Autonomous Systems Laboratory Research Articles

A ROS-Based GNC Architecture for Autonomous Surface Vehicle Based on a New Multimission Management Paradigm

This paper presents the design and implementation of BAICal (Intelligent Autonomous Buoy by the University of Calabria), an autonomous surface vehicle (ASV) developed at the Autonomous Systems Lab (LASA) of the Department of Computer Science, Modeling, Electronics, and Systems Engineering (DIMES), University of Calabria. The basic project was born as a research program in marine robotics with multiple applications, either in the sea or in lake/river environments, for data monitoring, search and rescue operations and diver support tasks. Mechanical and hardware designs are discussed by considering a three-degree-of-freedom (3DoF) dynamical model of the vehicle. An extension to the typical guidance, navigation, and control (GNC) software architecture is presented. The software design and the implementation of a manager module (M-GNC architecture) that allows the vehicle to autonomously coordinate missions are described. Indeed, autonomous guidance and movement are only one of several more complex tasks that mobile robots have to perform in a real scenario and that allow a long-term life cycle. Module-based software architecture is developed by using the Robot Operating System (ROS) framework that is suitable for different kinds of autonomous vehicles, such as aerial, ground, surface or underwater drones.

Preface

The International Conference on Control Theory and Applications 2022 (ICoCTA 2022), organized by the Hong Kong Society of Robotics and Automation (HKSRA), was held virtually (originally scheduled in Chengdu, China) in June 10-12, 2022. ICoCTA 2022 aims at bringing together researchers, engineers, scientists and industry professionals in a unique platform and present their stimulating research and knowledge transfer ideas in control theory and applications.Control theory and engineering have witnessed dramatic achievements, which have made possible space travel and communication satellites, have assisted in the design of safe and efficient aircraft, ships, trains, and cars, have helped in developing cleaner chemical processes while addressing environmental concerns.The conference included two full days of technical sessions consisting of keynote lectures, oral presentations, and poster presentations. 13 papers were selected from 28 submissions after peer review, and all the authors participated in ICoCTA 2022 with presentations and poster. In the keynote speech session, we were greatly honor to have invited Professor Romeo Ortega, IEEE Fellow, Full Professor at ITAM, Mexico; Professor Petros Ioannou, A.V. ‘BAL’ Chair Professor, University of Southern California; Prof.YangQuan Chen, University of California, Merced; Professor Anthony G Cohn, University of Leeds, UK; Prof. Yang Shi, Department of Mechanical Engineering, University of Victoria, Victoria, Canada; Professor Hyo-Sung Ahn, Distributed Control & Autonomous Systems Lab. (DCASL), School of Mechanical Engineering, Gwangju Institute of Science and Technology (GIST), Korea; Professor Ioannis Pitas, Department of Informatics, University of Thessaloniki, Greece for addressing impressive and insightful speeches to the audience.List of ICoCTA 2022 Committees are available in this pdf.

Pairwise registration for terrestrial laser scanner point clouds based on the covariance matrix

ABSTRACT Automatic pairwise registration for unordered terrestrial laser scanner (TLS) point clouds is the pre-requisite for many applications, including 3D model reconstruction, cultural heritage management and landslide monitoring. However, most of the existing registration methods are still suffering from several limitations: (1) difficult to find the correct corresponding point pairs from the source and target point cloud, (2) low accuracy of pairwise registration and (3) the iterative process of registration is time-consuming. To overcome these challenges, based on the covariance matrix, this paper presents a robust and descriptive feature descriptor vector (FDV) to locally describe a point, by which the corresponding point pairs are obtained and the registration matrix is calculated. First, the FDVs of the original point cloud’s keypoints are calculated. Second, the corresponding point pairs are found via their FDVs. Third, the coarse transformation matrix is calculated by the corresponding point pairs using the singular value decomposition (SVD) algorithm, and which is further refined by the iterative closest point (ICP) algorithm to get a better result. Finally, the experiments are conducted on the Autonomous Systems Lab (ASL) and ISPRS datasets; our results show that the proposed algorithm can obtain good registration performance, and outperforms the compared methods.

Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.

