Showing papers on "Autonomous system (mathematics) published in 2021"

Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Abstract: With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

NTU VIRAL: A visual-inertial-ranging-lidar dataset, from an aerial vehicle viewpoint

Abstract: In recent years, autonomous robots have become ubiquitous in research and daily life. Among many factors, public datasets play an important role in the progress of this field, as they waive the tal...

Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

Abstract: The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference.

A human-like decision intelligence for obstacle avoidance in autonomous vehicle parking

Abstract: The autonomous vehicle parking problem has drawn increased attention in recent times. It can resolve the parking-related issues that involve minimizing human driving errors and saving fuel and time. In practice, parallel parking needs extra care when dynamicity is present in the environment. Many human drivers have the expertise to take quick action when obstacles appear on their driving path. An autonomous system should possess such intelligence that can mimic human-like behavior in the presence of obstacles. This work focuses on providing an intelligent autonomous parking design using a car-like mobile robot (CLMR) that efficiently parks the vehicle in dynamic environmental conditions. A novel fuzzy-based obstacle avoidance controller is proposed that integrates sensor information into the parking problem, obtained from the surrounding of a CLMR. Ultrasonic sensors’ arrangement and their grouping provide inputs for the fuzzy system. A fuzzy-based obstacle avoidance controller can execute intelligent parking like a human by avoiding static as well as moving obstacles. The proposed work is tested in different challenging environmental conditions. Simulation results demonstrate that the proposed algorithm accomplishes autonomous parallel parking reasonably well in the presence of static and moving obstacles and can be used to park the vehicle in real-time. The proposed work helps to solve the autonomous parking problem with safety, especially in dynamic environmental conditions.

A study of users’ preferences after a brief exposure in a Shared Autonomous Vehicle (SAV)

Abstract: Shared Autonomous Vehicles are expected to significantly change transport and mobility, improving road safety, environmental impact and traffic efficiency. However, the successful implementation of a SAV mobility service strongly depends on public acceptance and adoption, which might be influenced by a number of factors, such as socio-demographic characteristics of potential users, and their expectations and perceptions towards the autonomous system. This study presents the results of a novel experiment carried out in a non-simulated environment, to explore car users’ preferences towards autonomous mobility options. Participants took part in a stated preference task before and after a brief exposure in a Shared Autonomous Vehicle. Interestingly, results show that the experience influenced people’s mode choice preferences, moving from car (the most preferred mode before the experiment) to autonomous taxi and shared autonomous taxi (after the experiment). The study and the results of the structural equation and the choice model also highlighted the importance of comfort in people’s preferences towards shared autonomous options.

Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis

Abstract: Autonomous Vehicles have the potential to change the urban transport scenario. However, to be able to safely navigate autonomously they need to deal with faults that its components are subject to. Therefore, Health Monitoring System is a essential component of the autonomous system, since allows Fault Detection and Diagnosis. In addition, Prognosis System is also important, since it allows predictive maintenance and safer decisions during vehicle navigation. This paper presents a Hierarchical Component-based Health Monitoring System with Fault Detection, Diagnosis and Prognosis using Dynamic Bayesian Network (DBN) with residue generation, a combination of knowledge-based and model-based detection, diagnosis and prognosis approaches. We evaluate the proposed Dynamic Bayesian Network using different machine learning metrics and a dataset with sensor readings gathered using the CaRINA II autonomous vehicle platform, and the CARLA simulator. Both simulated and experimental results demonstrated a positive performance of the DBNs even with high rate of missing data for some of the model’s variables.

LMSRE-Based Adaptive PI Controller for Enhancing the Performance of an Autonomous Operation of Microgrids

Abstract: Microgrids take a large part in power networks thanks to their operational and economic benefits. This research introduces a novel implementation of an adaptive proportional plus integral (PI) controller to boost the autonomous microgrid operation efficiency. The least mean and square roots of the exponential algorithm are utilized in the adaptive PI control strategy. The multi-objective function for both sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms is obtained by The Response Surface Methodology. The system is evaluated under different environments, which are stated as follows: 1) disconnect the system from the grid (islanding), 2) autonomous system exposure to load variability, and 3) autonomous system exposure to a symmetrical fault. The proposed practicality of the control plan is shown by the data of the simulation, which is extracted from PSCAD/EMTDC software. The strength of the suggested adaptive control is confirmed through matching its results with those obtained using the SFO and PSO based optimal PI controllers.

