human right that builds peace and drives sustainable development across the world. The integration and application of AI in the classrooms will make teaching and learning effective by supporting teachers and learners in the process through the usage of robotic technology and sensors. AI-based technology facilitates inclusive and equitable quality education along with ensuring universal access to life-long learning for all across the world. The technology of AI has been advanced and sophisticated that can recognize the gesture of the students and understand their mood and ease during the lecture even it can read facial expressions and posture of the students to understand difficulties and problems they are facing in the lecture and recommends altering the lesson. AI technology-based assessment system can be used to assess students' knowledge, understanding, skills such as collaboration and persistence and characteristics such as confidence and motivation etc. AI technology has developed speech-to-text transcription, predictive text and facial recognition promising an inclusive future for all learners.

Underwater bionic whisker sensor based on triboelectric nanogenerator for passive vortex perception

Seals, as well as other terrestrial and marine animals, utilize their whiskers as key tactile sensors to locate and track underwater wakes. Herein, an underwater bionic whisker sensor (UBWS) based on triboelectric nanogenerator was designed for capturing complex stimuli produced by moving aquatic species and using the acquired signals to follow their hydrodynamic trails. The UBWS is inspired by the structural properties of seal whiskers and incorporates a free-standing triboelectric nanogenerator, which gives it ability to capture vortices passively. The design of the UBWS is fully flexible and self-powered, which eliminates the need for an external power source while retaining the capability of high sensitivity to stimuli. Moreover, the output signal’s interaction with the movement parameters was highly correlated, and this principle was utilized to determine the relative motion state of an underwater moving object. Experiments demonstrated that the developed UBWS was capable of perceiving the hydrodynamic trails created by moving objects underwater. These new insights into the biological foundation of tactile perception through whiskers provide new design criteria for developing efficient underwater robotic sensors.

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

In order to study the improved genetic algorithm for intelligent grinding trajectory of industrial robot, a trajectory planning method with optimal energy consumption is proposed. Firstly, the trajectory of the robot is regarded as a series of type value points in space, and each adjacent type value point is connected by a quintic B -spline curve to obtain the trajectory function of the robot. Then, the kinetic energy is taken as the target energy consumption function, and the kinematic and dynamic constraints of each joint are considered at the same time. Finally, the genetic algorithm is improved to optimize the objective energy consumption function. The improved genetic algorithm improves the operation efficiency, local search ability, and real-time performance of the algorithm. Front and rear times during handling were 15.034 s and 17.456 s for 2.422 s. The optimal secondary trajectory planning algorithm is called in the single workstation of the welding robot and the robot welding the bucket with spatial straight line and spatial curve, respectively. Through the time difference, the time used in the spatial linear welding is 5.462 s, and the spatial curve welding time is 12.981 s. The proposed algorithm can be applied to the production of various industrial sensor robots such as welding robot, cutting robot, and spraying robot and improves the working efficiency of robot operation. In addition, the genetic method of multichromosome structure also has a certain reference for solving the general GSTP problem.