Model-based path prediction for fixed-wing unmanned aircraft using pose estimates

Kang, Changkoo; Woolsey, Craig A.

doi:10.1016/j.ast.2020.106030

Cited by 9 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As UAS applications continue to expand, researches have been carried out on the path prediction, tracking and control as well as collision avoidance algorithm development [27,28,29,30,31,32,33,34]. Detect-and-Avoid concept of operation is illustrated in [35] and several studies [36,37,38,29,39] specifically focus on the safety critical analysis of a standalone DAA with different methodologies as well as design of cognitive pilot-machine interface [40,41].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

View full text Add to dashboard Cite

This paper develops and evaluates the performance of an allocation agent to be potentially integrated into the onboard Detect and Avoid (DAA) computer of an Unmanned Aircraft System (UAS). We consider a UAS that can be fully controlled by the onboard DAA system and by a remote human pilot. With a communication channel prone to latency, we consider a mixed initiative interaction environment, where the control authority of the UAS is dynamically allocated by the allocation agent. In an encounter with a dynamic intruder, the probability of collision may increase in the absence of pilot commands in the presence of latency. Moreover, a delayed pilot command may not result in safe resolution of the current scenario and need to be improvised. We design an optimization algorithm to reduce collision risk and refine delayed pilot commands. Towards this end, a Markov Decision Process (MDP) and its solution are employed to create a wait time map. The map consists of estimated times that the UAS can wait for the remote pilot commands at each state. A command blending algorithm is designed to select an avoidance maneuver that prioritizes the pilot intention extracted from the pilot commands. The wait time map and the command blending algorithm are implemented and integrated into a closed-loop simulator. We conduct ten thousands fast-time Monte Carlo simulations and compare the performance of the integrated setup with a standalone DAA setup. The simulation results show that the allocation agent enables the UAS to wait without inducing any near mid air collision (NMAC) and severe loss of well clear (LoWC) while positively improve pilot involvement in the encounter resolution.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Tabassum,

Bai

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The study of the spatial trajectory localization prediction of motion targets currently has great research significance and application value in the fields of the military, aerospace, and intelligent driving [1]. Taking intelligent driving as an example, the number of traffic accidents due to drivers' driving behaviors has increased rapidly in recent years.…”

Section: Introductionmentioning

confidence: 99%

Study on the Construction of a Time-Space Four-Dimensional Combined Imaging Model and Moving Target Location Prediction Model

Zhu

Zeng

Han

et al. 2022

Sensors

View full text Add to dashboard Cite

Time-space four-dimensional motion target localization is a fundamental and challenging task in the field of intelligent driving, and an important part of achieving the upgrade in existing target localization technologies. In order to solve the problem of the lack of localization of moving targets in a spatio-temporal four-dimensional environment in the existing spatio-temporal data model, this paper proposes an optical imaging model in the four-dimensional time-space system and a mathematical model of the object-image point mapping relationship in the four-dimensional time-space system based on the central perspective projection model, combined with the one-dimensional “time” and three-dimensional “space”. After adding the temporal dimension, the imaging system parameters are extended. In order to solve the nonlinear mapping problem of complex systems, this paper proposes to construct a time-space four-dimensional object-image mapping relationship model based on a BP artificial neural network and demonstrates the feasibility of the joint time-space four-dimensional imaging model theory. In addition, indoor time-space four-dimensional localization prediction experiments verify the performance of the model in this paper. The maximum relative error rates of the predicted motion depth values, time values, and velocity values of this localization method compared with the real values do not exceed 0.23%, 2.03%, and 1.51%, respectively.

show abstract

Dynamic control allocation between onboard and delayed remote control for unmanned aircraft system detect-and-avoid

Tabassum

Bai

2022

Aerospace Science and Technology

View full text Add to dashboard Cite

Model-based path prediction for fixed-wing unmanned aircraft using pose estimates

Cited by 9 publications

References 16 publications

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Dynamic Control Allocation between Onboard and Delayed Remote Control for Unmanned Aircraft System Detect-and-Avoid

Study on the Construction of a Time-Space Four-Dimensional Combined Imaging Model and Moving Target Location Prediction Model

Dynamic control allocation between onboard and delayed remote control for unmanned aircraft system detect-and-avoid

Contact Info

Product

Resources

About