A Flexible, Open-Source DAQ for Real-Time Multi-Channel Signal Processing on Autonomous Platforms

This abstract is intended for the special session “Acoustic Vector Field Studies.” In this work, we will show our progress towards a low-power, lightweight, embedded data acquisition system and real-time signal processor for acoustic vector sensors, using an open-source microcontroller. Modern advances in low-cost, low-power “systems-on-a-chip” (SoC) provide unprecedented computational power, and some state-of-the-art devices are particularly well designed for acoustic signal processing applications. Here, we present the results of performance experiments, demonstrating the speed and accuracy of sampling and processing audio-frequency signals for a wide variety of signals and sampling regimes. We demonstrate trade-offs between sampling rates and resolutions for accuracy and speed at desired frequencies, and how these may be dynamically managed by autonomous systems. Finally, we will share some of our work towards real-time acoustic beamforming for use on autonomous vehicles.

Real-time scalable data acquisition systems and high performance transducer arrays are strongly interconnected subjects as most transducer arrays demands sophisticated readout systems in order to perform properly. Sound and ultrasound systems such SOund NAvigation and Ranging (SONAR), Sonography, Speech Recognition, Robotic Sensing and Sound Source Separation are a few of many systems that can deploy transducer arrays and require real-time high speed and expandable data acquisition systems. For these system characteristics such as speed, scalability and real-time signal processing are paramount, demanding highly developed data acquisition and processing platforms. Successfully integrating acoustic arrays with generic data acquisition systems is challenging due to the wide variety of requirements regarding speed, signal processing capabilities and scale that acoustic arrays can present. The objective of this work is to present an acoustic imaging system using CAPTAN (Compact And Programmable daTa Acquisition Node) architecture that can deliver these requirements while closely integrated with a specially designed sound imaging array.

It is important to evaluate the current reliability of a system during runtime, especially for AUV (Autonomous Unmanned Vehicle). In this paper, a FTA (Fault Tree Analysis) based method is presented to evaluate runtime reliability in system level. we describe how to construct the fault trees for runtime and how to calculate the realtime probability of top-level event. A case study on an autonomous unmanned aerial vehicle is given for illustration. The method can also be used to other similar scenarios.

ABSTRACTTo address the challenge of parallel parking in narrow environments, a multi‐segment path planning method is proposed for autonomous unmanned vehicles (AUVs), which integrates circular arc and dual clothoid curves, ensuring continuous path curvature and minimal longitudinal space occupancy. First, considering the narrow environmental constraints and the parameters of AUV, the planning for the preparatory phase path and the parking‐in phase path are completed, respectively. The role of the preparatory phase path is to guide the AUV to the target point, and the parking‐in phase path can guide the AUV into the target parking space. Then, by using the particle swarm optimization (PSO) algorithm with a penalty function framework, a multi‐constraint nonlinear programming path function model is established to seek out the parking path. Subsequently, the pure pursuit (PP) algorithm is employed to control the movement of AUV along the planned path. Finally, the simulation result and real‐vehicle experiment are conducted, which show that the parking path planning method proposed in this article enables AUV to safely complete parking operations in the narrow environment.

Unmanned autonomous vehicles have been used for military, shipping, transportation, and scientific research applications for decades. Following the advancement of artificial intelligence (AI) technology, autonomous vehicles now possess increased potential for sensing applications. AI-based sensing technologies not only reduce the complication of the data process, but also provide real time sensing results. This study demonstrates the design and applications of an ASV with an AI-based sensor. The functions of the proposed ASV include autonomous sailing, remote communications, smart 3D mapping, and real-time image detection and identification. In addition, the ASV also can sail in dangerous or shallow waters. The experimental results presented herein verify the performances of the proposed functions. The open module design of the ASV can lead to further developments of AI-based sensing applications.

