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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

FPV Video Adaptation for UAV Collision Avoidance

First person view (FPV) technology for unmanned aerial vehicles (UAVs) provides an immersive experience for pilots and enables various personal and commercial applications such as aerial photography, drone racing, search and rescue operations, agricultural surveillance, and structural inspection. While real time video streaming from a UAV and vision-based collision avoidance strategies have been studied in literature as separate topics, in this paper we tackle collision avoidance in FPV scenarios, taking into account network delays and real time video parameters. We present a theoretical model for obstacle collisions that considers the current communication channel conditions, the real time video parameters, and the UAV's position relative to the closest obstacle. A video adaptation algorithm is then designed, using this metric, to tune the FPV video resolution, number of re-transmission attempts, and the modulation scheme to maximize the probability of avoiding collisions. This algorithm also takes into account specific latency constraints of the application. This video algorithm was evaluated in various scenarios and its ability to respond to both distances to the obstacle as well as the communication channel conditions was demonstrated. It was found that, for the considered scenarios, the performance of the proposed adaptive algorithm was, on an average, 58.63% higher than the closest non-adaptive one in terms of maximizing the probability of avoiding collision. Such collision avoidance strategies could be used to make UAV FPV applications safer and more reliable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Multi-agent motion planning with sporadic communications for collision avoidance

Here, a novel multi-vehicle motion planning and collision avoidance algorithm is proposed and analyzed. The algorithm aims to reduce the amount of onboard calculations and inter-agent communications needed for each vehicle to successfully navigate through an environment with static obstacles and reach their goals. To this end, each agent first calculates a path to the goal by means of an asymptotically optimal rapidly-exploring random tree (RRT*) with respect to the static obstacles. Then, other agents are treated as dynamic obstacles and potential collisions are determined by means of collision cones. Collision cones depend on the position and velocity from other agents and are grown conservatively between inter-agent communications. Based on the available information, each agent determines if a deconfliction maneuver is needed, if it can continue along its current path, or if communication is needed to make a decision about a conflict. With probability one, our algorithm guarantees that the agents keep from colliding with each other. Under an assumption on the existence of a solution for a vehicle to its goal, this algorithm also solves the planning problem with probability one. Simulations illustrate a group of agents successfully reaching their goal configurations and examine how the uncertainty affects the communication frequency of the multi-agent system.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Drone Video Platform—Collision Avoidance, Situational Awareness, and Communications

Small unmanned aerial systems are ideal for delivering sensors into areas that are inaccessible or too dangerous for human entry or manned aircraft over-flights. The systems can fly low and slow for enhanced sensitivity and in tight spaces where ordinary aircraft could never fit. We have configured and flown sensor payloads for radiation, chemical, and optical detection on small fixed-wing and rotary-wing aircraft. All the sensor packages provide telemetry, GPS coordinates, LIDAR distance ranging, and absolute time of day to microsecond accuracy. Payload weights range from less than 1 kg up to 5.5 kg. Currently, communications are over 2.4 GHz and 915 MHz radios, as well as over 4G LTE cellular data links. Real-time data feeds from the sensor payloads to web browser clients anywhere in the world are possible via a virtual EC2 server on the Amazon AWS cloud. In FY 2019, the Nevada National Security Site teamed with Virginia Tech, H3D Corp., and Unmanned Systems, Inc. to accomplish the four flight missions described in this report. A large part of this year’s mission goals were to demonstrate the ability to fly beyond visual line of sight (BVLOS).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Attract-repel path planner system for collision avoidance

A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system assigns a repel value to the object locations and an attract value. A repel represents a magnitude of a directional repulsive force, and the attract value represents the magnitude of a directional repulsive force. The system calculates an attract-repel field having an attract-repel magnitude and attract-repel direction for the current location based on the repel values and their directions and the attract value and its direction. The system then determines the travel direction for a vehicle to be the direction of the attract-repel field at the current location.

Paglieroni, David W.↗
DOE OSTI2024

Collision avoidance based on traffic management data

A system for determining a travel direction that avoids objects when a vehicle travels from a current location to a target location is provided. The system determines a travel direction based on an attract-repel model. The system accesses external object information provided by an external object system. The external object information may include, for each of a plurality of objects, location, type, and constraint. The system assigns a repel value to the location of each object based on the type of and constraint on the object. The system assigns an attractive value to the target location. The system calculates a cumulative force based on the attractive and repulsive forces and sets the travel direction based on the cumulative force.

