Browse Topic: Autonomous vehicles
ABSTRACT Over time, the National Institute of Standards and Technology (NIST) has refined the 4Dimension / Real-time Control System (4D/RCS) architecture for use in Unmanned Ground Vehicles (UGVs). This architecture, when applied to a fully autonomous vehicle designed for missions in urban environments, can greatly assist in the process of saving time and lives by creating a more intelligent vehicle that acts in a safer and more efficient manner. Southwest Research Institute (SwRI®) has undertaken the Southwest Safe Transport Initiative (SSTI) aimed at investigating the development and commercialization of vehicle autonomy as well as vehicle-based telemetry systems to improve active safety systems and autonomy. This paper will discuss the implementation of the 4D/RCS architecture to the SSTI autonomous vehicle, a 2006 Ford Explorer.
SCOPE IS UNAVAILABLE.
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the accuracy and reliability of atmospheric visibility estimation, which is crucial for improving safety in applications such as aviation, maritime navigation, and autonomous vehicles. By addressing challenges such as data variability, environmental factors, and the inherent complexity of atmospheric conditions, MAVEN contributes to more reliable and robust visibility estimation systems, thereby enhancing safety and operational efficiency in critical environments.
The recent discovery of glacier remains in Noctis Labyrinthus, the "Maze of the Night" near Mars' equator sheds new light on the history of water on Mars, the evolution of the planet’s climate and geology, and the possibility of life. It also opens the possibility for massive amounts of clean glacier ice to be accessed by astronauts at low latitudes on Mars, alleviating the need to operate in more frigid higher latitudes. Further reconnaissance of the site requires a robotic vehicle capable of traversing rough, salt-crusted glacier surfaces and leaping across crevasse fields. To address this need, we propose a conceptual hybrid aerial/ground vehicle, LILI (Long-term Ice-field Levitating Investigator). LILI combines episodic rotary-wing flight with ground mobility as a propeller-driven sled through an arrangement of skis/runners, wheels, and tilting proprotors. A high-level look at the Noctis Labyrinthus "relict glacier" site is presented, along with a notional LILI mission traverse concept designed to ensure critical scientific measurements are captured. The NASA Design and Analysis of Rotorcraft (NDARC) software is utilized to ensure that mission requirements and sizing constraints are met. Furthermore, future work considers guidance, navigation, and control requirements to satisfy mission objectives, and an initial construction for a simplified LILI small-scale prototype.
Fault detection in autonomous VTOL aircraft is critical because even minor degradations can quickly destabilize multirotor vehicles in safety-critical environments. However, real-flight fault detection remains challenging due to sensor noise, environmental disturbances, and the nonlinear aeromechanics of multirotor platforms. This study proposes a comprehensive machine-learning framework for rotor fault detection, isolation, and severity prediction using real flight data. A convolutional neural network (CNN) architecture is developed to learn spatio-temporal patterns from multivariate flight dynamics, enabling direct inference of both the faulty rotor and its damage level. The framework is first validated using simulated data generated by our in-house flight dynamic model. Next, to verify the framework using real flight data, a hexcopter was designed, fabricated and flight tested for both nominal and faulty cases by introducing controlled blade-tip breakage. The trained model achieves rotor-wise fault classification accuracies above 99% and sample-wise severity estimation accuracy of 96% within a ±1% tolerance in experimental data, demonstrating strong generalization and supporting real-time health monitoring for autonomous VTOL systems.
A new motion platform facility was developed at the University of Maryland to study autonomous landing of vertical lift aircraft on moving ship decks. It includes a 6-by-6-ft platform of 1.5 ton payload, a Vicon system, and a quadrotor. The platform can generate high-frequency / high-amplitude stochastic motions in all six degrees of freedom, including simpler motions typical of DDG-51 class vessels at high seas. The quadrotor is custom-built in-house around a feature-based vision system for detection, tracking, and pose estimation of a moving deck. In this paper, the vision data is coupled with the flight controls to execute autonomous landings. The paper presents a preliminary assessment of the platform, the quadrotor, and the performance of the closed-loop algorithm using simple tracking and landing experiments.