Deep CNN-based autonomous system for safety measures in logistics transportation

Abstract: The careless activity of drivers in logistics transportation is a primary reason inside the vehicle during road accidents. This research aims to reduce the number of accidents caused by a failure of the driver in logistics transportation by incorporating an autonomous system. We propose a convolutional neural network -based architecture to recognize and classify different positions which cause road accidents. The proposed system is evaluated with the State Farm Distracted Driver Database, which included examples illustrating ten different driving positions like reaching behind and talking to the passenger, making up, safe driving, talking on the phone, clothing, checking right/left hand, right/left hand, and running the radio. The proposed approach has also been tested against recent algorithms and evaluated. Our model has obtained 98.98% accuracy compared to other types of approaches with different descriptors and classification techniques

Heterogeneous multistability in a novel system with purely nonlinear terms

Abstract: In this paper, the dynamic properties of the new 3-Dimensional autonomous system without linear terms are investigated. The nonlinear features of the explored model are highlighted in terms of bifu...

Assistive technology: autonomous wheelchair in obstacle-ridden environment.

Abstract: The benefits for the advancement and enhancement of assistive technology are manifold. However, improving accessibility for persons with disabilities (PWD) to ensure their social and economic inclusion makes up one of the major ones in recent times. This paper presents a set of new nonlinear time-invariant stabilizing controllers for safe navigation of an autonomous nonholonomic rear-wheel drive wheelchair. Autonomous wheelchairs belong to the category of assistive technology, which is most sought in current times due to its usefulness, especially to the less abled (physically and/or cognitively), hence helping create an inclusive society. The wheelchair navigates in an obstacle-ridden environment from its start to final configuration, maintaining a robust obstacle avoidance scheme and observing system restrictions and dynamics. The velocity-based controllers are extracted from a Lyapunov function, the total potentials designed using the Lyapunov based Control Scheme (LbCS) falling under the classical approach of the artificial potential field method. The interplay of the three central pillars of LbCS, which are safety, shortness, and smoothest course for motion planning, results in cost and time effectiveness and the velocity controllers' efficiency. Using the Direct Method of Lyapunov, the stability of the wheelchair system has been proved. Finally, computer simulations illustrate the effectiveness of the set of new controllers.

Fuzzy-PI double-layer stability control of an online vision-based tracking system

Abstract: In many manufacturing processes (e.g., welding, spraying, coating adhesive), the control for the velocity in the main direction heavily affects operation quality. In addition to traditional manual operations, the industrial robot with a tracking system is capable of accurate and stable velocity control. In this paper, an intelligent robot tracking system is designed for implementing an appropriate velocity control and improving the performance of an autonomous system with online structured light vision tracking. For this aim, an effective tracking algorithm is proposed based on position-based visual servoing (PBVS), and motion compensation is implemented according to both detected path and taught path. To improve the adaptability of the system, a Fuzzy-PI double-layer controller is developed, which adjusts the movement of the end effector in both cases of large and small deviation. Welding experiments demonstrate the effectiveness of the proposed vision tracking system.

Chronos: Timing Interference as a New Attack Vector on Autonomous Cyber-physical Systems

Abstract: Timing property plays a vital role in the Cyber-Physical System(CPS) due to its interaction with the physical world. The smooth operation of these robotic systems often relies on an accurate and timely perception and actuation of the physical world. In this poster, we demonstrated a unique new class of attack, Chronos, that exploits timing interference to cause system destabilization in cyber-physical systems. Using a compromised non-privileged non-critical task on the system, we launch timing interference attacks on both drone and autonomous vehicle platforms. Through both open-loop and close-loop testing on the end-to-end stack, we showed that the timing attack could lead to complete loss of control of the autonomous system, crashing them onto the surroundings when there is no software vulnerability. To further understand this novel attack vector, we perform preliminary investigations on the localization component of these two platforms, because they both make use of well-known simultaneous localization and mapping (SLAM) algorithms that depend on timing-sensitive multimodal data from different sensors. Building on the insights from the case study, we present our formulation of the timing attack surface and highlight future directions.

Precision Landing for Low-Maintenance Remote Operations with UAVs

Abstract: This work proposes a fully integrated ecosystem composed of three main components with a complex goal: to implement an autonomous system with a UAV requiring little to no maintenance and capable of flying autonomously. For this goal, was developed an autonomous UAV, an online platform capable of its management and a landing platform to enclose and charge the UAV after flights. Furthermore, a precision landing algorithm ensures no need for human intervention for long-term operations.

Development of an Autonomous Robotics Platform for Road Marks Painting Using Laser Simulator and Sensor Fusion Technique

Abstract: The design and experimental works of an autonomous robotic platform for road marks painting are presented in this paper as the first autonomous system of its kind. The whole system involves two main sub-systems, namely: an autonomous mobile robot navigation system which is used for recognizing the roads and estimating the position of road marks, and automatic road marks painting system that is attached to the mobile robot platform to control the spray of the paint on the road’s surface. The experimental results show the capability of the proposed system to perform the task of autonomous road marks painting with accuracy of ±10 cm.

