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Results for “Trajectory Replanning”

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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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Combined Bernstein Polynomial, Optimal Reciprocal Collision Avoidance, Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

This paper presents an integration of Differential Dynamic Programming (DDP) with the Optimal Reciprocal Collision Avoidance (ORCA) algorithm as the basis for a new algorithm, titled Combined Bernstein Polynomial Optimal Reciprocal Collision Avoidance DDP (COBRA-DDP), for trajectory replanning and collision avoidance for Urban Air Mobility (UAM) vehicles. State-constrained variants of DDP provide the ability to plan trajectories while avoiding obstacles, but these methods require a large increase in computational time per iteration which hinders the overall speed of the algorithm. ORCA utilizes simplified dynamics to recognize potential collisions along a trajectory and provides an optimal velocity for the avoidance of multiple vehicles. These velocity commands, however, may not result in a dynamically feasible trajectory for DDP to plan around. As such, a Bernstein polynomial curve that considers the general dynamic constraints of the vehicle is generated to approximate a trajectory based on the velocity commands. COBRA-DDP optimizes this suggested trajectory via unconstrained DDP to provide a dynamically feasible trajectory that provides collision avoidance. This new trajectory can be applied to the vehicle or used to warm start the state constrained DDP algorithms to decrease computation time. Its benefits and effectiveness of the algorithm are demonstrated on a UAM Vertical Takeoff and Landing (VTOL) vehicle simulation with highly nonlinear dynamics.

Optimal Reciprocal Collision Avoidance↗
NASA NTRSDate not supplied

Adaptive Optimization for System Performance and Combined Bernstein Polynomial, Optimal Reciprocal Collision Avoidance, Differential Dynamic Programming for Trajectory Replanning and Collision Avoidance for UAM Vehicles

The emerging urban air mobility (UAM) sector in aerospace is driving development of unconventional multi-modal vehicle configurations and autonomous flight. The combination of multi-modal vehicle dynamics, complex environment, requirements to deal with flight contingencies in an efficient and safe manner, as well as necessity for precise trajectory following and performance, are the driving influence behind adaptive optimization for system performance. We are interested in trajectory optimization algorithm that would system parameter estimation and identifying the optimal switching time between modes of hybrid dynamical systems. This presentation discusses a parameterized optimal control trajectory optimization algorithm that is an extended and generalized version of Differential Dynamic Programming (DDP), titled Parameterized Differential Dynamic Programming (PDDP). DDP is an efficient trajectory optimization algorithm relying on second order approximations of a system’s dynamics and cost function and has recently been applied to optimize systems with time invariant parameters. Experiments are presented applying PDDP to solve model predictive control (MPC) and moving horizon estimation (MHE) tasks simultaneously. In particular, PDDP is used to determine the optimal transition point between flight regimes of a complex urban air mobility (UAM) class vehicle exhibiting multiple phases of flight and to identify and compensate for actuation faults.

optimization↗
NASA NTRSDate not supplied

Traffic Prediction for Uncommunicative Aircraft in Terminal Airspace: Development Framework and Performance Evaluations

This paper presents an air traffic prediction algorithm that takes observations of an aircraft and classifies aircraft type, estimates the aircraft's intent to and method of joining an airport traffic pattern, and predicts the aircaft's future trajectory. To develop algorithms that enable autonomous aircraft to safely insert into un-towered traffic patterns, several challenges need to be addressed. These challenges range from traffic detection to sensor fusion to own-ship trajectory replanning. Critical to a trajectory replanning algorithm is information regarding the future behavior of all traffic aircraft in the operational environment. The presented traffic prediction algorithm generates this information using regular measurements of traffic aircraft position and velocity to classify the aircraft by speed-class, estimate how the aircraft will approach the runway, and construct a predicted trajectory to the runway including future positions and velocities at specific times. The predictions of the presented algorithm are the necessary inputs for any downstream traffic pattern sequencing and own-ship trajectory planning routines. The presented algorithm is benchmarked using approximately 300 randomized traffic trajectories, spanning four vehicle weight classes and eight traffic entry types. While the algorithm can process multiple traffic vehicles in the terminal area, there is no prediction of traffic-on-traffic interaction. Each traffic vehicle is processed separately.