This paper reports on design and implementation of data acquisition system based on dsPIC Microcontroller for Monitoring, Control and Security Application. Data acquisition is fundamental stage in any DSP, monitoring and digital control and security system. The efficiency and effectiveness of the system is defined by the quality of acquired data, which in turn depends on the characteristics of data acquisition system. There are two types of data acquisition; (a) digital (b) analog data acquisition, having different characteristics and system requirements. Microchip's dsPIC provides various on-chip integrated modules which enable efficient data acquisition such as 10/12-bit Analog to Digital Convertor (ADC) with up to 1Msps (Million samples per second) sampling rate, simultaneous sampling and various trigger mechanisms, Timers, Input Capture (IC), External (hardware) and Internal (software) Interrupt and processing capability up to 30 MIPS (Million Instructions Per Second). A system is developed for data acquisition of 16 analog signals with 10/12-bit resolution, simultaneous sampling of 4 signals, fixed and variable sampling rate, on chip storage and real-time signal processing capabilities. The system also supports for data acquisition of digital signals with time resolution of up to 33.33nsec and signal parameters like frequency, time period, pulse width, duty cycle, and delay & time difference between two signals. It can be customized according to the system requirements and provides advanced data acquisition capabilities to the low cost monitoring, control or security system.

Autonomous vehicle control based on receding horizon method

In this paper, we experimentally investigate the effect of state-of-the-art digital to analog converter (DAC) circuit, on the performance of high speed optical transmitter. We particularly consider two operating regimes, we refers as over- and under-sampling conditions. It is well known that a sampling rate of 64GSa/s limits the maximum baud rate to 32Gbaud with a sampling rate of 2 sample/symbol as a standard oversampling. However, in a number of applications including test, characterization, and measurement of advanced communication systems, it is needed to test the capacity to use the hardware well above its normal operation regime. One important, yet attractive testing, is to examine the behavior of an overall communication system or its subsystems in case of 2-fold criterion is not respected. In these cases, the optical transmitter is constrained to generate and transmit optical symbols at a baud rate that is much higher than the half of the hardware limited sampling rate. In our case, we generate variable baud rates higher than 32Gbaud using an always fixed sampling rate 64GSa/s (i.e. limited by hardware). We hence constrain our symbols to be generated by less than 2 samples/symbol we refer as under sampling regime. Our experimental results show that in under sampling regime, we obtain a variable baud rate ranging from 32 up to 56Gbaud using a sampling ratio starting by 2 and decreasing down to 1.14 respectively. In addition we show, how these high and variable baud rates, have been achieved at the expense of much larger spectral bandwidth, important signal distortions especially for the highest frequency band, and a net decrease in the modulation order from 128 down to 4. We also investigate the performance of the generated signals in terms of bit error rate (BER) and error vector magnitude (EVM) and illustrate how the performance dramatically degrades as the sampling rate decreases. Furthermore, we digitally pre-emphasize the DP-MQAM Optical transmitter in order to pre-compensate for devices imperfections. This relaxes the signal processing at the receiver side at a maximum expense of 1dB penalty for 32Gbaud speed.

<div class="htmlview paragraph">Microprocessor based automatic test equipment has made it possible for maintenance personnel to conveniently make sophisticated tests of vehicle engines. In some cases, the most difficult part of the process is no longer the data evaluation, but rather the data acquisition or sensing. This has made sensor development an important part of improving vehicle testing. This paper discusses the development and operating principles of a diesel engine speed sensor which improves substantially the mechanic's access to the speed signal, thereby augmenting the use of existing automated tests based on speed signal processing. Developed by the U.S. Army Tank Automotive Command, this Variable Reluctance Sensor (VRS) overcomes problems of diesel speed signal accessibility. Conventional speed signal sources, such as tachometer drives, are difficult for a military mechanic to access due to the presence of engine accessories and armor. The VRS senses the motion of a rocker arm by projecting a strong magnetic field around it and detecting the field variations caused by its motion. Thus the mechanic can measure engine speed without tools by simply placing the VRS on the valve cover. Then using Simplified Test Equipment for Internal Combustion Engines (STE/ICE) the mechanic can rapidly assess engine speed adjustments and power. This paper describes the technical problems encountered in VRS development and reviews the development sequence. It then presents the final mechanical configuration of the VRS and details the signal processing logic and circuitry. Finally the intended VRS applications are discussed, with mention of potential future uses with Army automated test equipment.</div>

