Long-term Autonomous Operation Research Articles

Disinfection of Surfaces Contaminated with SARS-CoV-2 Coronavirus by UV Radiation of Low-Pressure Mercury-Vapour Lamp

The aim of the work was to determine the effective ultraviolet (UV) doses required for the disinfection of surfaces contaminated with the SARS-CoV-2 coronavirus using a low-pressure mercury lamp. Materials and methods. To carry out prompt disinfection of surfaces, a specially designed source of UV radiation with a power of 7.5 W at a wavelength of 254 nm in the form of a portable flashlight was employed, which has a high efficiency of UV radiation output and the possibility of long-term autonomous operation from a compact battery. In the studies, a suspension culture of the SARS-CoV-2 coronavirus with biological activity of 5.3∙106 PFU/ml was used. The objects of testing were plastic Petri dishes (disposable) and office paper (grade C, density 80 g/m2 ). Results and discussion. Doses of UV radiation that provide disinfection of surfaces contaminated with the COVID-19 pathogen with an efficiency of 99.0 % (paper) to 99.95 % (plastic) have been determined. The results obtained make it possible to recommend a portable UV irradiator for use in the practice of preventive measures to combat the spread of the disease caused by the SARS-CoV-2 coronavirus.

Challenges, limitations, and measurement strategies to ensure data quality in deep-sea sensors

In this paper we give an overview of factors and limitations impairing deep-sea sensor data, and we show how automatic tests can give sensors self-validation and self-diagnostic capabilities. This work is intended to lay a basis for sophisticated use of smart sensors in long-term autonomous operation in remote deep-sea locations. Deep-sea observation relies on data from sensors operating in remote, harsh environments which may affect sensor output if uncorrected. In addition to the environmental impact, sensors are subject to limitations regarding power, communication, and limitations on recalibration. To obtain long-term measurements of larger deep-sea areas, fixed platform sensors on the ocean floor may be deployed for several years. As for any observation systems, data collected by deep-sea observation equipment are of limited use if the quality or accuracy (closeness of agreement between the measurement and the true value) is not known. If data from a faulty sensor are used directly, this may result in an erroneous understanding of deep water conditions, or important changes or conditions may not be detected. Faulty sensor data may significantly weaken the overall quality of the combined data from several sensors or any derived model. This is particularly an issue for wireless sensor networks covering large areas, where the overall measurement performance of the network is highly dependent on the data quality from individual sensors. Existing quality control manuals and initiatives for best practice typically recommend a selection of (near) real-time automated checks. These are mostly limited to basic and straight forward verification of metadata and data format, and data value or transition checks against pre-defined thresholds. Delayed-mode inspection is often recommended before a final data quality stamp is assigned.

Анализ подходов к функционированию автономного необитаемого аппарата и надводного судна при проведении совместных миссий

Autonomous unmanned vehicles can successfully solve a variety of civil, scientific and military tasks. Robotic complexes of this class demonstrate their high efficiency when performing seismic surveys, hydrochemical monitoring of water areas and inspection of technical facilities of various purposes. Vehicles powered by a rechargeable battery, as a rule, have a very limited power reserve, and require periodic replenishment of the battery charge. The physical properties of the operating environment impose significant limitations on the communication methods used, which complicates the transfer of information between the operator and an autonomous unmanned submersible. Delivery of the vehicle to the mission site, as well as providing power to the vehicle and information exchange with the operator require some additional infrastructure. This paper focuses on finding and selecting solutions for transportation, power and communications support for autonomous underwater vehicles. The constructional solutions of underwater docks and general principles of building systems for ensuring the operation of this type of vehicles are considered, and a classification of approaches to the energy supply of underwater vehicles is made. Based on the analysis the most perspective solutions ensuring long-term autonomous operation were selected. Docking stations with the ability to lift and dive an underwater vehicle directly inside the station have a number of operational advantages. The use of contact methods for energy and information transfer requires high accuracy of underwater vehicle positioning and complication of its sensor system, as well as application of special methods for protection of contact pairs from environmental impacts. These disadvantages are deprived by wireless solutions, which are actively introduced at present.