Beer, N. Reginald↗
DOE OSTI2022

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

Abstract Formation control of Unmanned Aerial Vehicles (UAVs) requires them to tightly cooperate to reach and keep the formation, while avoiding collision. This paper proposes a novel decentralized hybrid supervisory control approach for the formation control of multiple UAVs. This is achieved by developing a symbolic motion planning technique to polarly partition the motion space resulting in a finite state discrete event model for the motion dynamics of each UAV. Then, a modular discrete supervisor is designed for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Further, for the collision avoidance mechanism, a novel top‐down decomposition‐based approach is developed to design local supervisors decentralizedly. It is formally proved that with the proposed top‐down decomposition‐based approach, the (locally) supervised UAVs, as a whole, can cooperatively satisfy the desired (global) collision avoidance specification. The proposed decentralized supervisory control algorithm is also verified through a hardware‐in‐the‐loop simulator for the formation control of unmanned helicopters.

Karimoddini, Ali↗
DOE OSTI2023

Integrated path planning and control through proximal policy optimization for a marine current turbine

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. Further, to enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Abstract Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Autonomous Aerial Power Plant Inspection in GPS-denied Environments

Inspection of coal-fired power plants is frequently dangerous, includes difficult places to reach, and can turn expensive due to the downtimes and cost of inspection crew. Robotic systems have shown capabilities to address some of these issues, but most of the current robotic inspection technology in power plants is designed for specific components. Conversely, recent advances in machine vision have empowered aerial platforms for long-range, remotely-controlled, GPS-based inspections of industrial plants. This capability has led to wide spread utilization of aerial robots (commonly termed Drones, UVS or UAS) platforms for inspection in less challenging environments where both collision avoidance, and GPS reception are not significant issues. The challenge in adapting airborne technology for power plant inspection lies in internal structures and the complex network of piping, and distribution systems, which impose significant risks for collision and can hinder the reception and transmission of GPS signals. The current state of the art in aerial inspection technology within the energy sector is controlled via radio control, and utilizes GPS-based navigation, for inspection of large-scale plants such as offshore platforms and wind turbine parks. Nevertheless, close-range and autonomous inspection in the GPS-denied environments of power plants has not yet been achieved, as it requires precise guidance and navigation with real-time situational awareness and obstacle avoidance capabilities. This endeavor introduced the use of rotary wing flying robots, due to their station keeping and vertical take-off capabilities for power plant components inspection. To enable close quarter inspection two methods were used. One method uses the 3D CAD (Three-dimensional Computer-Aided Design) model of the asset to inspect to generate the UAV’s inspection path. To acquire, analyze and process the 3D model, first, the STL file is produced to obtain surface points and vectors normal to the surface. Later, by introducing other variables such as wall offset and a controlled trajectory between each outline and each subsequent layer, the flight path is generated. The proposed framework will generate a path that will pass as close as desired from the surface and navigate in intricate environments. A second method, use advanced manufacturing techniques such as CNC (Computer Numerical Control) and additive manufacturing. Once the inspection flight path is obtained, vision-based navigation systems are employed to have the UAV autonomously tracking the provided trajectory. Finally, Artificial Intelligence-enabled developments are in charge of detecting cracks and corrosion in structural components of power plants. The proposed methods are validated in simulations, laboratory and industrial setups, where it is shown that the developed systems acting together enable close-quarter autonomous aerial inspection and mapping in power plant assets. The system can be further improved by adding more sensors to navigate in different GPS-denied environments, with non-homogeneous lighting conditions, dust and in general situations where vision-based systems may fail.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

A machine-learning framework for the simulation of nuclear deflection of Planet-Killer-Asteroids