The performance of autonomous vehicle systems is currently limited by their inability to accurately analyze their surrounding environment. In order to function in a dynamic real world environment, an autonomous vehicle system must be capable of interpreting terrain based upon predetermined mission goals. This paper describes a concept of knowledge-based terrain analysis currently being developed to support the information needs of an autonomous helicopter system. The terrain analysis system consists of five integrated processing stages. Each process is discussed in detail and supported by a number of mission oriented examples. Int roduction One of the most unique system concepts being explored today is that of the development of totally autonomous land, sea, and air vehicles [1 - 3]. For military applications, autonomous vehicles provide a mechanism for removing humans from modern day battlefields while not impacting the tactical capabilities of the battle force. Civilian applications for such a device entail the investigation of hazardous areas (e.g. nuclear reactors and toxic waste dumps), search over large areas (e.g. downed aircraft or lost campers) and monitoring of large facilities (e.g. robot security police). As military funding for these systems is currently at an alltime high, a variety of autonomous system concepts are continually being presented in the technical literature. The primary limitation of an autonomous vehicle still lies in its inability to accurately analyze surrounding terrain information in order to generate tactical plans of action. This paper describes activities being performed in the Artificial Intelligence Branch of the Georgia Tech Research Institute on knowledge-based terrain analysis, particularly in support of the autonomous helicopter program.System OverviewThe knowledge-based tactical terrain analysis system consists of five separate processing phases: [1] image segmentation, [2] region classification, [3] visual model, [4] route planning, [5] threat location and coverage. Each processing phase is briefly discussed in the following subsections with actual image results given where appropriate.

Dynamical systems generate complex signals. Very often, traditional sonar signal processing in both the time domain and frequency domain are not adequate enough to characterize the complex dynamics of underwater sources that generate, radiate and/or reflect sonar signals. Recent advances in signal processing are aimed at capitalizing on the inherent complexities in a signal structure by applying information theoretic/chaotic processing analysis tools to reveal signal dynamics, otherwise unseen when traditional (linear) signal processing methods are applied. Here examples are presented of periodicities (i.e., limit cycles), quasi-periodicites (2-torus and n-torus motion) and nonperiodic (chaotic) motion as applied to signals-of-interest in underwater acoustic applications. It is demonstrated how nonlinear diagnostic tools, when appropriately applied to underwater acoustic time series measurements, can be used for extracting new information about system dynamics.

Introduction:Corridor selection plays a critical role in determining the optimal locations for deploying autonomous vehicles. With the rapid advancement of self-driving technology, identifying suitable corridors becomes paramount to maximize the efficiency, safety, and overall performance of autonomous vehicle systems. The process involves analyzing various factors such as traffic patterns, infrastructure conditions, regulatory frameworks, and user preferences. This paper explores the significance of corridor selection and highlights the key considerations involved, shedding light on the crucial decision-making process for locating autonomous vehicles in the most favorable corridors. Research significance: : The research on corridor selection for locating autonomous vehicles holds significant importance in the field of transportation and autonomous systems. It addresses the need to identify the most suitable corridors for deploying self-driving vehicles, taking into account factors such as traffic flow, infrastructure conditions, and regulatory aspects. The outcomes of this research can inform policymakers, urban planners, and transportation companies in making informed decisions regarding the placement of autonomous vehicles, ultimately leading to optimized traffic management, enhanced mobility, and improved safety on roadways. Methodology: The methodology for studying the supplier selection process in the dairy industry will involve a combination of qualitative and quantitative approaches. Firstly, a comprehensive literature review will be conducted to gather insights into the existing supplier selection criteria and methodologies used in the industry. This will be followed by qualitative interviews and surveys with key stakeholders such as dairy company managers, procurement professionals, and suppliers to gather their perspectives on the supplier selection process. Additionally, quantitative analysis will be conducted to measure the importance and performance of various supplier selection criteria through statistical tools and models such as Analytic Hierarchy Process (AHP) and TOPSIS. Alternative parameters: Availability of road space, Segregation from vehicle traffic, Segregation from pedestrians, Number of passengers, Revenue potential. Evaluation parameters:Availability of road space, Segregation from vehicle traffic, Segregation from pedestrians, Number of passengers, Revenue potential Result:from the result the availability of road has secured the first place and segregation from pedestrians stays at the least. Conclusion:: availability of road plays an important role in building corridor selection for locating autonomous vehicles. Keywords: Automated driving, policy and societal implications, ripple effect, first, second, and third order impacts.

