On-board Computer Research Articles

Software Integration of an IMU Sensor to a CubeSat Platform Based on CSP (CubeSat Space Protocol)

The development and use of nanosatellites have increased in recent years. Space programs were exclusive to governments with significant capital. Now, nanosatellites have changed the orbital ecosystem and have allowed new regions and a wide range of industries to position themselves in the so-called new space, a growing sector that democratizes the commercialization of space thanks to a smaller, more agile, and affordable technology. Nanosatellites have similar capabilities to their conventional counterparts but are commonly used for highly specific missions such as Earth observation, telecommunications, and meteorology. The complex development of a nanosatellite requires solving different engineering problems to assembly and integrate all hardware and software components into a small space. It requires the monitoring, control, and operation of the satellite’s features from the ground segment. One major challenge is the development of satellite mission control software for both space and ground segments in limited-time scheduled missions. This article describes the integration process of an EPSON M-G364PDCA Inertial Measurement Unit (IMU) to a CubeSat platform nanosatellite based on the Danish company GomSpace devices and a CSP protocol. The result is a hardware implementation and software development by the Colombian Air Force (FAC) team as part of the FACSAT-2 program. The integration of these components into the space and ground segment contributes to solve one challenge in the development of control software for space missions, as described above, and becomes the first approach for Colombian space nanosatellites software development. In addition, this research presents the Colombian Air Force configuration for space mission subsystems on the CSP network, the software development for the main on-board computer based on a NanoMind A3200 to setup the IMU —controlled and monitored from the ground segment through a CSP terminal on a Linux server—, and setup telemetry data from space to be sent periodically to the ground segment and stored locally in a MongoDB database for its subsequent visualization and analysis.

PECULIARITIES OF USING THE CAN BUS FOR READING AND PROCESSING INFORMATION WITH SUBSEQUENT TRANSMISSION TO THE GPS MONITORING SYSTEM

The theses describes the possibilities of using the CAN bus for reading and processing data from various sensors on the vehicle and transferring it to the on-board computer. The most common data types read from sensors and methods of connection to the CAN

Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks

Benefiting from advanced satellite payload technologies, edge computing servers can be deployed on satellites to achieve orbital computing and reduce the mission processing delay. However, geostationary Earth orbit (GEO) satellites are hindered by long-distance communication, whereas low Earth orbit (LEO) satellites are restricted by time windows. Relying solely on GEO or LEO satellites cannot meet the strict quality of service (QoS) requirements of on-board missions while conserving energy consumption. In this paper, we propose a computation offloading strategy for GEO/LEO hybrid satellite networks that minimizes total energy consumption while guaranteeing the QoS requirements of multiple missions. We first innovatively transform the on-board partial computation offloading problem, which is a mixed-integer nonlinear programming (MINLP) problem, into a minimum cost maximum flow (MCMF) problem. Then, the successive shortest path-based computation offloading (SSPCO) method is introduced to obtain the offloading decision in polynomial time. To evaluate the effectiveness and performance of SSPCO, we conduct a series of numerical experiments and compare SSPCO with other offloading methods. The experimental results demonstrate that our proposed SSPCO outperforms the reference methods in terms of total energy consumption, QoS violation degree, and algorithm running time.

Multi-Modal Mobility Morphobot (M4) with appendage repurposing for locomotion plasticity enhancement

Robot designs can take many inspirations from nature, where there are many examples of highly resilient and fault-tolerant locomotion strategies to navigate complex terrains by recruiting multi-functional appendages. For example, birds such as Chukars and Hoatzins can repurpose wings for quadrupedal walking and wing-assisted incline running. These animals showcase impressive dexterity in employing the same appendages in different ways and generating multiple modes of locomotion, resulting in highly plastic locomotion traits which enable them to interact and navigate various environments and expand their habitat range. The robotic biomimicry of animals’ appendage repurposing can yield mobile robots with unparalleled capabilities. Taking inspiration from animals, we have designed a robot capable of negotiating unstructured, multi-substrate environments, including land and air, by employing its components in different ways as wheels, thrusters, and legs. This robot is called the Multi-Modal Mobility Morphobot, or M4 in short. M4 can employ its multi-functional components composed of several actuator types to (1) fly, (2) roll, (3) crawl, (4) crouch, (5) balance, (6) tumble, (7) scout, and (8) loco-manipulate. M4 can traverse steep slopes of up to 45 deg. and rough terrains with large obstacles when in balancing mode. M4 possesses onboard computers and sensors and can autonomously employ its modes to negotiate an unstructured environment. We present the design of M4 and several experiments showcasing its multi-modal capabilities.

