Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

Аl-Мashhadani, Mohammad Abdulrahman

doi:10.1177/0020294019866860

Cited by 12 publications

(8 citation statements)

References 40 publications

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“…Different state estimation methods can be used to estimate the probability density function of the states and parameters of the UAV and its components. The estimation of the attitude, velocity, and position has been accomplished through complementary filtering techniques (Mahony et al, 2012), Kalman filtering methods and its extensions (Burri et Yang et al, 2017;Al-mashhadani, 2019). Inertial and visual sensor fusion algorithms can be employed in case visual sensors are available (Corke et al, 2007).…”

Section: System-level Prognostics Methodsologymentioning

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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Section: System-level Prognostics Methodsologymentioning

confidence: 99%

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

Quinones¹,

Darrah

Biswas³

et al. 2020

PHM_CONF

View full text Add to dashboard Cite

show abstract

“…On board sensor readings are fed to the UAV autopilot system to generate UAV state estimates [ 67 ]. The need for state estimation is due to the fact that data from measurement sensors is prone to uncertainties due to atmospheric disturbances, vibrations noise, inaccuracy of coordinate transformations, and missing measurements [ 68 ]. Sensors such as the GPS suffers from signal obstruction and reflections caused by nearby objects leading to missing or inadequate information [ 69 ].…”

Section: Drone Hardware Overviewmentioning

confidence: 99%

A review on drones controlled in real-time

Kangunde

Jamisola

Theophilus

2021

Int. J. Dynam. Control

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This paper presents related literature review on drones or unmanned aerial vehicles that are controlled in real-time. Systems in real-time control create more deterministic response such that tasks are guaranteed to be completed within a specified time. This system characteristic is very much desirable for drones that are now required to perform more sophisticated tasks. The reviewed materials presented were chosen to highlight drones that are controlled in real time, and to include technologies used in different applications of drones. Progress has been made in the development of highly maneuverable drones for applications such as monitoring, aerial mapping, military combat, agriculture, etc. The control of such highly maneuverable vehicles presents challenges such as real-time response, workload management, and complex control. This paper endeavours to discuss real-time aspects of drones control as well as possible implementation of real-time flight control system to enhance drones performance.

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“…We estimate the attitude, velocity, and position of the UAV using complementary filtering techniques (Mahony et al, 2012), or using extensions of Kalman filtering methods (Burri et al, 2015;Yang et al, 2017;Al-mashhadani, 2019). Inertial and visual sensor fusion algorithms can be employed if imaging sensors are available (Corke et al, 2007).…”

Section: State Estimationmentioning

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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Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty

Cited by 12 publications

References 40 publications

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

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

A review on drones controlled in real-time

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

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