Here, as detection capabilities in astronomy have dramatically improved over the last two decades, concerns over Planet-Killer-Asteroids (PKAs) have become widespread, with nuclear weapons being proposed to destroy or deflect asteroids that are on a short-term projected collision course with Earth. Two main mitigation strategies have been proposed: • Case 1: Break up an incoming asteroid into smaller pieces that will disperse widely, resulting in smaller-scale, less detrimental, Earth-impacts or • Case 2: Deflect an incoming asteroid trajectory to avoid collision altogether. While the two strategies are not mutually exclusive, deflection is a safer strategy, ideally by harnessing all of the released energy from a nuclear device to move the asteroid as a rigid body. However, this case may not be always possible, since the strength of the energy release may break up the asteroid. In this work, the dynamical response of a PKA to a series of ultra-high energy impulses, such as those generated by nuclear devices, is formulated. A rapid iterative Discrete Element Method (DEM) method is developed to describe the deflection and potential breakup of the PKA as a function of a material bonding strength parameter within the asteroid and the magnitude of the applied impulse. The use of DEM allows for fragmentation of the PKA and the ability to compute the trajectories and distribution of the resulting debris field. Finally, a machine-learning algorithm is then developed and combined with the DEM approach to optimize the pulsation strategy for maximum possible safety and success.

42 ENGINEERING↗
DOE OSTI2023

Real-time control of connected vehicles in signalized corridors using pseudospectral convex optimization

Recent advances in Connected and Automated Vehicle (CAV) technologies have opened up new opportunities to enable safe, efficient, and sustainable transportation systems. However, developing reliable and rapid speed control algorithms in highly dynamic environments with complex inter-vehicle interactions and nonlinear vehicle dynamics is still a daunting task. In this paper, we develop a novel speed control method for CAVs to produce optimal speed profiles that minimize the fuel consumption and avoid idling at signalized intersections. To this end, an optimal control problem is formulated using the information of the upcoming traffic signal to adapt vehicles' speeds to avoid frequent stop-and-go driving patterns. Here, by applying the pseudospectral discretization method and the sequential convex programming method, the computational efficiency is greatly improved, enabling potential real-time on-vehicle applications. In addition, the algorithm is implemented under a model predictive control framework to ensure online control with instant response for collision avoidance and robust vehicle coordination. The proposed algorithm is verified through numerical simulations of three different traffic scenarios. The convergence and accuracy of the proposed approach are demonstrated by comparing with a popular nonlinear solver. Furthermore, the benefit of the proposed method in both traffic mobility and fuel efficiency is validated using the speed profile determined from a traffic following model in a simulation software as the baseline.

42 ENGINEERING↗
DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗
DOE OSTI2026

Optimization-based approaches to control of connected and automated vehicles: Principles, complexities, applications, challenges, and outlook

Safe and optimal motion control for connected and automated vehicles (CAVs) poses a fundamental optimization challenge at the intersection of system complexity, environmental uncertainty, and stringent real-time constraints. Existing surveys address this challenge in isolation – focusing either on specific control techniques or individual uncertainty sources – without providing a unified framework that characterizes the trade-offs among computational tractability, performance verifiability, and adaptive generalization across paradigms. This review addresses that gap by presenting a cohesive analytical framework concentrated on the decision-making and trajectory optimization layers of the CAV autonomy stack. We systematically analyze three major optimization paradigms – first-principles model-based optimization, data-driven methods, and hybrid synergistic architectures – evaluating each against four core complexity axes: problem formulation, constraint handling, optimality guarantees, and robustness. Key applications including platooning, trajectory planning, collision avoidance, and cooperative control are examined to reveal recurring methodological patterns and critical operational constraints that limit real-world performance. Our synthesis identifies verifiable hybrid architectures, incentive-aligned multi-agent cooperation, and hardware-algorithm co-design as the defining research frontiers, and distills a targeted agenda for developing CAV control systems that are simultaneously safe, computationally efficient, and deployable in the full complexity of real-world traffic environments.