Data Acquisition for Condition Monitoring in Tactical Vehicles: On-Board Computer Development

This paper outlines the development of an onboard computer prototype for data registration, storage, transformation, and analysis. The system is intended for health and use monitoring systems in military tactical vehicles according to the North Atlantic Treaty Organization Standard Agreement for designing vehicle systems using an open architecture. The processor includes a data processing pipeline with three main modules. The first module captures the data received from sensor sources and vehicle network buses, performs a data fusion, and saves the data in a local database or sends them to a remote system for further analysis and fleet management. The second module provides filtering, translation, and interpretation for fault detection; this module will be completed in the future with a condition analysis module. The third module is a communication module for web serving data and data distribution systems according to the standards for interoperability. This development will allow us to analyze the driving performance for efficiency, which helps us to know the vehicle’s condition; the development will also help us deliver information for better tactical decisions in mission systems. This development has been implemented using open software, allowing us to measure the amount of data registered and filter only the relevant data for mission systems, which avoids communication bottlenecks. The on-board pre-analysis will help to conduct condition-based maintenance approaches and fault forecasting using the on-board uploaded fault models, which are trained off-board using the collected data.

Using redundant control to optimize control torque

The object of research is the process of automatic control of the redundant structure of the vessel's executive devices for extreme rotation in the yaw channel. Traditionally, redundant structures have been used to improve the reliability of automated control systems and the maneuverability of vessels. At the same time, control redundancy can also be used to optimize control processes, thereby reducing fuel consumption, increasing control forces and moments, and reducing the time required to perform operations. This allows gaining advantages in movement over vessels not equipped with optimization modules. The paper considers the optimal management of the redundant structure of an offshore vessel, which ensures the rotational movement of the vessel around the center of rotation with the maximum angular velocity. As well as simultaneous maintenance of a given position or movement in the longitudinal and lateral channel, taking into account control restrictions. This problem is reduced to a nonlinear optimization problem with nonlinear and linear control constraints. The method, algorithmic and software of the module of extreme rotation of the vessel with a redundant structure of executive devices have been developed. The workability and efficiency of the developed method, algorithmic and software are verified by mathematical modeling in the closed circuit «Control Object – Control System». The results of the conducted experiment showed that the use of optimal control allows, in comparison with traditional methods of splitting controls, to increase the control moment and angular speed of rotation by 1.5–2 times. The obtained opportunities are explained by the use of a mathematical model of the redundant control structure and the optimization procedure for calculating optimal controls in the on-board computer of the automated system. The developed method can be used on vessels, provided it is integrated into the existing automated system of the on-board computer with an open architecture, to increase the capabilities of automatic traffic control.

Study on the practical method implementation of the navigation equations in a simulated FMS

This paper presents the main objective of Air Traffic Management Systems – ATMS that is to achieve optimized performance in terms of aviation safety and the enhancement of the current air transport system, through the in-depth study of the complex integration of all advanced technologies regarding air traffic monitoring, aeronautical communications on-board computers, display and control systems within the extended framework of Air Traffic Services - ATS. The Flight Management System - FMS is indispensable for flight planning, air navigation, flight performance management, aircraft guidance on the flight path and continuous monitoring of the progress during flight. The FMS is the device used by the flight crew to select various modes of flight control via the flight control computer - FCM and to display flight plans and other flight data via the Multi-Functional Control Display Unit - MCDU. The flight path is monitored by the MCDU and the Electronic Instrument System – EIS. The in-depth study of Air Transport Efficiency when applying air traffic service operational analysis and trajectory determination methods to identify key areas of Air Traffic Management inefficiencies and to prioritize and implement appropriate optimization methods are presented and analyzed in detail in this paper.