The Rotor Blown Wing (RBW) is a tailsitter Vertical Takeoff and Landing (VTOL) Unmanned Aerial System (UAS) configuration that leverages cutting-edge autonomous flight controls through Sikorsky's MATRIX™ technology to create a highly capable, efficient, and scalable technology platform. By combining the benefits of fixed- and rotary-wing aircraft, the RBW configuration eliminates the need for traditional UAS launch and recovery infrastructure. This paper describes the RBW-5 prototype, a 100-pound, dual 5-foot diameter proprotor demonstrator, and discusses the comprehensive evaluation of its design and operability through a combination of flight tests, wind tunnel experiments, and computational fluid dynamics (CFD) simulations. The results demonstrate the maturity of the UAS and highlights key accomplishments of the RBW-5 program, including successful autonomous takeoff and landing and transitions between hover and forward flight, the extraction of critical "blown-physics" underlying RBW aerodynamics, and the validation of CFD models against unpowered and powered wind tunnel data.
Electric Vertical Takeoff and Landing (eVTOL) vehicles undergoing advanced air mobility (AAM) operations feature increasingly autonomous systems (IAS) with non-traditional role allocations. Ensuring the safety of these operations and their novel human–machine teaming (HMT) paradigms requires an appropriate body of knowledge created through relevant, reproducible research. In this paper, we briefly examine the meaning of teaming; current regulation, standards, and guidance; and the knowledge required to build resilient HMTs before turning our attention to how this knowledge is being created by recent research and what conclusions or recommendations can be made. We identify the need for further research into the holistic performance of HMTs, the effect of novel allocations of roles between humans and machines, the ability of humans to provide resilience to unforeseen dangers when acting as a part of these teams; and the characteristics required for clear, timely, and accurate communication between the humans and machines. This work is done in the context of eVTOL aircraft with an indirect flight control system (IFCS) undergoing urban air mobility operations.
The vertical flight industry is on its way to a transformative era, with autonomous technologies set to alter aerial vehicle operations. While it seems certain that fully autonomous helicopters will eventually be deployed for a variety of missions, some high-stakes situations—like medical evacuations (MEDEVAC)—will for the foreseeable future demand human participation in the form of Emergency Medical Care-giving Crew. This study describes the testbed built to run and investigate hypothetical future situations in which a helicopter is autonomously piloted while a human medic with no aviation training, subjected to aviation and medical emergencies, manages patient care onboard. A total of 22 participants, with emergency medical technician certification, nursing or a medical board certification, were invited to run and evaluate the use of AI pilot (AP) in different scenarios of medical evacuation under the following emergencies: medical, empty fuel tank, pressure sensor miscalibration, and engine failure. A comprehensive evaluation of both objective and subjective performance metrics revealed that novice medical professionals could effectively execute medical evacuation operations in conjunction with an AI pilot, even during unforeseen circumstances. The analysis of response times unveiled distinct perspectives on how medics perceive and manage various emergency situations when an AP functions as a collaborative and effective team member.
The National Research Council of Canada is conducting a multi-year autonomous flight systems research and technology development project entitled Advanced Autonomy Systems for Challenging ENvironments Development & Demonstration (AASCEND). As part of AASCEND a no-hover landing capability has been developed and demonstrated in a variety of environmental conditions, including in limited degraded visual environment (DVE) operations. This paper discusses the requirements for no-hover landings, their value within an Autonomous Flight System (AFS), and the implementation of this capability in the NRC's AASCEND autonomous flight system. It presents a methodology for identifying a no-hover landing envelope, taking into account the complex maneuvering required. Within that methodology a proposed set of assessment criteria for no-hover landing performance and behaviour is introduced. The paper reports on the results of applying this methodology to the AASCEND no-hover landing algorithm in a simulation study, along with associated Landing Zone Evaluation (LZE) system performance requirements.
