Failure Analysis for an Unmanned Aerial Vehicle Using Safe Path Planning

Lin, Chin E.; Shao, Pei-Chi

doi:10.2514/1.i010795

Cited by 34 publications

(26 citation statements)

References 5 publications

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“…Computing risk and safety is another area of research that is a key component of ensuring safe UAV operation. Lin & Shao (2020) compute the expected level of safety (ELS) of a path as a function of mean time between failures, the area of exposure in square meters (assuming a ground impact), the population density, and accident severity. Ancel et al (2017) and Ancel et al (2019) present a real-time risk assessment framework that quantifies the risks to bystanders for operations in populated areas.…”

Section: Background: Safety Analysis Health Man-agement and Online Pmentioning

confidence: 99%

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

̃nones-Grueiro

Biswas

Ahmed

et al. 2021

IJPHM

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As the potential for deploying low-flying unmanned aerial vehicles (UAVs) in urban spaces increases, ensuring their safe operations is becoming a major concern. Given the uncertainties in their operational environments caused by wind gusts, degraded state of health, and probability of collision with static and dynamic objects, it becomes imperative to develop online decision-making schemes to ensure safe flights. In this paper, we propose an online decision-making framework that takes into account the state of health of the UAV, the environmental conditions, and the obstacle map to assess the probability of mission failure and re-plan accordingly. The online re-planning strategy considers two situations: (1) updating the current trajectory to reduce the probability of collision; and (2) defining a new trajectory to find a new safe landing spot, if continued flight would result in risk values above a pre-specified threshold. The re-planning routine uses the differential evolution optimization method and takes into account the dynamics of the UAV and its components as well as the environmental wind conditions. The new trajectory generation routine combines probabilistic road-maps with B-spline smoothing to ensure a dynamically feasible trajectory. We demonstrate the effectiveness of our approach by running UAV flight simulation experiments in urban scenarios.

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Section: Background: Safety Analysis Health Man-agement and Online Pmentioning

confidence: 99%

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

̃nones-Grueiro

Biswas

Ahmed

et al. 2021

IJPHM

View full text Add to dashboard Cite

show abstract

Section: Background: Safety Analysis Health Man-agement and Path Planmentioning

confidence: 99%

A decision-making framework for safe operations of unmanned aerial vehicles in urban scenarios

Quinones¹,

Darrah

Biswas³

et al. 2020

PHM_CONF

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This paper presents a decision-making scheme at the level of individual unmanned aerial vehicles (UAVs) with the goal of maintaining safe operations for urban mobility. The decision-making approach for a single UAV will consider the risks associated with the current trajectory given the existing environmental conditions and the state of the vehicle. The proposed scheme combines the analysis of system performance, environmental conditions, and mission level parameters for contingency management, i.e., make a determination on: (1) to abort mission and land safely; (2) re-plan current mission in full or abbreviated form; and (3) change mission. A path planning and trajectory optimization algorithm with the goal of minimizing the overall risk of mission failure by considering a number of factors such as the uncertainties in the environment and operating state of the vehicle is proposed. We will consider the mission failure as the loss of control of the vehicle resulting in a collision with other objects or a crash into the ground. An offline part of the framework generates an initial mission plan by considering the state of the vehicle, the environmental, conditions, and the static features of a map of the environment. Once the vehicle takes off, the risk of mission’ failure associated with the remaining trajectory is re-computed in an online framework to assess whether re-planning is required or not. A key challenge that we consider in this paper is to study the effects of multiple interacting subsystems of the UAV on system performance, especially under degraded conditions.

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“…A few studies have investigated the risks of a drone operation to the people and vehicles on the ground in urban low altitude airspace [4][5][6][7] and damage severity due to drone intrusion into aerodrome airspace [8][9][10]. In addition, some studies have developed a drone risk-based model assessment for path-planning by considering different third-party risks scenario [11,12].…”

Section: Introductionmentioning

confidence: 99%

Crash Area Estimation for Ground Risk of Small Unmanned Aerial Vehicles Due to Propulsion System Failures.

Man

Haoliang

Low

2022

AIAA SCITECH 2022 Forum

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Failure Analysis for an Unmanned Aerial Vehicle Using Safe Path Planning

Cited by 34 publications

References 5 publications

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

Online decision making and path planning framework for safe operation of unmanned aerial vehicles in urban scenarios

A decision-making framework for safe operations of unmanned aerial vehicles in urban scenarios

Crash Area Estimation for Ground Risk of Small Unmanned Aerial Vehicles Due to Propulsion System Failures.

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