Integration and preliminary evaluation of a robotic cotton harvester prototype

Cotton is conventionally harvested with large and costly harvesters once at the end of the growing season. Fiber in the early-opened bolls is left on the plants till the end of the season to be harvested, resulting in its exposure to weather and thus degraded quality. Furthermore, heavy cotton harvesters can compact the soil and adversely affect soil health and crop yield. These issues can potentially be addressed by using small robotic cotton harvesters that can harvest multiple times during the season, picking seed cotton soon after the bolls open. Robotic cotton harvesters are not currently available. This article presents the system integration and performance evaluation of a new robotic cotton harvester prototype consisting of a 3-DOF (degrees of freedom) linear robotic arm with a custom three-finger end-effector, integrated with a deep-learning based perception module to enable fast and automatic harvesting of cotton. The perception system utilized a stereovision camera and an onboard computer with a trained YOLOv4-tiny algorithm for detecting and locating cotton bolls within the plant. Three different control algorithms were examined to control manipulation of the arm and run the end-effector throughout the harvesting process. Performance of the harvester was evaluated in terms of picking ratio – picked seed cotton over available seed cotton in the image – and cycle time through laboratory tests using real cotton plants. Results showed that a closed-loop control algorithm using continuous feedback from cotton boll images during picking was the most efficient, successfully harvesting 72% of the seed cotton with an average cycle time of 8.8 s.

On-Board Computer and Testing Platform for CubeSat Development

The design and development of a CubeSat testing platform built from scratch is the focus of this work. The investigation was conducted as part of the Canadian CubeSat Project (CCP), initiative conducted by the Canadian Space Agency (CSA) to support the development of 15 CubeSats across Canada. In this paper, a particular emphasis is placed on three key subsystems: an On-Board Computer (OBC), a GNSS based payload, and a communication board, all connected together through a FlatSat board. The Mission software running on an STM32-microcontroller-based OBC is responsible for managing all CubeSat activities. The OBC was designed to meet a range of requirements, including mechanical, electrical, and thermal requirements. Indeed, due to the intense heat and radiation that the CubeSat will be exposed to in Low Earth Orbit (LEO), the CubeSat may experience many difficulties, potentially leading to mission failure. The risk-reduction techniques used in the design of the OBC will be discussed in detail. The tests performed on the developed OBC were successful, including an initial power test and a vacuum test, where the microcontroller entered low-power mode for a total of 10 seconds, consuming only 0.0528 W of power.

Noise-Tolerant Identification and Tuning Approach Using Deep Neural Networks for Visual Servoing Applications

Vision-based control of unmanned aerial vehicles (UAVs) has been adopted in a wide range of applications due to the availability of low-cost onboard sensors and computers. Tuning such systems to work properly requires extensive domain-specific experience, which limits the growth of emerging applications. Moreover, obtaining performance limits for UAVs performing visual servoing is difficult due to the complexity of the models used. In this article, we propose a novel noise-tolerant approach for real-time identification and tuning of visual servoing systems. This is based on the deep neural networks (DNNs) classification of the system response generated by the modified relay feedback test (MRFT). The proposed method, called DNN with noise-protected MRFT (DNN-NP-MRFT), can be used with a multitude of vision sensors and estimation algorithms despite high levels of sensor noise. The response of DNN-NP-MRFT to noise perturbations is investigated and its effect on identification and tuning performance is analyzed. The proposed DNN-NP-MRFT is able to detect performance changes induced by the use of high latency vision sensors or by integrating an inertial measurement unit sensor into the UAV states estimation pipeline. Experimental identification closely matches simulation results, which can be used to explain the system behavior and predict the closed-loop performance limits for a given hardware and software setup. We also demonstrate the ability of DNN-NP-MRFT-tuned UAVs to reject external disturbances like wind or human push and pull. Finally, we discuss the advantages of the proposed DNN-NP-MRFT visual servoing design approach compared with other approaches in the literature.