John D McMinn↗
NASA NTRSDate not supplied

COBRA-DDP: Trajectory Generation and Collision Avoidance Augmentations for eVTOL Vehicles

This paper presents a receding horizon model predictive control variation of the combined Bernstein polynomial optimal reciprocal collision avoidance (ORCA) differential dynamic programming (COBRA-DDP) algorithm for AAM vehicles. Collision avoidance in combination with effective trajectory replanning are expected to be core components of AAM vehicles operating within a crowded airspace. This environment necessitates the use of real-time trajectory planning algorithms that are capable of planning around large amounts of stationary and moving obstacles. Previous work on COBRA-DDP demonstrated the capability of the algorithm to produce dynamically feasible trajectories for AAM vehicles and general collision avoidance. This paper improves upon the previous work by increasing the number of stationary and moving obstacles, implementing a variation of COBRA-DDP that lends itself to real-time application. These advancements are demonstrated on a vertical takeoff and landing (VTOL) vehicle simulation with highly nonlinear vehicle dynamics.

COBRA-DDP↗
NASA NTRSDate not supplied

COBRA-DDP: Trajectory Generation and Collision Avoidance Augmentations for eVTOL Vehicles

This paper presents a receding horizon model predictive control variation of the combined Bernstein polynomial optimal reciprocal collision avoidance (ORCA) differential dynamic programming (COBRA-DDP) algorithm for AAM vehicles. Collision avoidance in combination with effective trajectory replanning are expected to be core components of AAM vehicles operating within a crowded airspace. This environment necessitates the use of real-time trajectory planning algorithms that are capable of planning around large amounts of stationary and moving obstacles. Previous work on COBRA-DDP demonstrated the capability of the algorithm to produce dynamically feasible trajectories for AAM vehicles and general collision avoidance. This paper improves upon the previous work by increasing the number of stationary and moving obstacles, implementing a variation of COBRA-DDP that lends itself to real-time application. These advancements are demonstrated on a vertical takeoff and landing (VTOL) vehicle simulation with highly nonlinear vehicle dynamics.

COBRA-DDP↗
NASA NTRS2024

An On-Board Off-Board Framework for Online Replanning: Applied to UAVs in Urban Environments

Autonomous systems are being used in a multitude of areas at an increasing rate and require a high level of adaptivity and intelligence to operate safely, especially under faulty conditions. This paper introduces a novel genetic algorithm tailored for UAV trajectory replanning, with an improved execution time via search space reduction based on the operating conditions of the UAV and its remaining mission. A unique characteristic of the replanning agent is its fast-start and adaptive properties, pre-seeding candidates with partial solutions and dynamically tuning elitism, crossover, and mutation rates in correspondence to the average fitness and diversity of the population. A population restart mechanism and early stopping mechanism are evaluated as well to assess their effect on solution quality and runtime. Previous work on genetic algorithms for UAV replanning were conducted with short trajectories in a small state space. Our UAV operates in a 56,000 square meter simulated urban environment, with static obstacles and a total of 53 possible waypoints. The agent increases the safety and reliability of UAV autonomy when operating under faulty conditions and when replanning is required.

Machine Learning↗
NASA NTRS2012

Traffic Aware Strategic Aircrew Requests (TASAR)

Under Instrument Flight Rules, pilots are not permitted to make changes to their approved trajectory without first receiving permission from Air Traffic Control (ATC). Referred to as "user requests," trajectory change requests from aircrews are often denied or deferred by controllers because they have awareness of traffic and airspace constraints not currently available to flight crews. With the introduction of Automatic Dependent Surveillance-Broadcast (ADS-B) and other information services, a rich traffic, weather, and airspace information environment is becoming available on the flight deck. Automation developed by NASA uses this information to aid flight crews in the identification and formulation of optimal conflict-free trajectory requests. The concept of Traffic Aware Strategic Aircrew Requests (TASAR) combines ADS-B and airborne automation to enable user-optimal in-flight trajectory replanning and to increase the likelihood of ATC approval for the resulting trajectory change request. TASAR may improve flight efficiency or other user-desired attributes of the flight while not impacting and potentially benefiting the air traffic controller. This paper describes the TASAR concept of operations, its enabling automation technology which is currently under development, and NASA s plans for concept assessment and maturation.