Experimental characterisation of eye-tracking sensors for adaptive human-machine systems

Adaptive Human-Machine Interfaces and Interactions (HMI2) are closed-loop cyber-physical systems comprising a network of sensors measuring human, environmental and mission parameters, in conjunction with suitable software for adapting the HMI2 (command, control and display functions) in response to these real-time measurements. Cognitive HMI2 are a particular subclass of these systems, which support dynamic HMI2 adaptations based on the user’s cognitive state. These states are estimated in real-time using various neuro-physiological parameters from gaze, cardiorespiratory and brain signals, which are processed by an Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the accuracy and precision of neuro-physiological measurements are affected by a variety of environmental factors and therefore need to be accurately characterised prior to operational use. This paper describes the characterisation activities performed on two types of eye tracking devices used in the Aerospace Intelligent and Autonomous Systems (AIAS) laboratory of RMIT University to support the development of cognitive human-machine systems. The uncertainty associated with the ANFIS outputs is quantified by propagating the uncertainties in the input data (determined experimentally) through the inference engine. This process is of growing relevance because similar machine learning techniques are now being developed for an increasing number of applications including aerospace, transport, biomedical and defence cyber-physical systems.

Research work on cloth recognition and manipulation in the Autonomous Intelligence and Systems (AIS) laboratory at Shinshu University

Research work on cloth recognition and manipulation in the Autonomous Intelligence and Systems (AIS) laboratory at Shinshu University

Comments on Aur’s “From Neuroelectrodynamics to Thinking Machines”

In [1] Dorian Aur heatedly criticizes what he calls the ‘‘current neurophysiological doctrine,’’ which relies on the measurement of neural events on a millisecond time scale, that is, spikes or action potential. Aur’s intention is no other than to terminate one of the most fundamental ideas in neuroscience since the pioneering work of Edgard Adrian in the 20’s, the functional relevance of these nerve impulses as carriers of information. In Aur’s view, the spike timing and other related forms of neural coding expressed in terms of temporal observables are no more than epiphenomena. The principles of neural computation must be found in the spatial distribution of electrical processes that occur during the action potential. Thus, ‘‘current mainstream provides a weak understanding of computations performed in the brain,’’ because it ignores the ‘‘hidden information’’ embedded in the complex microscopic interactions inside the cell, during the millisecond time frame of a spike. Thus, ‘‘intrinsic computational processes’’ are decided at a much slower time scale and smaller space scale than is commonly assumed in neurophysiology. Neuroelectrodynamics (NED), a new theoretical framework that borrows from Hamiltonian mechanics, Thermodynamics, Quantum Physics and non-Turing computation, is surmised as the ‘‘change in paradigm required’’ to understand ‘‘brain language.’’ This review highlights the methodological pitfalls and conceptual errors introduced in the model suggested by Aur. First, it is shown that the mathematical equations proposed are not adequate for the studied system, that is, the brain, and second, a discussion on the aftermath of the dismissal of spike trains as carriers of relevant information, as stated by Aur, is sketched. First, with regard to the methodological aspects, Aur makes a claim for ‘‘adequate techniques’’ in order to understand ‘‘the neuron’s language.’’ For Aur, the diversity found in actual recording of action potential propagation in nerve cells needs to be explained in terms of the spatial distribution of electrical charges inside the neuron. The spike directivity vector is presented as the tool put on place to reveal the hidden information laying in the intracellular interactions inside the cell. Thus, while mainstream neurophysiology assumes that it is the timing of the spike that matters, Aur announces a new approach to understand neural computation, up to now unperceived by neurophysiologists, in which meaningful patterns are built upon spike directivity vectors that quantify transient charge density taking place during action potential. The methodological implications of Aur’s approach are of a devastating complexity, owing to the stratospheric dimensionality of the neuron models needed to capture the dynamics of ions, molecules and proteins inside every single cell. It is hard to imagine how one may come to grips with the dynamics of such a gargantuan system. There are millions of proteins inside each neuron! Surprisingly enough, Aur’s bet is Hamiltonian mechanics, which is mainly geometry in phase space [2]. Although Aur’s modeling choice is entirely legitimate, the actual model proposed does not apply neither aims at the physical reality for which claims to be conceived, the brain. It goes without saying that a model is always a simplified description of some features of a system, for example point neuron models are simplifications unable to simulate J. Gomez-Ramirez (&) Autonomous Systems Laboratory, Universidad Politecnica de Madrid, Jose Gutierrez Abascal, 2, 28006 Madrid, Spain e-mail: jd.gomez@upm.es

A Discussion with Roland Siegwart [Turning Point

The following is the interview by IEEE Robotics & Automation Magazine (RAM) Editor-in-Chief Peter I. Corke (PIC). Comments and suggestions for future interviews are welcome. In this issue, I am talking to Roland Siegwart (RS), the director of the Autonomous Systems Laboratory and Vice President Research and Corporate Relations, ETH Zurich.