Planetary boundary layer height retrieval from a diode-laser-based high spectral resolution lidar

The planetary boundary layer height (PBLH) is an essential parameter for weather forecasting and climate modeling. The primary methods for obtaining the PBLH include radiosonde measurements of atmospheric parameters and lidar measurements, which track aerosol layers in the lower atmosphere. Radiosondes provide the parameters to determine the PBLH but cannot monitor changes over a diurnal cycle. Lidar instruments can track the temporal variability of the PBLH and account for spatial variability when operated in a network configuration. The networkable micropulse DIAL (MPD) instruments for thermodynamic profiling are based on diode-laser technology that is eye-safe and cost-effective and has demonstrated long-term autonomous operation. We present a retrieval algorithm for determining the PBLH from the quantitative aerosol profiling capability of the high spectral resolution channel of the MPD. The PBLH is determined using a Haar wavelet transform (HWT) method that tracks aerosol layers in the lower atmosphere. The PBLH from the lidar is compared with the PBLH determined from potential temperature profiles from radiosondes. In many cases, good agreement among the PBLH retrievals was seen. However, the radiosonde retrieval often missed the lowest inversion layer when several layers were present, while the HWT could track the lowest layer.

Seawater Salinity Estimating Module Based on the Sound Velocity Measurements

Purpose. Reliability of knowledge about the ocean dynamics and climate variability is largely limited for lack of systematic in situ observations of the sea surface layer salinity, which is one of the basic hydrological parameters determining circulation and stratification of the water masses. The study is aimed at developing an autonomous device for long-term monitoring of salinity in the seawater upper layer. Methods and Results. One of the most effective tools for in situ observations of the ocean upper layer is the global network of surface drifting buoys – drifters. At present, the network consists of more than 1500 buoys, but only a few of them provide sea surface salinity observations within the framework of a limited number of pilot experiments. In the drifters, salinity is calculated by the traditional method using the results of the electrical conductivity and temperature measurements. There are a few problems related both to the principle of determining salinity by this method and to providing long-term stable running of conductivity sensors under the conditions of pollution and biological fouling. A drifter equipped with the module for the sound velocity and temperature measurements used for calculating salinity by an alternative method just aboard the drifter, was developed in Marine Hydrophysical Institute, Russian Academy of Sciences. The sound velocity and temperature module includes a specially designed time-of-flight sound velocity sensor with the fixed base and a quartz temperature sensor. In course of two years, numerous laboratory and in situ tests of several prototypes of the sound velocity and temperature module were performed. The laboratory tests showed that the repeatability limits for the results of the sound velocity measurements in the distilled water were 0.02 m/s. According to the data of the long-term in situ tests performed at intensive biological fouling, the error of salinity estimation resulted from of the sound velocity and temperature measurements were within 0.05 ‰. This result permits to expect that the sound velocity and temperature module parameters will remain stable in real conditions of long-term autonomous operation. Conclusions. The obtained results make it possible to recommend application of the drifters equipped with the modules for the sound velocity and temperature measurements as an effective tool for regular operational monitoring of the salinity field of the upper sea layer.

