Optimal scheduling for refueling multiple autonomous aerial vehicles

Zhou, Jin; Shima, Tal; Schumacher, Corey

doi:10.1109/tro.2006.878793

Cited by 47 publications

(7 citation statements)

References 33 publications

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“…The aerial refuelling problem was examined in [9][10][11][12]. Jin et al [9] used a recursive dynamic programming approach to determine the optimal order for refuelling the aircraft to minimise the priority weighted time of refuelling for each aircraft. Such an approach is not applicable to SCAR scenarios as the optimal future decision in a SCAR scenario is dependent on the sequence of decisions leading to it.…”

Section: Related Literaturementioning

confidence: 99%

Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

Palmer

Hill

Scheding

2017

Robotics and Autonomous Systems

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Consideration of resources such as fuel, battery charge, and storage space, is a crucial requirement for the successful persistent operation of autonomous systems. The Stochastic Collection and Replenishment (SCAR) scenario is motivated by mining and agricultural scenarios where a dedicated replenishment agent transports a resource between a centralised replenishment point to agents using the resource in the field. The agents in the field typically operate within fixed areas (for example, benches in mining applications, and fields or orchards in agricultural scenarios), and the motion of the replenishment agent may be restricted by a road network. Existing research has typically approached the problem of scheduling the actions of the dedicated replenishment agent from a short-term and deterministic angle. This paper introduces a method of incorporating uncertainty in the schedule optimisation through a novel prediction framework, and a branch and bound optimisation method which uses the prediction framework to minimise the downtime of the agents. The prediction framework makes use of several Gaussian approximations to quickly calculate the risk-weighted cost of a schedule. The anytime nature of the branch and bound method is exploited within an MPC-like framework to outperform existing optimisation methods while providing reasonable calculation times in large scenarios.

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Section: Related Literaturementioning

confidence: 99%

Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

Palmer

Hill

Scheding

2017

Robotics and Autonomous Systems

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“…Here it is assumed that τ is chosen in accordance with (10). Note that our optimal periodic strategies introduced in the next section admit an easy derivation of D i (s) and then straightforward calculation ofl(s) by formula (15).…”

Section: Lemmamentioning

confidence: 99%

“…Theorem 3 Let n be an odd number. Suppose that τ satisfies inequality (10). Then strategy (18) is periodic.…”

Section: Optimal Replacement Schedulementioning

confidence: 99%

“…In some cases UAVs must continuously follow known trajectories through space without interruption [11], which necessitates the scheduling of a replacement that takes over at the precise point where the original UAV leaves the mission. In other cases, there is a mobile robot with the ability to recharge others, leading to the problem of scheduling meetings between robots as well as computing paths leading to the selected meeting points [14,10,4,13]. These solutions are not suitable for the case we consider here, where the recharging station is immobile and where no individual UAV is indispensable for guarding the perimeter.…”

Section: Introductionmentioning

confidence: 99%

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Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles

2016

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Guarding the perimeter of an area in order to detect potential intruders is an important task in a variety of security-related applications. This task can in many circumstances be performed by a set of camera-equipped unmanned aerial vehicles (UAVs). Such UAVs will occasionally require refueling or recharging, in which case they must temporarily be replaced by other UAVs in order to maintain complete surveillance of the perimeter. In this paper we consider the problem of scheduling such replacements. We present optimal replacement strategies and justify their optimality.

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“…This last configuration is known as the 'Probe and Drogue' system. The effort described in this paper is mostly relative to the US Air Force refuelling boom system and has the general goal of extending the use of this system to the refuelling of UAVs (1,2) . For this purpose a key issue is represented by the need of accurate measurement of the 'tanker-UAV' relative position and orientation from a 'pre-contact' to a 'contact' position and during the refuelling.…”

Section: Receptacle-3dw-centre Vectormentioning

confidence: 99%

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

Campa¹,

Napolitano²,

Perhinschi³

et al. 2007

Aeronaut. j.

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This paper describes the results of an effort on the analysis of the performance of specific ‘pose estimation’ algorithms within a Machine Vision-based approach for the problem of aerial refuelling for unmanned aerial vehicles. The approach assumes the availability of a camera on the unmanned aircraft for acquiring images of the refuelling tanker; also, it assumes that a number of active or passive light sources – the ‘markers’ – are installed at specific known locations on the tanker. A sequence of machine vision algorithms on the on-board computer of the unmanned aircraft is tasked with the processing of the images of the tanker. Specifically, detection and labeling algorithms are used to detect and identify the markers and a ‘pose estimation’ algorithm is used to estimate the relative position and orientation between the two aircraft. Detailed closed-loop simulation studies have been performed to compare the performance of two ‘pose estimation’ algorithms within a simulation environment that was specifically developed for the study of aerial refuelling problems. Special emphasis is placed on the analysis of the required computational effort as well as on the accuracy and the error propagation characteristics of the two methods. The general trade offs involved in the selection of the pose estimation algorithm are discussed. Finally, simulation results are presented and analysed.

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Optimal scheduling for refueling multiple autonomous aerial vehicles

Cited by 47 publications

References 33 publications

Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

Methods for Stochastic Collection and Replenishment (SCAR) optimisation for persistent autonomy

Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles

Addressing pose estimation issues for machine vision based UAV autonomous serial refuelling

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