This work presents a methodological proposal for acquisition of biometric data through telemetry basing its development on a research-action and a case study. Nowadays, the qualified professionals of physical evaluation have to use specific devices to obtain biometric signals and data. These devices in the most of the time are high cost and difficult to use and handling. Therefore, the methodological proposal was elaborate in order to develop, conceptually, a bio telemetric device which could acquire the desirable biometric signals: oxymetry, biometrics, corporal temperature and pedometry which are essential for the area of physical evaluation. It was researched the existent biometrics sensors, the possible ways for the remote transmission of signals and the computer systems available so that the acquisition of data could be possible. This methodological proposal of remote acquisition of biometrical signals is structured in four modules: Acquisitor of biometrics data; Converser and transmitter of biometric signals; Receiver and Processor of biometrics signals and Generator of Interpretative Graphs. The modules aim the obtention of interpretative graphics of human biometric signals. In order to validate this proposal a functional prototype was developed and it is presented in the development of this work.

Autonomous systems operating in dangerous and hard-to-reach environments such as defense systems deployed into enemy territory, petroleum installations running in remote arctic and off-shore environments, or space exploration systems operating on Mars and further out in the solar system often are designed with a wide operating envelope and deployed with control systems that are designed to both protect the system and complete mission objectives, but only when the on-the-ground environment matches the expected and designed for environment. This can lead to overly conservative operating strategies such as preventing a rover on Mars from exploring a scientifically rich area due to potential hazards outside of the original operating envelope and can lead to unanticipated failures such as the loss of underwater autonomous vehicles operating in Earth’s oceans. This paper presents an iterative method that links computer simulation of operations in unknown and dangerous environments with conceptual design of systems and development of control system algorithms. The Global to Local Path Finding Design and Operation Exploration (GLPFDOE) method starts by generating a general mission plan from low resolution environmental information taken from remote sensing data (e.g.: satellites, plane fly-overs, telescope observations, etc.) and then develops a detailed path plan from simulated higher-resolution data collected “in situ” during simulator runs. GLPFDOE attempts to maximize system survivability and scientific or other mission objective yield through iterating on control system algorithms and system design within an in-house-developed physics-based autonomous vehicle and terrain simulator. GLPFDOE is best suited for autonomous systems that cannot have easy human intervention during operations such as in the case of robotic exploration reaching deeper into space where communications delays become unacceptably large and the quality of a priori knowledge of the environment becomes lower fidelity. Additionally, in unknown extraterrestrial environments, a variety of unexpected hazards will be encountered that must to be avoided and areas of scientific interest will be found that must be explored. Existing exploratory platforms such as the Mars Exploratory Rovers (MERs) Curiosity and Opportunity either operate in environments that are sufficiently removed from immediate danger or take actions slowly enough that the signal delay between the system and Earth-based operators is not too great to allow for human intervention in hazardous scenarios. Using the GLPFDOE methodology, an autonomous exploratory system can be developed that may have a higher likelihood of survivability, can accomplish more scientific mission objectives thus increasing scientific yield, and can decrease risk of mission-ending system damage. A case study is presented in which an autonomous Mars Exploration Rover (MER) is generated and then refined in a simulator using the GLPFDOE method. Development of the GLPFDOE methodology allows for the execution of more complex missions by autonomous systems in remote and inaccessible environments.

An autonomous unmanned surface vehicle (USV) landing system of vertical takeoff and landing (VTOL) fixed-wing unmanned aerial vehicle (UAV) based on raptor vision is presented in this paper. The color saliency detection method based on raptor vision in the biological raptor vision color resolution space is developed to obtain the landing target and markers in the mission scene. A three-color nested landing target scheme is designed to accommodate the different height of UAV. Considering the various interferences exist in the complex environment, the target occlusion strategy and the go-around (GA) strategy are supplemented in the vision navigation system to improve the accuracy and security of the landing mission. The complete UAV/USV flight verification system is developed to validate the effectiveness of the proposed method. Results show that the autonomous landing system based on raptor vision obtain a quite satisfactory landing outcome. The horizontal errors of UAV autonomous landing are both less than 5cm. The success rate of UAV landing has been greatly improved. Therefore, the proposed autonomous landing system provides a feasible technical pathway for UAV landing on a USV.