The Dragonfly relocatable lander was selected as NASA's New Frontiers mission in 2019 to explore the organic-rich surface of Titan, Saturn's largest moon. The coaxial quadrotor vehicle will fly to multiple geologic sites covering a distance of over 50 miles near the Titan equator. At each site, Dragonfly will sample materials, determine the surface composition, and investigate how far prebiotic chemistry has progressed on Titan. Upon arrival, the lander will enter the Titan atmosphere protected inside an aeroshell, which will descend and decelerate with parachutes. At an altitude of approximately 1 km above the ground, the lander will separate from the backshell and perform a controlled transition to powered flight. Prior to separation from the backshell and after the heatshield has been ejected, the Preparation for Powered Flight (PPF) sequence will be initiated, which ensures the lander is in a safe and stable state for autonomous descent. A critical element of PPF is the de-spin maneuver, where diagonally opposing rotors rotate at maximum speed to reduce any residual angular momentum by creating a yaw moment in lander body axes. This paper presents high-fidelity computational fluid dynamics simulations of the Dragonfly rotorcraft lander during the PPF sequence. Aerodynamic performance predictions are compared with test data from the National Full-Scale Aerodynamics Complex to validate the simulations and build confidence in the PPF simulation results. Blade-resolved simulations capture the unsteady and complex flow behavior in Titan's dense, low-temperature atmospheric conditions during PPF. The results are analyzed, providing insight into aerodynamic performance and the aerodynamic moments critical for mission success.
In this work, a vision-based solution is developed to address the challenge of landing on a ship deck with precision and accuracy. For an autonomous landing, it is important to have a fast and accurate pose estimation system along with a reliable control strategy. This research uses fractal ArUCo markers instead of multiple separate markers to allow smooth pose estimation at different heights. Pose estimates are further improved using an Extended Kalman Filter, and a tracking algorithm then uses these estimates to guide the landing. A four degree-of-freedom (roll, pitch, heave and sway) simulator platform was built and used to validate the algorithm. The accuracy of the vision system is compared against that of a motion capture system. Real-world experiments were performed on different quadrotors to demonstrate tracking and landing on the platform with sway, roll, and pitch motions. The results show that the system is efficient and reliable in achieving safe and successful landings. The proposed landing system is concluded to be applicable for landings on the deck of the ship under sea-state 4.
Heavy wind and high sea states pose challenges to operating unmanned rotorcraft on-board a naval ship, in particular the recovery phase. A novel autonomous landing strategy for unmanned rotorcraft is proposed and investigated. The new landing strategy makes use of a prediction of the future deck motion based on a sensor on the ship deck. The study is based on a nonlinear simulation environment which includes the dynamics of a 100 kg unmanned helicopter and the dynamics of an ocean-going patrol vessel of the Royal Netherlands Navy. The performance of the autonomous landing strategy is evaluated for a wide variety of environmental conditions (sea state) and operational conditions (ship speed and heading). The results clearly indicate that the environmental conditions have a strong influence on the landing performance in terms of touchdown velocity and landing accuracy. Furthermore, the autonomous landing strategy is effective in reducing the mean and peak value of the touchdown velocity compared to a standard automatic landing strategy. A reduction of as much as 60% in landing impact is observed for the worst case environmental and operational condition considered. The results confirm and underline the potential of the novel landing strategy.
ABSTRACT Automatic guided vehicles (AGV) have made big inroads in the automation of assembly plants and warehouse operations. There are thousands of AGV units in operation at OEM supplier and service facilities worldwide in virtually every major manufacturing and distribution sector. Although today’s AGV systems can be reconfigured and adapted to meet changes in operation and need, their adaptability is often limited because of inadequacies in current systems. This paper describes a wireless navigated (WN) omni-directional (OD) autonomous guided vehicle (AGV) that incorporates three technical innovations that address the shortfalls. The AGV features consist of: 1) A newly developed integrated wireless navigation technology to allow rapid rerouting of navigation pathways; 2) Omnidirectional wheels to move independently in different directions; 3) Modular space frame construction to conveniently resize and reshape the AGV platform. It includes an overview of the AGVs technical features and how the flexibility and agility can be adapted to fit military and commercial application. The AGV is being evaluated as a mobile work station platform and a precise material handling robot.
ABSTRACT For many rotorcraft platforms, incorrect timing of the autorotation flare and deceleration maneuvers may result in significant aircraft damage and injury to the crew, or worse. There is a clear need for new pilot cueing and control augmentation technologies that lead to a higher probability of a successful autorotation landing. This paper describes a recent effort to develop two different Tau (time-to-contact)-based autorotation controllers that can be used to drive visual aids to help guide a pilot to apply the required control inputs to complete a safe autorotative landing. Such controllers may also be useful for fully autonomous autorotation landing for unmanned vehicles.