Ballin, Mark G.↗
NASA NTRS2013

Developing an Onboard Traffic-Aware Flight Optimization Capability for Near-Term Low-Cost Implementation

The concept of Traffic Aware Strategic Aircrew Requests (TASAR) combines Automatic Dependent Surveillance Broadcast (ADS-B) IN and airborne automation to enable user-optimal in-flight trajectory replanning and to increase the likelihood of Air Traffic Control (ATC) approval for the resulting trajectory change request. TASAR is designed as a near-term application to improve flight efficiency or other user-desired attributes of the flight while not impacting and potentially benefiting ATC. Previous work has indicated the potential for significant benefits for each TASAR-equipped aircraft. This paper will discuss the approach to minimizing TASAR's cost for implementation and accelerating readiness for near-term implementation.

Wing, David J.↗
NASA NTRSDate not supplied

Evaluating Lunar Descent and Landing Performance From a Near Rectilinear Halo Orbit Using Linear Covariance Resetting Techniques

Upcoming lunar programs are striving the achieve precision landing in a safe and robust manner. Various elements impact this mission objective ranging from on-orbit operations with ground station tracking to incorporating relative sensors with hazard detection and avoidance (HDA) to support the final approach and landing phase. Modeling the impacts of ground tracking, trajectory replanning, relative navigation sensors, and particularly a potential HDA system on the integrated closed-loop GN\&C system performance poses a unique challenge due to the complexity and interaction with multiple facets of the vehicle including the trajectory design, sensing hardware, navigation system, guidance and targeting, and the overall mission concept of operations. This paper outlines techniques to systematically analyze and compare the performance impacts of ground tracking and replanning and an HDA system where the onboard navigation errors are reset or uploaded from an external source and the vehicle's reference trajectory is regenerated requiring the system dispersions to also be reset to reflect this in-flight profile adjustment. To illustrate the application of these general techniques for analyzing the performance impacts due to incorporating these resetting events, they are demonstrated with a human lunar descent and landing scenario starting from a near rectilinear halo orbit (NRHO) until the vehicle precisely reaches its predetermined landing site on the lunar surface. Performance metrics such as inertial and relative navigation errors, trajectory dispersions, footprint dispersions, and propellant usage are provided.

GN&C↗
NASA NTRS1988

A software engineering approach to expert system design and verification

Software engineering design and verification methods for developing expert systems are not yet well defined. Integration of expert system technology into software production environments will require effective software engineering methodologies to support the entire life cycle of expert systems. The software engineering methods used to design and verify an expert system, RENEX, is discussed. RENEX demonstrates autonomous rendezvous and proximity operations, including replanning trajectory events and subsystem fault detection, onboard a space vehicle during flight. The RENEX designers utilized a number of software engineering methodologies to deal with the complex problems inherent in this system. An overview is presented of the methods utilized. Details of the verification process receive special emphasis. The benefits and weaknesses of the methods for supporting the development life cycle of expert systems are evaluated, and recommendations are made based on the overall experiences with the methods.

Bochsler, Daniel C.↗
NASA NTRS2012

Development of Complexity Science and Technology Tools for NextGen Airspace Research and Applications

The objective of this research by NextGen AeroSciences, LLC is twofold: 1) to deliver an initial "toolbox" of algorithms, agent-based structures, and method descriptions for introducing trajectory agency as a methodology for simulating and analyzing airspace states, including bulk properties of large numbers of heterogeneous 4D aircraft trajectories in a test airspace -- while maintaining or increasing system safety; and 2) to use these tools in a test airspace to identify possible phase transition structure to predict when an airspace will approach the limits of its capacity. These 4D trajectories continuously replan their paths in the presence of noise and uncertainty while optimizing performance measures and performing conflict detection and resolution. In this approach, trajectories are represented as extended objects endowed with pseudopotential, maintaining time and fuel-efficient paths by bending just enough to accommodate separation while remaining inside of performance envelopes. This trajectory-centric approach differs from previous aircraft-centric distributed approaches to deconfliction. The results of this project are the following: 1) we delivered a toolbox of algorithms, agent-based structures and method descriptions as pseudocode; and 2) we corroborated the existence of phase transition structure in simulation with the addition of "early warning" detected prior to "full" airspace. This research suggests that airspace "fullness" can be anticipated and remedied before the airspace becomes unsafe.