Modul' otsenivaniya solenosti morskoy vody na osnove izmereniy skorosti zvuka

Purpose. Reliability of knowledge about the ocean dynamics and climate variability is largely limited for lack of systematic in situ observations of the sea surface layer salinity, which is one of the basic hydrological parameters determining circulation and stratification of the water masses. The study is aimed at developing an autonomous device for long-term monitoring of salinity in the seawater upper layer. Methods and Results. One of the most effective tools for in situ observations of the ocean upper layer is the global network of surface drifting buoys – drifters. At present, the network consists of more than 1500 buoys, but only a few of them provide sea surface salinity observations within the framework of a limited number of pilot experiments. In the drifters, salinity is calculated by the traditional method using the results of the electrical conductivity and temperature measurements. There are a few problems related both to the principle of determining salinity by this method and to providing longterm stable running of conductivity sensors under the conditions of pollution and biological fouling. A drifter equipped with the module for the sound velocity and temperature measurements used for calculating salinity by an alternative method just aboard the drifter, was developed in Marine Hydrophysical Institute, Russian Academy of Sciences. The sound velocity and temperature module includes a specially designed time-of-flight sound velocity sensor with the fixed base and a quartz temperature sensor. In course of two years, numerous laboratory and in situ tests of several prototypes of the sound velocity and temperature module were performed. The laboratory tests showed that the repeatability limits for the results of the sound velocity measurements in the distilled water were ± 0.02 m/s. According to the data of the long-term in situ tests performed at intensive biological fouling, the error of salinity estimation resulted from of the sound velocity and temperature measurements were within ± 0.05 ‰. This result permits to expect that the sound velocity and temperature module parameters will remain stable in real conditions of long-term autonomous operation. Conclusions. The obtained results make it possible to recommend application of the drifters equipped with the modules for the sound velocity and temperature measurements as an effective tool for regular operational monitoring of the salinity field of the upper sea layer

Unified Software Platform for Intelligent Home Service Robots

Although the mobile manipulation capability is crucial for a service robot to perform physical work without human support, the long-term autonomous operation of such a mobile manipulation robot in a real environment is still a tremendously difficult task. In this paper, we present a modular, general purpose software framework for intelligent mobile manipulation robots that can interact with humans using complex human speech commands; navigate smoothly in tight indoor spaces; and finally detect and manipulate various household objects and pieces of furniture autonomously. The suggested software framework is designed to be easily transferred to different home service robots, which include the Toyota Human Support Robot (HSR) and our Modular Service Robot-1 (MSR-1) platforms. It has successfully been used to solve various home service tasks at the RoboCup@Home and World Robot Summit international service robot competitions with promising results.

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring.

This paper presents the technological developments and the policy contexts for the project “Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring” (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding. It is economically unviable to fulfill the current policy for ecosystem monitoring with traditional approaches. Thus, this project developed platforms for bentho-pelagic monitoring using an arrangement of crawler and stationary platforms at the Lofoten-Vesterålen (LoVe) observatory network (Norway). Visual and acoustic imaging along with standard oceanographic sensors have been combined to support advanced and continuous spatial-temporal monitoring near cold water coral mounds. Just as important is the automatic processing techniques under development that have been implemented to allow species (or categories of species) quantification (i.e., tracking and classification). At the same time, real-time outboard processed three-dimensional (3D) laser scanning has been implemented to increase mission autonomy capability, delivering quantifiable information on habitat features (i.e., for seascape approaches). The first version of platform autonomy has already been tested under controlled conditions with a tethered crawler exploring the vicinity of a cabled stationary instrumented garage. Our vision is that elimination of the tether in combination with inductive battery recharge trough fuel cell technology will facilitate self-sustained long-term autonomous operations over large areas, serving not only the needs of science, but also sub-sea industries like subsea oil and gas, and mining.

Autonomous node in the short wave radio network

The paper covers questions related to the creation of the comprehensive device designed for long-term autonomous operations as part of the radio networks for various purposes in short-wave bands. Such radio networks are intended for remote monitoring and control over facilities at distances of hundreds and thousands of kilometres. The device and features of functioning of autonomous nodes in short-wave radio networks are based on daily and seasonal characteristics of distribution of radio waves, operation under conditions of self-provision with electricity and in areas of harsh climate, as well as on impossibility of their timely repair and maintenance. The authors have proposed solutions to the issue of a reliable communication channel using advanced low-energy types of angle modulation with low-value signal-to-noise ratios and the choice of the best frequency for a given time of the day for specific nodes. There is an overview of issues related to the choice of an antenna type for the autonomous radio network node, taking into account changes of working ranges. The issue of power supply to the radio network node is solved by applying the combination of a photovoltaic panel and wind generator working from a frost-resistant battery. Thermal modes for electronic equipment are provided through the use of a thermostatically controlled container. Authors have presented a possible architecture for the short-wave range radio network node. The study results include advice for developers of autonomous nodes of shortwave radio networks.

Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation From Human Demonstrations

In this paper, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration (LbD) approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes (GPs). Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases.

Feasibility of RF energy harvesting for wireless gas sensor nodes

Wireless Sensor Network (WSN) paradigm has been applied to a high number of monitoring and control applications. However, the scarcity of WSN energy resources imposes restrictions on its truly ubiquitous and long-term deployments. We address the problem of long-term autonomous operation of a wireless gas sensor by employing the far-field RF energy harvesting technology at 900 MHz in the power range from −20 dBm to 10 dBm. The node conducts regular CH4 or CO measurements and communicates the data to a wireless network coordinator at 2.4 GHz. Experimental results demonstrate that the optimized energy consumption of WSN, as well as energy harvesting of at least 1 mW (0 dBm) from ambient RF signal, ensure the ‘perpetual’ operation of WSN.

Cooperative in-network computation in energy harvesting device clouds

The Internet of Things paradigm is creating an environment where the big data originators will be located at the edge of the Internet. Accordingly, data analytic infrastructure is also being relocated to the network edges, to fulfill the philosophy of data gravity, under the umbrella of Fog computing. The extreme edge of the hierarchical infrastructure consists of sensor devices that constitute the wireless sensor networks. The role of these devices has evolved tremendously over the past few years owing to significant improvements in their design and computational capabilities. Sensor devices, today, are not only capable of performing sense and send tasks but also certain kinds of in-network processing. As such, triple optimization of sensing, computing and communication tasks is required to facilitate the implementation of data analytics on the sensor devices. A sensor node may optimally partition a computation task, for instance, and offload sub-tasks to cooperative neighbouring nodes for parallel execution to, in turn, optimize the network resources. This approach is crucial, especially, for energy harvesting sensor devices where the energy profile and, therefore, the computation capability of each device differs depending on the node location and time of day. Accordingly, future in-network computing must capture the energy harvesting information of sensor nodes to jointly optimize the computation and communication within the network. In this paper, we present a theoretical model for computation offloading in micro-solar powered energy harvesting sensor devices. Optimum data partitioning to minimize the total energy consumption has been discussed based on the energy harvesting status of sensor nodes for different scenarios. The simulation results show that our model reduced both energy losses and waste due to energy conversion and overflows respectively compared to a data partitioning algorithm that offloads computation tasks without taking the energy harvesting status of nodes into consideration. Our approach also improves energy balance of a WSN which is an important factor for its long-term autonomous operation.

The STRANDS Project: Long-Term Autonomy in Everyday Environments

Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance.

Opportunistic sampling-based active visual SLAM for underwater inspection

This paper reports on an active SLAM framework for performing large-scale inspections with an underwater robot. We propose a path planning algorithm integrated with visual SLAM that plans loop-closure paths in order to decrease navigation uncertainty. While loop-closing revisit actions bound the robot's uncertainty, they also lead to redundant area coverage and increased path length. Our proposed opportunistic framework leverages sampling-based techniques and information filtering to plan revisit paths that are coverage efficient. We employ Gaussian process regression for modeling the prediction of camera registrations and use a two-step optimization procedure for selecting revisit actions. We show that the proposed method offers many benefits over existing solutions and good performance for bounding navigation uncertainty in long-term autonomous operations with hybrid simulation experiments and real-world field trials performed by an underwater inspection robot.

University of Michigan North Campus long-term vision and lidar dataset

This paper documents a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan’s North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on long-term autonomous operation in changing environments. The dataset is composed of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g. pedestrians, bicyclists and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g. falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference.

Improving information autonomy of marine SINS

The paper considers operation algorithms of a strapdown inertial navigation system comprising several angular rate sensors and one position gyro. Such a SINS can be optimally used onboard search underwater vehicles with a long-term autonomous operation in the areas where the sonar logs fail to provide coordinate dead-reckoning due to hydrological conditions. Position gyro data are used in SINS algorithms to obtain the navigation solution. Calibration mode in the vehicle surface position uses GNSS data, and navigation mode in the vehicle submerged position uses water speed log data. Results from MATLAB (Simulink) simulation modeling confirm the effectiveness of applying position gyro data in SINS to significantly improve its information autonomy.