Through the development and flight testing of the Canadian Vertical Lift Autonomy Demonstration (CVLAD) Autonomous Flight System (AFS), the NRC has developed technical and operational insight into many high-level concepts of a full-scale supervised-autonomous helicopter, which are thought to be applicable to a wide variety of implementations and approaches. This paper presents two important concepts: "The Contract" and "Levels of Aggression", for which it is expected some aspect of implementation would be required in any supervised autonomous platform and in particular for platforms where the pilot supervising the autonomy remains on-board the aircraft (and thus the AFS provides a 'competent co-pilot' type functionality).
In this paper, we develop and validate a 3D feature-based algorithm for tracking stochastic ship-deck motion at high sea states, specifically Sea-State 6 using data from the Navy SCONE dataset. The new vision algorithm was developed from the structure-from-motion technique, which recovers the 3D structure of an object from a series of 2D images, and was validated using a simulated 3D ship-deck attached to a moving Stewart platform. Algorithm performance with different feature detectors and image resolutions was compared. In hand-held tests, the vision algorithm was demonstrated to accurately estimate the pose of a moving ship-deck using a quadrotor. Visually degraded conditions were also evaluated; the algorithm is robust to occlusion and low illumination, but performance reduces in severe glare. The vision algorithm was then validated in a simple free-flight test. All results were compared with Vicon ground-truth data. Additionally, as the 3D algorithm is computationally demanding, we develop and validate a method to improve the computational speed of the vision algorithm.
The paper presents a novel strategy for minimum energy consumption in automatic conversion control of tiltrotor eVTOL aircraft, exemplified by the Aston Martin Volante Vision model. We introduce a tilt schedule methodology that strategically balances conversion and reconversion performance with climb, descent, and cruise phases to minimize overall energy expenditure. Our approach accounts for critical factors such as blade loading, operation handling qualities, and passenger ride comfort within a predefined conversion corridor. The optimized trajectories approximate the minimum energy pathway, essential for operational efficiency in urban air mobility. Analytical results demonstrate that our proposed conversion and reconversion phase profiles significantly reduce energy consumption, contributing to the sustainability of tiltrotor flight operations. This research not only enhances understanding of tiltrotor dynamics but also serves as a pivotal step toward achieving globally optimized energy usage, marking a significant advancement in autonomous flight technology for advanced air mobility systems.
This paper presents an overview of the Autonomous Rotorcraft Project (ARP), a collaborative research initiative launched by the US Army and NASA in 2000 aimed at advancing rotorcraft autonomy. ARP has made substantial progress in areas such as real-time reactive obstacle-avoidance, threat- and terrain-aware navigation, identification of safe landing zones, autonomous flight-control, external sling-load operations, scalable autonomy, and pilot-autonomy interfaces. These advancements have undergone extensive validation through both simulation and flight test. This paper chronicles ARP's research evolution, highlighting milestones achieved and remaining challenges.
Fundamental advancements in aircraft design over the past 50 years have enabled a range of Vertical Takeoff and Landing (VTOL) air vehicle configurations and have significantly enhanced aircraft performance, safety, and reliability. This summary paper chronicles the evolution of rotorcraft design from 1974-2024 as presented by the Vertical Flight Society (VFS) Aircraft Design Technical Committee (TC). It is segregated into three key pillars of aircraft design preceded by an aircraft design overview. The three pillars are: processes and tools, technology, and air vehicle configuration. The first pillar on design processes and tools describes advancements in technology, methodologies, and computational capabilities such as the transition from design solely by wind tunnel testing, physical models, and hand calculations to computer aided design/synthesis and simulation in a model-based engineering digital-twin environment. The second pillar on technology focuses on advances in disciplinary technologies and how they have been incorporated into aircraft design. Technologies discussed include composite materials; rotor systems; propulsion systems, ranging from advancements in turbine engines to all- and more-electric propulsion technologies utilizing various energy storage systems to convertible engines; fly-by-wire systems; avionics and cockpit architectures, including digital displays, navigation aids, and communication equipment; and autonomous and semi-autonomous systems. The third pillar on air vehicle configuration focuses on platform architectures. Major architectures discussed are high-speed VTOLs such as the thrust-vectored aircraft, tiltrotors, lift/thrust compounded helicopters and all- and more-electric aircraft.
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In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing, and training a neural network, following which the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on the go based on the predicted steering angle and other parameters such as the actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed the computational efficiency of the pipeline and evaluated the robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.
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