Flying solo and solar to Mars

This paper describes the work done by the Autonomous Systems Lab of Ecole Polytechnique Federale de Lausanne (EPFL) on the global design of an unmanned solar airplane that can fly continuously over days and night. Part of the Sky-Sailor Project, a first prototype was designed and demonstrated to have very good flight characteristics. The prototype achieved an autonomous flight of five hours with its energy only from the sun. A lightweight control and navigation system was also designed for the aircraft

시험용 자율 무인 잠수정, ODIN-III의 새로운 시스템 소프트웨어 구조의 설계와 구현 및 실험

As underwater robotic vehicles (URVs) become attractive for more sophisticated underwater tasks, the demand of high performance in terms of accuracy and dexterity has been increased. An autonomous underwater robotic vehicle, ODIN (Omni-Directional Intelligent Navigator) was designed and built at the Autonomous Systems Laboratory of the University of Hawaii in 1991. Since 1991, various studies were conducted on ODIN and have contributed to the advancement in underwater robotics. Its refurbished model ODIN II was based on VxWorks in VMEbus. Recently, ODIN was born again as a PC based system, ODIN III with unique features such as new vehicle system software architecture with an objective-oriented concept, a graphical user interface, and an independent and modular structure using a Dynamic Linking Library (DLL) based on the Windows operating system. ODIN III software architecture offers an ideal environment where various studies for advanced URV technology can be conducted. This paper describes software architecture of ODIN III and presents initial experimental results of fine motion control on ODIN III.

Real-Time Control Architecture for Sauvim

This paper provides an overview of hardware and software architecture of SAUVIM (Semi-Autonomous Underwater Vehicle for Intervention Missions) which has been developed at the Autonomous Systems Laboratory (ASL). Unlike other autonomous underwater vehicles (AUVs), SAUVIM is equipped with an electric motor-driven dexterous robotic manipulator for underwater tasks requiring physical contacts with underwater objects. The overall vehicle-manipulator system becomes a high degrees-of-freedom multi-body nonlinear dynamic system. To effectively control this complex robotic system, distributed real-time hardware architecture and modified hierarchical software architecture with Sensor Data Bus (SDB) were used. The SDB makes it possible to get a prompt response from high-level control with respect to low-level sensor data. SAUVIM Task Description Language (STDL) was also developed to increase the flexibility of the vehicle tasks. Using STDL, any modification of control software is not required for various tasks. The user can also easily describe a task with various sensor data and handle multiple actuators.

Advanced Underwater Robot Research at Autonomous Systems Laboratory

As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Underwater robotics represent a fast growing research area and promising industry as advanced technologies in various subsystems develop and potential application areasare explored. Great efforts have been made in developing autonomous underwater robots or vehicles (AUVs) to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. With the development of new materials, advanced computing and sensorytechnology, as well as theoretical advancements, R & D activities in the AUV community have increased. This paper describes advanced underwater robot research activities at the Autonomous Systems Laboratory

Experimental study on a learning control system with bound estimation for underwater robots

Underwater robotic vehicles (URVs) have become an important tool for numerous underwater tasks due to their greater speed, endurance, depth capability, and a higher factor of safety than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. Conventional proportional-integral-derivative (PID) type controllers cannot provide good performance without fine-tuning the controller gains and may fail for sudden changes in the vehicle dynamics and its environment. Conventional adaptive control systems based on parameter adaptation techniques also fail in the presence of unmodeled dynamics. This paper describes a new vehicle control system using the bound estimation techniques, capable of learning, and adapting to changes in the vehicle dynamics and parameters. The control system was extensively “wet-tested” on the Omni-Directional Intelligent Navigator (ODIN)-a six degree-of-freedom, experimental underwater vehicle developed at the Autonomous Systems Laboratory, and its performance was compared with the performance of a conventional linear control system. The results showed the controller's ability to provide good performance in the presence of unpredictable changes in the vehicle dynamics and its environment, and it's capabilities of learning and adapting.

Development of the Omni Directional Intelligent Navigator

The Autonomous Systems Laboratory is in the midst of developing an advanced underwater robotic technology test platform. The platform consists of the Omni-Directional Intelligent Navigator (ODIN) and the Integrated Graphic Workstation (IGW). ODIN is a six degree-of-freedom (dof) underwater vehicle with dual operational modes (autonomous and tethered) and a single dof mechanical manipulator. IGW is a real-time, 3-dimensional graphic monitoring, testing, and evaluation workstation. This paper presents ODIN's mechanical and electrical specifications; its vehicle dynamics and depth control system; its recent simulation and experimental results; and IGW's specifications. >