Holmes, Bruce J.↗
NASA NTRS1988

Collision detection for planning collision-free motion of two robot arms

The authors focus on collision detection for planning collision-free motion of two robot arms in a common workspace. A collision-free motion is obtained by detecting collisions along the straight-line trajectories of the robots using a sphere model for the wrists and then replanning the paths and/or trajectories of one or both of the robots to avoid collisions. A novel approach to collision detection is presented and a discussion on collision avoidance is given.

Basta, Robert A.↗
NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

We propose a framework for adaptive guidance based on optimal control and estimation using closed-loop performance models. In the proposed approach, parameters of a reduced-order performance model are estimated in real time so that trajectories and guidance commands are replanned with more accurate knowledge of the system's response and performance capabilities. To apply this methodology to flight control systems, we introduce a simple yet expressive performance model modified from a reference linear design model. The proposed framework is applied to guidance of a simulated Advanced Air Mobility class concept aircraft experiencing control effector failures. We demonstrate that, by optimizing performance model parameters in real time, guidance commands can be intelligently adjusted to recover system stability and the performance of a full-order vehicle model, even in the event of effector failures. Furthermore, the reduced-order performance model requires a fraction of the computational cost of the full-order model, facilitating real-time use of adaptive, optimal guidance.

Differential dynamic programming↗
NASA NTRSDate not supplied

Trajectory Planning and Online Performance Model Estimation for Advanced Air Mobility

We propose a framework for adaptive guidance based on optimal control and estimation using closed-loop performance models. In the proposed approach, parameters of a reduced-order performance model are estimated in real time so that trajectories and guidance commands are replanned with more accurate knowledge of the system's response and performance capabilities. To apply this methodology to flight control systems, we introduce a simple yet expressive performance model modified from a reference linear design model. The proposed framework is applied to guidance of a simulated Advanced Air Mobility class concept aircraft experiencing control effector failures. We demonstrate that, by optimizing performance model parameters in real time, guidance commands can be intelligently adjusted to recover system stability and the performance of a full-order vehicle model, even in the event of effector failures. Furthermore, the reduced-order performance model requires a fraction of the computational cost of the full-order model, facilitating real-time use of adaptive, optimal guidance.

Differential dynamic programming↗
NASA NTRS2001

Techniques for Improving Pilot Recovery from System Failures

This project examined the application of intelligent cockpit systems to aid air transport pilots at the tasks of reacting to in-flight system failures and of planning and then following a safe four dimensional trajectory to the runway threshold during emergencies. Two studies were conducted. The first examined pilot performance with a prototype awareness/alerting system in reacting to on-board system failures. In a full-motion, high-fidelity simulator, Army helicopter pilots were asked to fly a mission during which, without warning or briefing, 14 different failures were triggered at random times. Results suggest that the amount of information pilots require from such diagnostic systems is strongly dependent on their training; for failures they are commonly trained to react to with a procedural response, they needed only an indication of which failure to follow, while for 'un-trained' failures, they benefited from more intelligent and informative systems. Pilots were also found to over-rely on the system in conditions were it provided false or mis-leading information. In the second study, a proof-of-concept system was designed suitable for helping pilots replan their flights in emergency situations for quick, safe trajectory generation. This system is described in this report, including: the use of embedded fast-time simulation to predict the trajectory defined by a series of discrete actions; the models of aircraft and pilot dynamics required by the system; and the pilot interface. Then, results of a flight simulator evaluation with airline pilots are detailed. In 6 of 72 simulator runs, pilots were not able to establish a stable flight path on localizer and glideslope, suggesting a need for cockpit aids. However, results also suggest that, to be operationally feasible, such an aid must be capable of suggesting safe trajectories to the pilot; an aid that only verified plans entered by the pilot was found to have significantly detrimental effects on performance and pilot workload. Results also highlight that the trajectories suggested by the aid must capture the context of the emergency; for example, in some emergencies pilots were willing to violate flight envelope limits to reduce time in flight - in other emergencies the opposite was found.

Pritchett, Amy R.↗
NASA NTRS2014

Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms

Dynamic environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal paths through these environments. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially with the number of agents in the system. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance.

Coevolution↗