Fast On-Orbit Pulse Phase Estimation of X-Ray Crab Pulsar for XNAV Flight Experiments

The recent flight experiments with Neutron Star Interior Composition Explorer (\textit{NICER}) and \textit{Insight}-Hard X-ray Modulation Telescope (\textit{Insight}-HXMT) have demonstrated the feasibility of X-ray pulsar-based navigation (XNAV) in the space. However, the current pulse phase estimation and navigation methods employed in the above flight experiments are computationally too expensive for handling the Crab pulsar data. To solve this problem, this paper proposes a fast algorithm of on-orbit estimating the pulse phase of Crab pulsar called X-ray pulsar navigaTion usIng on-orbiT pulsAr timiNg (XTITAN). The pulse phase propagation model for Crab pulsar data from \textit{Insight}-HXMT and \textit{NICER} are derived. When an exposure on the Crab pulsar is divided into several sub-exposures, we derive an on-orbit timing method to estimate the hyperparameters of the pulse phase propagation model. Moreover, XTITAN is improved by iteratively estimating the pulse phase and the position and velocity of satellite. When applied to the Crab pulsar data from \textit{NICER}, XTITAN is 58 times faster than the grid search method employed by \textit{NICER} experiment. When applied to the Crab pulsar data from \textit{Insight}-HXMT, XTITAN is 180 times faster than the Significance Enhancement of Pulse-profile with Orbit-dynamics (SEPO) which was employed in the flight experiments with \textit{Insight}-HXMT. Thus, XTITAN is computationally much efficient and has the potential to be employed for onboard computation.

Data acquisition unit for suborbital rocket vibration measurement

Suborbital rockets and space launch vehicles experience high levels of vibration. They are caused by various factors, such as aerodynamic effects on the external surfaces and engine combustion instability. The second factor is especially pronounced in hybrid rocket engines, such as the main engine of the ILR-33 AMBER 2K rocket. In order to ensure the correct operation of all the equipment and payload on board, vibration testing of the devices is necessary as part of pre-flight qualification. For such tests to be truly indicative of in-flight performance, the vibration environment of the vehicle must be analysed. A conceptual measurement method was proposed, along with a prototype measurement system. The system is adapted to be integrated into the modular ILR-33 AMBER 2K on-board computer or work independently as a separate module. The device measures vibration in three axes in several key points onboard the rocket, with the possibility of connecting multiple units in a daisy-chain configuration. This paper presents the design of the module, including the key aspects of integrating it as part of the avionics package. The means of acquiring and storing of measurement data are presented. The method of validating the system’s data acquisition capabilities is described in detail. Qualification testing results and identified issues are discussed. Possible future improvements are proposed.

ОРГАНИЗАЦИЯ ВЫЧИСЛЕНИЙ И ДИАГНОСТИРОВАНИЕ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ БОРТОВЫХ ВЫЧИСЛИТЕЛЕЙ РОБОТОТЕХНИЧЕСКИХ КОМПЛЕКСОВ

The aim of the study is to improve the efficiency of high-level design of robotic systems in termsof management of computing and diagnostics of on-board computer software. Three problems areconsidered: assignment, scheduling and diagnostics. The first problem, the assignment task, is todetermine the necessary resources and build the distribution of program modules among the onboardcomputer processors in accordance with a given criterion. The article presents a mathematicalformulation of the problem, algorithms are given, and the presence of areas of effective dominance ofalgorithms depending on the selected criterion is shown. The second problem, the scheduling problem,is to determine the sequence of tasks in multi-channel systems in accordance with a given criterion.A mathematical formulation of the problem is given, algorithms and the results of their studyare given. A feature of the scheduling algorithms under consideration is the use of a unified approach,namely the concept of the dominance relation between processors and the construction ofsolvable classes of systems. The third problem is software diagnostics. The complexity of the task ofdiagnosing computer systems is determined not only by their high dimensionality, but also by themultiplicity of causes of violations. The source of violations of the computing process can be bothhardware failures and errors in the organization of calculations made by developers. The article usesa hierarchical approach, in this case, the components of the system, connected by an inclusion relation,are arranged in accordance with the level of complexity in such a way that the model of higherlevelcomponents is represented by a composition of lower-level models. For each level, own diagnostictools are synthesized, focused on failures of information links between the components of theprevious level. The article proposes an approach to test diagnostics using a network dynamic model,which involves the introduction of redundancy in order to simplify the diagnostic experiment andreduce the complexity of its preparation. This approach allows you to automatically synthesize diagnostictools and detect violations in the addressing of information exchanges when the software ofrobotic systems works as intended. Approbation of the algorithms under consideration was carriedout using software developed by the author at the stage of designing on-board systems at JSC «Concern«Central Research Institute «Elektropribor».