Time Synchronization Algorithms in Low-Cost Wireless Sensor Network Systems

Time synchronization is an essential building block of sensor network systems. In this paper, we present a comprehensive study of least squares algorithms for time synchronization in sensor networks, with a focus on continuous monitoring and data collection applications. We propose a set of algorithms to address a number of issues in practical implementation on typical low-cost sensor network platforms, including a scaled signal model to achieve numerical stability in an ill-conditioned problem, sequential estimators for the scaled signal model to reduce computational complexity, a fast initialization scheme to improve energy efficiency, and outlier detection algorithms to improve robustness in long-term autonomous operations. The proposed algorithms are implemented and a measurement-based simulation method is employed to study the performance.

Kinetic Study of Homogeneously Mediated Electrode Reactions in Glucose-Based BioFuel Cells

The use of biofuel cells (BFC) is a promising approach to generate electricity. BFC can be successfully applied to achieve long-term autonomous operation of miniaturized implantable Body-Area-Network (BAN) devices. BAN devices are important for the development of future healthcare technologies. Glucose-based enzymatic BFC are a good option to power BAN devices due to the high availability of the fuel (glucose) in body fluids. Moreover, it shows excellent catalytic selectivity and good chemical safety. In this paper, the design of a glucose-based enzymatic BFC demonstrator is described and the performance of both the individual electrodes and the complete cell is evaluated. The measured maximum power output of the designed BFC-demonstrator was 5.8 μWcm-2. Additionally, the kinetics of the detailed energy conversion processes, occurring inside the BFC system, have been investigated from both an experimental and theoretical point of view. The proposed model describes the glucose oxidation at the anode of a BFC and includes the diffusion for all (electro-) chemically active species. The modeling results are qualitatively and quantitatively in good agreement with the experimental results.

Progress towards an Autonomous Field Deployable Diode-Laser-Based Differential Absorption Lidar (DIAL) for Profiling Water Vapor in the Lower Troposphere

A laser transmitter has been developed and incorporated into a micro-pulse differential absorption lidar (DIAL) for water vapor profiling in the lower troposphere as an important step towards long-term autonomous field operation. The laser transmitter utilizes two distributed Bragg reflector (DBR) diode lasers to injection seed a pulsed tapered semiconductor optical amplifier (TSOA), and is capable of producing up to 10 mJ of pulse energy with a 1 ms pulse duration and a 10 kHz pulse repetition frequency. The on-line wavelength of the laser transmitter can operate anywhere along the water vapor absorption feature centered at 828.187 nm (in vacuum) depending on the prevailing atmospheric conditions, while the off-line wavelength operates at 828.287 nm. This laser transmitter has been incorporated into a DIAL instrument utilizing a 35.6 cm Schmidt-Cassegrain telescope and fiber coupled avalanche photodiode (APD) operating in the photon counting mode. The performance of the DIAL instrument was demonstrated over a ten-day observation period. During this observation period, data from radiosondes were used to retrieve water vapor number density profiles for comparisons with the number density profiles retrieved from the DIAL data.

Navigation Satellite Clock Error Prediction Based on Functional Network

The high precision prediction of atomic clocks on board is a key technology for the long-term autonomous operation of a navigation satellite system. Some researches show that the performance of traditional prediction models of atomic clocks can not meet the requirements of practical applications. In order to improve the atomic clock error prediction accuracy, we propose a model based on functional network in this paper. According to the data characteristics of atomic clocks, the clock error series is firstly fit by polynomial and then the residuals is modeled by functional network. Finally, by using the data of GPS satellites, five independent prediction tests have been done to verify the model. The simulation results show that, compared with the traditional models, the proposed model can fit and predict clock error more effectively.