Vector Field UAV Guidance for Path Following and Obstacle Avoidance with Minimal Deviation

Wilhelm, Jay; Clem, Garrett

doi:10.2514/1.g004053

Cited by 59 publications

(33 citation statements)

References 51 publications

(87 reference statements)

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“…The proposed solutions are validated using hardware-in-the-loop simulations. Encoding a GNC solution with a vector field, similar to the one analyzed in this work, can be found in [7]. Specifically, the authors present a Gradient vector field (GVF) path following and circular obstacle avoidance that allows a fixed-wing UAV to avoid detected obstacles without the need to replan the whole flight path.…”

Section: Related Work a Aircraft Guidance Navigation And Controlmentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

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New generations of small advanced aircraft may soon transform how people and freight are transported. Thus, ensuring safe and reliable operation of these systems, in all plausible scenarios, is of utmost priority. In this paper, we address this problem by proposing a method to verify the safety of an autonomous aircraft controller following a navigational policy. The policy is encoded using discrete vector fields that are meant to drive the vehicle to a goal state and away from obstacles or restricted airspace. The solution presented in this paper provides the ability to verify the policy, catching unsafe scenarios which may be missed by random Monte Carlo methods or general reachability analysis. After illustrating the main theoretical principles, a possible practical implementation is described and compared with analysis based on Monte Carlo simulations. The comparison shows an insightful example where Monte Carlo simulations fail to detect several corner cases that are uncovered by the proposed method. In the conclusion of the paper, we provide insights about possible future research to implement the proposed solution obtaining higher accuracy and efficiency. INDEX TERMS Navigation, Unmanned aerial vehicles, Autonomous agents, Reachability analysis VOLUME 4, 2016 This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3062662, IEEE Access Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation Giovanni Miraglia et al.: Geometric Verification of Vector Fields for Autonomous Aircraft Navigation LOYD R. HOOK (Member, IEEE) has worked in aircraft autonomous systems research and implementation for the past 15 years beginning at NASA Dryden/Armstrong flight research center and continuing at his current position as Assistant Professor at the University of Tulsa. Loyd is currently the director of the TU Vehicle Autonomy and Intelligence Lab (TU VAIL) where his research focus is the development of safety assured autonomous vehicle systems. Loyd has MS and Ph.D. degrees from the University of Oklahoma in Electrical and Computer Engineering from 2006 and 2011 respectively.

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Section: Related Work a Aircraft Guidance Navigation And Controlmentioning

confidence: 99%

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Miraglia

Hook

2021

IEEE Access

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“…The higher the χ e , the quicker the RPA will attempt to travel towards the flight path. k is called the proportionality constant [20] or transition gain [23] that is commonly set to a value of 1 [20] and is constrained to 0 < k ≤ 1 [22][23][24], it characterizes the response time of how the autopilot will maintain RPA's course-hold [22]. τ is a boundary that surrounds the flight path and is a distance that defines whether the RPA is far from or close to the desired path, see Figure 2.2 (a).…”

Section: Terrain Following Radar Evolutionmentioning

confidence: 99%

“…β controls the convergence space; the higher the value, the closer the RPA will attempt to converge with the flight path regardless the length of the transition boundary [20]. Vector Field Straight-Line Following Algorithm (Pseudocode) The following section outlines the pseudocode for a circular orbit VF PFA algorithm that is summarized in [20,22,24]. As it was seen in the straight-line VF PFA (see Section 2.2.1), the circular VF PFA has two primary outputs for the algorithm:…”

Section: Terrain Following Radar Evolutionmentioning

confidence: 99%

Terrain Draping and Following Using Low-Cost Remotely Piloted Aircraft Systems for Geophysical Survey Applications

Shafi¹

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Collecting high-resolution aeromagnetic data require sensors to be active at lower above ground level altitudes. Cost associated is the driving factor for pursuing remotely piloted aircraft systems. Operators have been considering these systems to complement manned aircraft surveillance missions. To provide accurate aeromagnetic readings, remotely piloted aircraft must fly low at constant above ground level altitudes and therefore follow the terrain. I want to start by thanking NSERC and our industry partner Sander Geophysics Ltd. for the financial support that they provided for my research.

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“…While the problem of UAV standoff tracking has clearly been studied extensively, some authors have expanded LVFs beyond this application. For example, LVFs and other closely-related vector fields have been used for UAV obstacle avoidance [22,23]. Scorsoglio and Furfaro [18] used a LVF-based relative motion guidance for docking in a cislunar space environment.…”

Section: Previous Workmentioning

confidence: 99%

“…While these additional simplifying assumptions may seem limiting, it should be noted that all of these assumptions are used in the overwhelming majority of published work regarding LVFs. For example, the Γ matrix is considered implicitly to be a multiple of the identity matrix in [14,15,[20][21][22][23] as the contraction component of velocity is is simply defined as the negative gradient direction. In [17], the general positive definite Γ is presented, but every example within the paper chooses a Γ which is a multiple of identity.…”

Section: Acceleration Constraintsmentioning

confidence: 99%

Cascaded Lyapunov Vector Fields for Spacecraft Relative Trajectory Tracking in Rotating Reference Frames Under Acceleration Constraints

Hough¹

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Vector Field UAV Guidance for Path Following and Obstacle Avoidance with Minimal Deviation

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Cited by 59 publications

References 51 publications

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Geometric Verification of Vector Fields for Autonomous Aircraft Navigation

Terrain Draping and Following Using Low-Cost Remotely Piloted Aircraft Systems for Geophysical Survey Applications

Cascaded Lyapunov Vector Fields for Spacecraft Relative Trajectory Tracking in Rotating Reference Frames Under Acceleration Constraints

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