THE TERM COMBINATION AND THE METAPHOR IN THE OFFICIAL BUSINESS DOCUMENT: COGNITIVE ASPECT

THE TERM COMBINATION AND THE METAPHOR IN THE OFFICIAL BUSINESS DOCUMENT: COGNITIVE ASPECT

ArduSiPM technology: Compact and light All-in-one detectors for space application

ArduSiPM technology integrates System on Chip (SoC) innovation and Silicon photomultiplier (SiPM) detector improvements into a new generation of all-in-one scintillation detectors, developed by INFN Rome since 2014. To minimize reliance on pre-built Commercial off-the-shelf (COTS) components, we utilize the peripherals and CPU of the SoC for edge computing tasks and various functions, such as Counters, Analog-to-Digital Converters (ADCs), Digital-to-Analog Converters (DACs), Pulse-Width modulation (PWM), and digital input/output interfaces. Our compact electronic system eliminates the need for external data acquisition systems and ASICs, while also enabling the CPU to perform edge computing functions. The detector, capable of measuring the rate, arrival time (with a resolution of 7 ns correlated to the SoC main clock), and number of photons arriving on the SiPM, is ideal for radiation monitoring and highly sensitive photon detection. The INFN MICRO experiment explores circuits and engineering solutions for developing detectors for diverse fields such as Transient Luminous Events, analytical chemistry, and nano/picosatellites. Our ultimate goal is to integrate the detector into the onboard computer of a satellite, akin to navigation instruments and attitude control systems, with future improvements in SoC technology easily leveraged thanks to our approach’s independence from the specific SoC.

A CSAR 3D Imaging Method Suitable for Edge Computation

Due to the large amount of CSAR echo data carried by UAVs, either the original echo data need to be transmitted to the ground for processing or post-processing must be implemented after the flight. Therefore, it is difficult to use edge computing power such as a UAV onboard computer to implement image processing. The commonly used back projection (BP) algorithm and corresponding improved imaging algorithms require a large amount of computation and have slow imaging speed, which further limits the realization of CSAR 3D imaging on edge nodes. To improve the speed of CSAR 3D imaging, this paper proposes a CSAR 3D imaging method suitable for edge computation. Firstly, the improved Crazy Climber algorithm extracts sine track ridges that represent the amplitude changes in the range-compressed echo. Secondly, two-dimensional (2D) profiles of CSAR with different heights are obtained via inverse Radon transform (IRT). Thirdly, the Hough transform is used to extract the intersection points of the defocused circle along the heights in the X and Y directions. Finally, 3D point cloud extraction is completed through voting screening. In this paper, image detection methods such as ridge extraction, IRT, and Hough transform replace the phase compensation processing of the traditional BP 3D imaging method, which significantly reduces the time of CSAR 3D imaging. The correctness and effectiveness of the proposed method are verified by the 3D imaging results for the simulated data of ideal targets and X-band CSAR outfield flight raw data carried by a small rotor unmanned aerial vehicle (SRUAV). The proposed method provides a new direction for the fast 3D imaging of edge nodes, such as aircraft and small ground terminals. The image can be directly transmitted, which can improve the information transmission efficiency of the Internet of Things (IoT).

Drone Swarm Path Planning for Mobile Edge Computing in Industrial Internet of Things