Comparing driving behavior of humans and autonomous driving in a professional racing simulator.

Abstract: Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data In the first study, the participants' task was to achieve the fastest lap while keeping the car on the track We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car We investigated how the features used to predict driving performance in humans can be used in autonomous driving Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches Conversely, they can also be applied to training (professional) drivers to improve their racing line

An Optimized Collision Avoidance Decision-Making System for Autonomous Ships under Human-Machine Cooperation Situations

Abstract: Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA.

Continuous Dependence on Parameters and Boundedness of Solutions to a Hysteresis System

Abstract: We analyze an ordinary differential system with a hysteresis-relay nonlinearity in two cases when the system is autonomous or nonautonomous. Sufficient conditions for both the continuous dependence on the system parameters and the boundedness of the solutions to the system are obtained. We give a supporting example for the autonomous system.

Attack Detection and Countermeasures for Autonomous Navigation

Abstract: Advances in artificial intelligence, machine learning, and robotics have profoundly impacted the field of autonomous navigation and driving. However, sensor spoofing attacks can compromise critical components and the control mechanisms of mobile robots. Therefore, understanding vulnerabilities in autonomous driving and developing countermeasures remains imperative for the safety of unmanned vehicles. Hence, we demonstrate cross-validation techniques for detecting spoofing attacks on the sensor data in autonomous driving in this work. First, we discuss how visual and inertial odometry (VIO) algorithms can provide a root-of-trust during navigation. Then, we develop examples for sensor data spoofing attacks using the open-source driving dataset. Next, we design an attack detection technique using VIO algorithms that cross-validates the navigation parameters using the IMU and the visual data. Following, we consider hardware-dependent attack survival mechanisms that support an autonomous system during an attack. Finally, we also provide an example of spoofing survival technique using on-board hardware oscillators. Our work demonstrates the applicability of classical mobile robotics algorithms and hardware security primitives in defending autonomous vehicles from targeted cyber attacks.

Gripper Design for Radio Base Station Autonomous Maintenance System

Abstract: For an autonomous system to perform maintenance tasks in a networking device or a radio base station (RBS), it has to deal with a series of technological challenges ranging from identifying hardware-related problems to manipulating connectors. This paper describes the development of a robot maintenance system dedicated to detect and resolve faulty links caused by unplugged or poorly connected cables. Although the maintenance system relies on four subsystems, we significantly focus on our low-cost and efficient custom gripper solution developed to handle RJ45 Ethernet connectors. To examine our gripper, we conducted three experiments. First, a qualitative questionnaire was submitted to 30 users in the case of the teleoperated scenario of the gripper attached to a robotic arm. Similarly, we also tested the automatic operation mode. The results showed that our system is reliable and delivers a highly efficient maintenance tool in both teleoperated and autonomous operation modes. The practical experiment containing the removal or unplugging of connectors demonstrated our gripper′s ability to adequately handle these, whereas the feedback from the questionnaire pointed to a positive user experience. The automatic test assessed the gripper′s robustness against the continuous operation.

Autonomous Surface Vessel with Remote Human on the Loop: System Design for STCW Compliance

Abstract: Autonomous surface vessels comprise complex automated systems with advanced onboard sensors. These help establish situation awareness and perform many of the complex tasks required for safe navigation. However, situations occur that require assistance by a human proxy. If not physically present on board, information digestion and sharing between human and machine become crucial to maintain safe operation. This paper addresses the co-design of on-board systems and a Remote Control Centre (RCC). Using the international regulations on watch-keeping (STCW) as a basis, the paper discuss how an autonomous system is designed to meet the STCW requirements. It is discussed how the autonomous system is made aware of the state of the vessel, its surroundings, on-board defects or navigational challenges and shared with the RCC in a collaborating system perspective.

A Novel High Gain Array Antenna Design for Autonomous Vehicles of 6G Wireless Systems

Abstract: By 2030, 6G wireless networks will provide a fully autonomous system for many applications, one of them is fully automated vehicles. Antennas with different gains, half-power beamwidths (HPBW), and different radiation zones will be needed for different purposes such as parking assistance, lane change, emergency braking, and traffic jam assist in autonomous vehicles with integrated RADAR technologies. The Federal Communication Commission (FCC) has proposed millimeter-wave (mmW) frequency bands for vehicular RADAR systems. This paper describes the development of a new and improved 2x4 array antenna for autonomous vehicles. Development of proto-type single patch for the array is described in [1]. The array antenna resonates at7 7 GHz frequency. With a higher gain, the optimized array antenna has a lower mutual coupling effect. The gain of the 2x4 corporate feed array antenna is 18.0 dB. The return loss was calculated to be -39.8947 decibels. The array antenna designed and described in the paper can be used in autonomous vehicles.

