Evolutionary Path Planning for Multiple UAVs in Message Ferry Networks Applying Genetic Algorithm

Harounabadi, Mehdi; Bocksberger, Martin; Mitschele‐Thiel, Andreas

doi:10.1109/pimrc.2018.8580936

Cited by 20 publications

(16 citation statements)

References 5 publications

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“…GA based approaches. In the context of trucks and drones in the lastmile delivery, the authors in [14], solved optimally MTSPD for small size A similar study in [24] proposed to use a UAV as a message ferry node that is in charge of traveling among disconnected nodes in a DTN network to deliver their messages. The authors introduced a multiple message ferry…”

Section: Meta-heuristic Based Approachesmentioning

confidence: 99%

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

Cheikhrouhou¹

2021

Preprint

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The Multiple Travelling Salesman Problem (MTSP) is among the most interesting combinatorial optimization problems because it is widely adopted in real-life applications, including robotics, transportation, networking, etc.Although the importance of this optimization problem, there is no survey dedicated to reviewing recent MTSP contributions. In this paper, we aim to fill this gap by providing a comprehensive review of existing studies on MTSP. In this survey, we focus on MTSP’s recent contributions to both classical vehicles/robots and unmanned aerial vehicles. We highlight the approaches applied to solve the MTSP as well as its application domains. We analyze the MTSP variants and propose a taxonomy and a classification of recent studies.

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Section: Meta-heuristic Based Approachesmentioning

confidence: 99%

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

Cheikhrouhou¹

2021

Preprint

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“…Sørli et al (2017) studied CPP for a UAV group to deploy several sensors in a 2D obstacle-free environment, and the cooperative co-evolving genetic strategy was used to minimize the makespan by an allocation of sensor placement tasks between UAVs. Harounabadi et al (2018) applied GA to solve CPP for a UAV group in message ferry networks, in which UAVs act as message ferries to deliver messages among isolated wireless nodes to minimize the message delivery delay. Binol et al (2018) studied CPP for a UAV group gathering intelligent transportation system (ITS) data from roadside units in a 2D road network.…”

Section: Classification Of Existing Researchmentioning

confidence: 99%

A review of cooperative path planning of an unmanned aerial vehicle group

Zhang

Xin

Dou

et al. 2020

Front Inform Technol Electron Eng

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As a cutting-edge branch of unmanned aerial vehicle (UAV) technology, the cooperation of a group of UAVs has attracted increasing attention from both civil and military sectors, due to its remarkable merits in functionality and flexibility for accomplishing complex extensive tasks, e.g., search and rescue, fire-fighting, reconnaissance, and surveillance. Cooperative path planning (CPP) is a key problem for a UAV group in executing tasks collectively. In this paper, an attempt is made to perform a comprehensive review of the research on CPP for UAV groups. First, a generalized optimization framework of CPP problems is proposed from the viewpoint of three key elements, i.e., task, UAV group, and environment, as a basis for a comprehensive classification of different types of CPP problems. By following the proposed framework, a taxonomy for the classification of existing CPP problems is proposed to describe different kinds of CPPs in a unified way. Then, a review and a statistical analysis are presented based on the taxonomy, emphasizing the coordinative elements in the existing CPP research. In addition, a collection of challenging CPP problems are provided to highlight future research directions.

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“…For cooperative trajectory planning, there have been many works planning path by solving multiple travelling salesman problem (MTSP) [15]- [22].…”

Section: B Mtsp Based Cooperative Trajectory Planningmentioning

confidence: 99%

“…The authors in [15] and [16] both used GA to solve the problem of path planning with UAVs acting as relay nodes in message ferry networks and collecting information from desired regions respectively. Chen et al [17] are dedicated to solving the multi-robot patrolling problem under the consideration of two objectives, minimizing both the maximum and the total tour distances.…”

Section: B Mtsp Based Cooperative Trajectory Planningmentioning

confidence: 99%

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Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles

Dong

Zhu

et al. 2020

IEEE Access

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Owing to the flexibility and low cost, cooperative Unmanned Aerial Vehicles(UAVs) have been attractive in multi-target positioning recently. Although it is popular and easy to accomplish, positioning based on trilateration method still faces challenges under scenarios with multiple UAVs. First, large accumulated errors will be brought if a single UAV is used to perform trilateration on same targets. Second, due to the mobility of targets, the time interval between UAVs performing twice successive distance measurement on one target cannot be long for positioning precision. Finally, the limited energy provided by onboard battery limits the time for UAVs to perform tasks. Once the energy used by some of the UAVs reaches limitation, the whole positioning mission will fail. Thus, to complete the mission of locating multiple targets, this paper is intended to minimize the maximum energy consumption among all UAVs. We formulate the problem, and decompose it into two subproblems, one of which plans the routes for UAV groups and the other plans the routes for UAVs in a group. To solve the first subproblem, a heuristic algorithm called adjusted genetic algorithm (AGA) is proposed to plan trajectories for all UAV groups under constraints on maximum energy consumption. To guarantee stable performance and reduce computation complexity, we propose an approximation algorithm, Tree Decomposition united with Christofides Algorithm (TDCA), and the approximation ratio is proved to be (3 * N /(2 * (N − 1))), where N denotes the number of UAV groups. For the second subproblem, a two-step greedy heuristic algorithm is proposed to plan trajectories for UAVs in same groups. Extensive simulations show that compared to existing algorithms, the proposed algorithms can reduce up to 26.6% maximum and 26.3% average energy consumption.

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Evolutionary Path Planning for Multiple UAVs in Message Ferry Networks Applying Genetic Algorithm

Cited by 20 publications

References 5 publications

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy

A review of cooperative path planning of an unmanned aerial vehicle group

Fair-Energy Trajectory Planning for Multi-Target Positioning Based on Cooperative Unmanned Aerial Vehicles

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