Drone-swarm-assisted mobile edge computing (MEC) provides extra computation and storage capacity for smart city applications and the industrial Internet of Things. To solve the problems of traditional fixed base stations in complex terrain, including cost of deployment, transmission loss of telecommunication, and limited coverage, this paper brings forward the unmanned aerial vehicles (UAVs) as mobile edge computing nodes in the air. For the purpose of matching the dynamic mobile devices and UAV trajectory, this paper raises a multi-UAVs-assisted mobile edge computing offloading algorithm based on global and local path planning controlled by ground station and onboard computer. Firstly, this paper considers a drone swarm scheduling and allocation strategy based on the priority of monitoring areas, UAVs residual energy and distance to target points, so that to minimize the global flight length and energy consumption. Secondly, based on user mobility, this paper calculates the optimal communication coverage of UAV, and jointly optimizes the local path planning and computing offloading, so that to maximize the number of offloading services and minimize the total latency in completing the computation task. Finally, based on the total latency and energy consumption of path planning and computation offloading, a UAV cluster computation offloading strategy with optimized energy efficiency is realized. Experimental results prove that the proposed algorithm can provide more offloading services while obtaining shorter path length and greater energy efficiency.

Secure Task Offloading for MEC-Aided-UAV System

The promise of unmanned aerial vehicles (UAVs) combined with the mobile edge computing (MEC), named MECaided-UAV extends the MEC application to offer new flexible, low-latency computing services and considerable utilities for pervasive sensing of the world. In MEC, the UAV with limited onboard energy and computation resources needs to offload its tasks to resource-rich ground base stations (GBSs) servers. Because of the broadcast nature of line-of-sight (LoS) channels, one of the key challenges in task offloading is to guarantee that confidential data is offloaded safely to the GBSs without being intercepted by eavesdroppers (Eves). In this MEC-aided-UAV system, the GBSs help the UAV compute the offloaded tasks and transmit the artificial noise (AN) to suppress the vicious Eves.We make the first attempt to study the maximum-minimum average secrecy capacity problem, including joint optimization of the trajectory and transmit power of the UAV, the transmit power of AN, the local computation ratio, and the selection of GBSs with consideration of the practical constraints of completion delay of the tasks, maximum velocity, and the power consumption. The optimization issue is confirmed as a mixed-integer nonconvex problem. Thereafter, a low-complexity iterative algorithm with the block coordinate descent method and successive convex approximation technique is put forward to get its suboptimal solution. In addition, the convergent solution can be achieved by solving the subproblems in turn. Evaluation results validate that the proposed secure offloading scheme significantly effectiveness the baselines by 17.4%-71.2% on the maximum-minimum average secrecy capacity

Formation encirclement of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy

AbstractThis paper focuses on the formation encirclement problem of multiple quadrotors subjected to semi‐Markov jump switching topologies under consensus strategy. A target encirclement control algorithm is designed to achieve an enclosed formation, which drives autonomous quadrotors around the target so the target can't get away. Afterward, we propose a decentralized consensus‐based control protocol to realize the encirclement of the target quadrotor, where onboard miniature computers carry out all necessary computations. Notably, the quadrotor is modeled by a double integrator and the switching topology is described by a signed graph subjected to the semi‐Markov jump process. Whereafter, some criteria in the form of linear matrix inequalities are constructed based on the Lyapunov stability theory to achieve encirclement behavior. Ultimately, the proposed approach is verified by a comparison simulation and two flight experiments with three follower quadrotors enclosing a leader quadrotor.

Development and In-Orbit Operation of Deep Learning Attitude Sensor

This study presents in-orbit demonstrations of a deep learning attitude sensor (DLAS) aboard the Japan Aerospace Exploration Agency (JAXA) small-satellite, Rapid Innovative Payload Demonstration Satellite 1. The DLAS is a spaceborne component that consists of star trackers, Earth cameras, and onboard computers. We propose a novel attitude determination method that uses a small camera and image recognition algorithm on board the DLAS to achieve three-axis attitude determination with minimal weight and volume. The DLAS mission further includes a demonstration of a low-cost high-performance star tracker that integrates consumer products. The development of the DLAS began in 2016 when it was adopted as the theme of the JAXA Innovative Satellite Technology Demonstration program. It was launched into a sun-synchronous orbit at an altitude of 500 km in January 2019 for approximately one year. This paper describes the results obtained from the DLAS operation and its future applications. The DLAS demonstrated an Earth camera image identification accuracy of more than 70%. Moreover, the obtained attitude determination accuracy was approximately 2.8 deg for the Earth camera and approximately 14 arcs for the star trackers. The technologies developed in the DLAS have been implemented in a subsequent microsatellite (HIBARI) of the Tokyo Institute of Technology.