Autonomous Driving Cars: Decision-Making

Abstract: This study is an overview of autonomous cars and also the challenges faced in the field of autonomous cars Recent advances are being made in the field of planning, perception, and decision-making for autonomous vehicles This has led to great improvements in functional capabilities; several prototypes are already on roads The challenge is to obtain the safe execution of vehicles in all driving situations In this study, we will see recent methodologies for vehicle control using kinematic and dynamic model, collaborative autonomy, decision-making system for autonomous vehicles using convolutional neural network, path planning for autonomous vehicles using model predictive control, real-time decision-making for autonomous city vehicle using world model and driving maneuver system, and intention-aware autonomous driving decision-making using hidden Markov model and partially observable Markov decision process

An Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

Abstract: This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle.

Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester

Abstract: This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization within the given map. A planning algorithm searches for both an approach pose and a path in a single path planning problem. We design a path following controller leveraging the legged harvester's capabilities for negotiating rough terrain. Upon reaching the approach pose, the machine grabs a tree with a general-purpose gripper. This process repeats for all the trees selected by the operator. Our system has been tested on a testing field with tree trunks and in a natural forest. To the best of our knowledge, this is the first time this level of autonomy has been shown on a full-size hydraulic machine operating in a realistic environment.

Solution of the Problem of Optimizing Route with Using the Risk Criterion

Abstract: The aim of the work is to determine the conditions of optimality in the task of plotting the course of the vessel and the operation of divergence of vessels in conditions of intensive navigation. The need for such work is dictated, firstly, by an increase in the intensity of shipping and, secondly, by the emergence of autonomous ships and transport systems, the traffic control algorithms of which obviously require an optimal approach. The criterion of optimality in problems of this class is the expected risk, one of the components of which is the risk of collision of ships. Based on the analysis of methods for constructing ship divergence algorithms, the task is to find a control algorithm that delivers the best results for all participants in the operation. This formulation of the task greatly facilitates the forecast of the actions of all participants in the discrepancy and is especially expedient in the case of participation in the operation of an autonomous system or a ship with which no contact has been established. Theoretically, the task belongs to the most difficult class of control problems - optimal control of a distributed dynamic system with a vector - a goal functional [3, 5, 8, 13, 14, 15]. The ability to obtain a general solution to the task of optimal ship control makes this study expedient.

Autonomous Robot Planning System for In-Space Assembly of Reconfigurable Structures

Abstract: Large-scale space structures, such as telescopes or spacecrafts, require suitable in-situ assembly technologies in order to overcome the limitations on payload size and mass of current launch vehicles. In many application scenarios, manual assembly by astronauts is either highly cost-inefficient or not feasible at all due to orbital constraints. However, (semi-) autonomous robotic assembly systems may provide the means to construct larger structures in space in the near future. Modularity is a key concept for such structures, and also for reducing costs in novel spacecraft designs. The advantage of the modular approach lies in the capability to generate a high number of unique assets from a reduced number of building blocks. Thus, spacecrafts can be easily adapted to particular use cases, and could even be reconfigured during their lifetime using a robotic manipulation system. These ideas lie at the core of our current EU project MOSAR (MOdular Spacecraft Assembly and Reconfiguration). Teleoperating a space robotic system from Earth to assemble $a$ modular structure is not straightforward. Major difficulties are related to time delays, communication losses, limited control modalities, and low immersion for the operator. Autonomous robotic operations are then preferred, and with this goal we propose $a$ fully autonomous system for planning in-space assembly tasks. Our system is able to generate assembly and reconfiguration plans for modular structures in terms of high-level actions that can autonomously be executed by $a$ robot. Through multiple simulation layers, the system automatically verifies the feasibility and correctness of action sequences created by the planner. The layers implement different levels of abstraction, hierarchically stacked to detect infeasible transitions and initiate replanning at an early stage. Levels of abstraction increase in complexity, ranging from a basic geometric description of the spacecraft, over kinematics of the robotic setup, to full representations of the actions. The system reuses information from failed checks in all layers to avoid similar situations during replanning. We use a hybrid approach where symbolic reasoning is combined with considerations of physical constraints to generate a holistic sequence of actions. We demonstrate our planner for large space structures in a simulation environment. In particular, we consider the reconfiguration of a given modular structure, i.e. disassemble parts and reassemble them in a new configuration. The adaptability of our planning system is shown by executing the assembly plans on robots with different sets of skills and in scenarios with simulated hardware failures.