On solution of the Dubins touring problem

Faigl, Jan; Váňa, Petr; Saska, Martin; Báča, Tomáš; Spurný, Vojtěch

doi:10.1109/ecmr.2017.8098685

Cited by 19 publications

(28 citation statements)

References 14 publications

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“…The problem of determining the optimal headings at the waypoints for a given sequence of visits to the targets is called the DTP in Faigl et al (), and it has been addressed by several approaches. An optimal solution of the

D_{4}

instances of the DTP has been proposed in Goaoc, Kim, and Lazard ().…”

Section: Related Workmentioning

confidence: 99%

“…The required computational time is denoted

t

. (a) Uniform sampling—

N = 224

scriptL = 19.8

L_{U} = 13.8

t = 128

ms, (b) Guided sampling (Faigl et al, )–

N = 128

scriptL = 14.4

L_{U} = 14.2

t = 76

ms [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Related Workmentioning

confidence: 99%

“…Thus, for the interval of the full range

2 π

, the solution of DIP is a straight line segment connecting the locations. The tight lower bound (Manyam & Rathinam, ) has been utilized to guide sampling of the possible heading intervals and the heading values themselves in Faigl et al, (), where the authors show improved results over a uniform sampling of the heading (see Figure for an example of the DTSP solution based on the DTP and DIP).…”

Section: Related Workmentioning

confidence: 99%

“…A formal definition of the addressed problems with a brief overview of the Dubins vehicle model is presented in Section 3. Necessary background on the related Dubins touring problem (DTP; Faigl, Váňa, Saska, Báča, & Spurný, ) and 3D smooth trajectory parametrization based on Bézier curve is described in Section 4. The proposed VNS‐based

m

‐DTSPN solver is introduced in Section 5 and the generalized SOM‐based planner to the 3D surveillance planning is presented in Section 6.…”

Section: Introductionmentioning

confidence: 99%

“…An important part of the sampling‐based approaches for the DTSP is a solution of the DTP (Faigl et al, ). The DTP stands to determine the optimal heading values for a given sequence of the waypoint locations, and it can be formally defined as follows.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Faigl

Váňa

Pěnička

et al. 2018

Journal of Field Robotics

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The herein studied problem is motivated by practical needs of our participation in the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 in which a team of unmanned aerial vehicles (UAVs) is requested to collect objects in the given area as quickly as possible and score according to the rewards associated with the objects. The mission time is limited, and the most time‐consuming operation is the collection of the objects themselves. Therefore, we address the problem to quickly identify the most valuable objects as surveillance planning with curvature‐constrained trajectories. The problem is formulated as a multivehicle variant of the Dubins traveling salesman problem with neighborhoods (DTSPN). Based on the evaluation of existing approaches to the DTSPN, we propose to use unsupervised learning to find satisfiable solutions with low computational requirements. Moreover, the flexibility of unsupervised learning allows considering trajectory parametrization that better fits the motion constraints of the utilized hexacopters that are not limited by the minimal turning radius as the Dubins vehicle. We propose to use Bézier curves to exploit the maximal vehicle velocity and acceleration limits. Besides, we further generalize the proposed approach to 3D surveillance planning. We report on evaluation results of the developed algorithms and experimental verification of the planned trajectories using the real UAVs utilized in our participation in MBZIRC 2017.

show abstract

D_{4}

instances of the DTP has been proposed in Goaoc, Kim, and Lazard ().…”

Section: Related Workmentioning

confidence: 99%

“…The required computational time is denoted

t

. (a) Uniform sampling—

N = 224

scriptL = 19.8

L_{U} = 13.8

t = 128

ms, (b) Guided sampling (Faigl et al, )–

N = 128

scriptL = 14.4

L_{U} = 14.2

t = 76

ms [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Related Workmentioning

confidence: 99%

“…Thus, for the interval of the full range

2 π

Section: Related Workmentioning

confidence: 99%

m

‐DTSPN solver is introduced in Section 5 and the generalized SOM‐based planner to the 3D surveillance planning is presented in Section 6.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Faigl

Váňa

Pěnička

et al. 2018

Journal of Field Robotics

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Route Planning for Teams of Unmanned Aerial Vehicles Using Dubins Vehicle Model with Budget Constraint

Zahrádka

Pěnička

Saska

2019

Modelling and Simulation for Autonomous Systems

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Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle

et al. 2019

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Data collection missions are one of the many effective use cases of unmanned aerial vehicles (UAVs), where the UAV is required to visit a predefined set of target locations to retrieve data. However, the flight time of a real UAV is time constrained, and therefore only a limited number of target locations can typically be visited within the mission. In this paper, we address the data collection planning problem called the Dubins Orienteering Problem with Neighborhoods (DOPN), which sets out to determine the sequence of visits to the most rewarding subset of target locations, each with an associated reward, within a given travel budget. The objective of the DOPN is thus to maximize the sum of the rewards collected from the visited target locations using a budget constrained path between predefined starting and ending locations. The variant of the orienteering problem addressed here uses curvature-constrained Dubins vehicle model for planning the data collection missions for UAV. Moreover, in the DOPN, it is also assumed that the data, and thus the reward, may be collected from a close neighborhood sensing distance around the target locations, e.g., taking a snapshot by an onboard camera with a wide field of view, or using a sensor with a long range. We propose a novel approach based on the Variable Neighborhood Search (VNS) metaheuristic for the DOPN, in which combinatorial optimization of the sequence for visiting the target locations is simultaneously addressed with continuous optimization for finding Dubins vehicle waypoints inside the neighborhoods of the visited targets. The proposed VNS-based DOPN algorithm is evaluated in numerous benchmark instances, and the results show that it significantly outperforms the existing methods in both solution quality and computational time. The practical deployability of the proposed approach is experimentally verified in a data collection scenario with a real hexarotor UAV.

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On solution of the Dubins touring problem

Cited by 19 publications

References 14 publications

Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Unsupervised learning‐based flexible framework for surveillance planning with aerial vehicles

Route Planning for Teams of Unmanned Aerial Vehicles Using Dubins Vehicle Model with Budget Constraint

Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle

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