Muzahid, Abu Jafar Md [University of Tennessee, Kn↗
DOE OSTI2022

Natural switches in behaviour rapidly modulate hippocampal coding

Throughout their daily lives, animals and humans often switch between different behaviours. However, neuroscience research typically studies the brain while the animal is performing one behavioural task at a time, and little is known about how brain circuits represent switches between different behaviours. Here we tested this question using an ethological setting: two bats flew together in a long 135 m tunnel, and switched between navigation when flying alone (solo) and collision avoidance as they flew past each other (cross-over). Bats increased their echolocation click rate before each cross-over, indicating attention to the other bat. Hippocampal CA1 neurons represented the bat’s own position when flying alone (place coding). Notably, during cross-overs, neurons switched rapidly to jointly represent the interbat distance by self-position. This neuronal switch was very fast—as fast as 100 ms—which could be revealed owing to the very rapid natural behavioural switch. The neuronal switch correlated with the attention signal, as indexed by echolocation. Interestingly, the different place fields of the same neuron often exhibited very different tuning to interbat distance, creating a complex non-separable coding of position by distance. Theoretical analysis showed that this complex representation yields more efficient coding. Overall, our results suggest that during dynamic natural behaviour, hippocampal neurons can rapidly switch their core computation to represent the relevant behavioural variables, supporting behavioural flexibility.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Cooperative Merging Speed Planning: A Vehicle-Dynamics-Free Method

Various cooperative merging control strategies at on-ramp have been proposed in the last decade. Approximated vehicle longitudinal motion models, e.g., kinematics model, have been broadly adopted for controller synthesis because of their simplicity. However, what appears problematic is that the models used for controller validation remain, in many cases, the same as the ones used for controller design. Indeed, actual vehicle dynamics contain rich behaviors that the simplified models cannot fully cover. In this paper, we first demonstrate that the actual vehicle speed can be dissimilar to the reference from a speed planner once vehicle dynamics is considered. Then, we propose two data-driven speed generators agnostic to vehicle dynamics. SUMO/Simulink joint simulations demonstrate that the proposed reference speed planners can successfully merge vehicles with distinct dynamics characteristics by following the desired sequence, speed, and intervehicle distance at the merging point while avoiding collisions.

Wang, Zejiang↗
DOE OSTI2021

Quantitative Evaluation of Autonomous Driving in CARLA

There has been a great deal of recent advancements in end-to-end imitation and reinforcement learning for self-driving vehicles. Despite this, there is a severe lack of standardized metrics for evaluating the performance of autonomous self-driving agents. Existing metrics are generally lacking in their ability to capture a wide range of driving behaviors and compare the severity of different failure cases. In this work, we introduce the Quantitative Evaluation for Driving metric, or QED, which assigns a quantitative score from 0-100 that captures the quality of driving for any driving agent. Our QED metric assesses different aspects of driving behavior including the ability to stay in the center of the lane, avoid weaving and erratic behavior, follow the speed limit, and avoid collisions, and it can be used under a wide range of driving scenarios. To show the effectiveness of our QED metric, we compare the scores generated by QED against scores assigned by human evaluators on a total of 30 different drivers and 6 different towns in the CARLA driving simulator. In ``easy'' evaluation scenarios, where it is relatively straightforward to distinguish better drivers from worse drivers, QED attains an average Pearson correlation of 0.96 and average Spearman correlation of 0.97 when compared against human evaluators. In ``hard'' evaluation scenarios, where it is far more ambiguous how to rank/score different types of bad driving behavior, QED attains an average Pearson correlation of 0.82 and average Spearman correlation of 0.75 when compared against human evaluators, which are both slighter higher than when we compare human evaluators against each other. While QED may not capture every characteristic that defines good driving, we consider it an important foundation for reproducibility and standardization in the community.

Gao, Shang↗
DOE OSTI2022

Investigation of Timing Properties for an Event Driven with Access and Reset Decoder Readout Architecture for a Pixel Array

The large number of data generating sources (data channels) on a single chip requires appropriate techniques to manage a readout from these channels. One of the main methods is sharing a medium of transmission, which requires arbitration to avoid collisions or deadlocks. Existing solutions face several problems such as a dead time, unintended prioritization or metastability. That is why we decided to create a new readout architecture named EDWARD i.e., Event Driven with Access and Reset Decoder. The EDWARD architecture gets rid of the earlier mentioned problems and mitigates the other ones. However, due to the use of logic circuits outside a standard cell library, which are hard to characterize, we were challenged to perform an additional transient analysis to validate the architecture. Here we show a methodology and the results of the simulations. Based on the results obtained we can confirm the functional correctness of the system and plan the optimization of operating conditions in order to achieve better performance. Our goal is to use the EDWARD architecture in the future radiation detectors to be built at Brookhaven National Laboratory.

47 OTHER INSTRUMENTATION↗