Dubins orienteering problem with neighborhoods

Pěnička, Robert; Faigl, Jan; Váňa, Petr; Saska, Martin

doi:10.1109/icuas.2017.7991350

Cited by 19 publications

(20 citation statements)

References 20 publications

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“…The mathematical model corresponding to the orienteering problem allows us to accommodate various restrictions of personalized tours (Gavalas, Konstantopoulos, Mastakas, Pantziou, & Vathis, 2015;Zhu, Hu, Wang, Xu, & Cao, 2012). The tourism literature confirms that there exist different variants of orienteering problems related to tour planning problems (Archetti, Feillet, Hertz, & Speranza, 2009;Evers, Glorie, Van Der Ster, Barros, & Monsuur, 2014;Penicka, Faigl, Vana, & Saska, 2017). A recent survey on orienteering problems is also available (Gunawan et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Maximizing pleasure in day tours: Utility theory approach

Jana

Mitra

2020

Journal of Tourism Research

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A personalized day tour planning problem is studied to maximize the total pleasure gained from the tour. The concept of utility theory is employed for this purpose. Considering the uniform and diminishing marginal pleasure intensities, two zero-one mixed integer nonlinear programming models are proposed. Both the models can accommodate preferred time slots, visits to specific spots, and spots diversity. A branch-and-bound algorithm is used to solve the proposed models. Finally, they are applied to solve the day tour problem for Delhi, India. The tour plan obtained using the diminishing marginal pleasure intensity offers an improved value of total pleasure.

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Section: Literature Reviewmentioning

confidence: 99%

Maximizing pleasure in day tours: Utility theory approach

Jana

Mitra

2020

Journal of Tourism Research

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“…They provide a dynamic programming solution to the addressed path planning problem. In [33], [34] they present a VNS approach for Dubins vehicles to the OP and COP respectively. Their approach successfully computes optimised paths, but the computational cost is prohibitive for online application.…”

Section: Previous Workmentioning

confidence: 99%

An Evolutionary Algorithm for Online, Resource-Constrained, Multivehicle Sensing Mission Planning

Tsiogkas

Lane

2018

IEEE Robot. Autom. Lett.

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Mobile robotic platforms are an indispensable tool for various scientific and industrial applications. Robots are used to undertake missions whose execution is constrained by various factors, such as the allocated time or their remaining energy. Existing solutions for resource constrained multi-robot sensing mission planning provide optimal plans at a prohibitive computational complexity for online application [1], [2], [3]. A heuristic approach exists for an online, resource constrained sensing mission planning for a single vehicle [4]. This work proposes a Genetic Algorithm (GA) based heuristic for the Correlated Team Orienteering Problem (CTOP) that is used for planning sensing and monitoring missions for robotic teams that operate under resource constraints. The heuristic is compared against optimal Mixed Integer Quadratic Programming (MIQP) solutions. Results show that the quality of the heuristic solution is at the worst case equal to the 5% optimal solution. The heuristic solution proves to be at least 300 times more time efficient in the worst tested case. The GA heuristic execution required in the worst case less than a second making it suitable for online execution.

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“…An extension of the DTSP for the non-zero sensing distance is called the Dubins Traveling Salesman Problem with Neighborhoods (DTSPN) (Obermeyer 2009;Isaacs et al 2011;Váňa and Faigl 2015). In this paper, we consider a similar extension of the DOP to the Dubins Orienteering Problem with Neighborhoods (DOPN), initially introduced in Pěnička et al (2017b). Although Fig.…”

Section: Introductionmentioning

confidence: 99%

“…A preliminary version of this work appears in Pěnička et al (2017b), where the DOPN is addressed by a purely sampling-based approach. This paper is considered to make the following contributions.…”

Section: Introductionmentioning

confidence: 99%

“…The method introduced here significantly improves the solution quality and decreases the overall required computational time, which allows onboard online planning and anytime behavior. The proposed method combines combinatorial optimization and continuous optimization in a single VNS-based framework, which outperforms the previous purely combinatorial sampling-based solution (Pěnička et al 2017b) and also the competitive Self-Organizing Map (SOM) based solution (Faigl and Pěnička 2017). The initial low-density waypoint sampling allows us to obtain high quality initial solutions (≈ 90% of the best-known rewards) within a few seconds, and due to the continuous optimization of the waypoints, the solution quality is improved above the so far best-known solutions created by dense waypoint sampling with required initialization in tens of minutes.…”

Section: Introductionmentioning

confidence: 99%

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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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Dubins orienteering problem with neighborhoods

Cited by 19 publications

References 20 publications

Maximizing pleasure in day tours: Utility theory approach

Maximizing pleasure in day tours: Utility theory approach

An Evolutionary Algorithm for Online, Resource-Constrained, Multivehicle Sensing Mission Planning

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

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