Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations

Seyedi, Sepehr; Yazıcıoğlu, Yasin; Aksaray, Derya

doi:10.1016/j.ifacol.2019.12.157

Cited by 35 publications

(5 citation statements)

References 18 publications

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“…Similarly in Sun et al [18], the worker is planned to travel between waypoints, while the station is planned to rendezvous to charge the worker. Seyedi et al [19] planned trajectories for multiple workers and one station with scheduled charging order, which also requires a prior of the environment. Despite the above work managed to plan for both workers and station, it is only applicable on convex target areas with static obstacles, thus is infeasible in an arbitrary target area with dynamic interferers.…”

Section: B Worker-station Multi-robot Systemmentioning

confidence: 99%

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

Tang¹,

Gao²,

Lam³

2022

Preprint

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For massive large-scale tasks, a multi-robot system (MRS) can effectively improve efficiency by utilizing each robot's different capabilities, mobility, and functionality. In this paper, we focus on the multi-robot coverage path planning (mCPP) problem in large-scale planar areas with random dynamic interferers in the environment, where the robots have limited resources. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We aim to solve the mCPP problem for the worker-station MRS by formulating it as a fully cooperative multi-agent reinforcement learning problem. Then we propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station. Our method manages to reduce the influence of random dynamic interferers on planning, while the robots can avoid collisions with them. We conduct simulation and real robot experiments, and the comparison results show that our method has competitive performance in solving the mCPP problem for worker-station MRS in metric of task finish time.

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Section: B Worker-station Multi-robot Systemmentioning

confidence: 99%

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

Tang¹,

Gao²,

Lam³

2022

Preprint

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“…Other methods, such as [21]- [24], provide solutions to a similar application -persistent monitoring. While persistent monitoring is similar, it generally aims to create routes for robots which will be repeated constantly.…”

Section: Related Workmentioning

confidence: 99%

Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

Collins

Ghassemi

Esfahani

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents a novel multi-robot coverage path planning (CPP) algorithm -aka SCoPP -that provides a time-efficient solution, with workload balanced plans for each robot in a multi-robot system, based on their initial states. This algorithm accounts for discontinuities (e.g., nofly zones) in a specified area of interest, and provides an optimized ordered list of way-points per robot using a discrete, computationally efficient, nearest neighbor path planning algorithm. This algorithm involves five main stages, which include the transformation of the user's input as a set of vertices in geographical coordinates, discretization, load-balanced partitioning, auctioning of conflict cells in a discretized space, and a path planning procedure. To evaluate the effectiveness of the primary algorithm, a multi-unmanned aerial vehicle (UAV) post-flood assessment application is considered, and the performance of the algorithm is tested on three test maps of varying sizes. Additionally, our method is compared with a state-of-the-art method created by Guasella et al. Further analyses on scalability and computational time of SCoPP are conducted. The results show that SCoPP is superior in terms of mission completion time; its computing time is found to be under 2 mins for a large map covered by a 150-robot team, thereby demonstrating its computationally scalability.

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“…The problem of achieving continuous environment surveillance by using energy-constrained UAVs which are supported by mobile charging stations is researched by Seyedi et al [14]. In the work the routes of all vehicles and the schedule of UAVs charging are planned for minimizing the time between two flights over to areas of interest.…”

Section: State Of the Artmentioning

confidence: 99%

UAV Fleet Based Accident Monitoring Systems with Automatic Battery Replacement Systems: Algorithms for Justifying Composition and Use Planning

Kliushnikov¹,

Fesenko²,

Kharchenko³

et al. 2021

IJSSE

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The existing approaches suggest that justifying robotic monitoring systems composition and use planning are carried out through a set of predefined missions. The paper proposes the algorithm of justifying composition and utilization of monitoring systems for critical infrastructure objects based on the theory of transport systems considering possible types of the monitoring missions. The considered monitoring system comprises a fleet of unmanned aerial vehicles (UAV fleet) and automatic battery replacement systems. The algorithm provides a procedure for defining the monitoring system composition and using the UAV fleet and the automatic battery replacement systems taking into account the requirements for the number of ground objects from which data needs collecting, range, transmission frequency, volume of the transmitted data, and operation time. Methods of research are the following: graph theory, theory of transportation system (methods for solving the traveling salesman problem and multiple traveling salesman problem); mathematical optimization models (evolutionary algorithms), job shop scheduling. The algorithm allows finding an optimal or near optimal compromise solving for composition and utilization of monitoring system for different cases; reducing the time of justifying the monitoring system composition and takes into account the various requirements such as transmission frequency, volume of the transmitted data, and continuous operation time; planning using UAV and automated battery replacement aerial stations (ABRAS) as the parts of transportation system.

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Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations

Cited by 35 publications

References 18 publications

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks

Scalable Coverage Path Planning of Multi-Robot Teams for Monitoring Non-Convex Areas

UAV Fleet Based Accident Monitoring Systems with Automatic Battery Replacement Systems: Algorithms for Justifying Composition and Use Planning

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