Persistent Monitoring with Refueling on a Terrain Using a Team of Aerial and Ground Robots

Maini, Parikshit; Yu, Kevin; Sujit, P. B.; Tokekar, Pratap

doi:10.1109/iros.2018.8593508

Cited by 29 publications

(14 citation statements)

References 13 publications

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“…Most of the work presented in the literature utilize homogeneous type of robots and sensors such as the work presented in [28,40,69,84] which limits the coverage dimensionality. Few papers utilized heterogeneous type of robots and sensors as in [45,48,49,64]. 2.…”

Section: Discussion and Future Research Directionsmentioning

confidence: 99%

“…The work in [44] divided the area of interest into big voronoi cells based on the used number of agents. Furthermore, the work in [45] used different representation for the region of interest such as a 1.5D terrain where the altitude value for every x along a single dimension is returned by a function interpreting the 1.5D terrain. This process utilizes fixed altitude paths and the chain visibility property of 1.5D terrains to generate visibility segments that are then used to extract viewpoints for each agent.…”

Section: Model-basedmentioning

confidence: 99%

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A survey on multi-robot coverage path planning for model reconstruction and mapping

Taha²,

et al. 2019

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There has been an increasing interest in researching, developing and deploying multi-robot systems. This has been driven mainly by: the maturity of the practical deployment of a single-robot system and its ability to solve some of the most challenging tasks. Coverage path planning (CPP) is one of the active research topics that could benefit greatly from multi-robot systems. In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions. We classified the topics into: viewpoints generation approaches; coverage planning strategies; coordination and decision-making processes; communication mechanism and mapping approaches. This paper provides a detailed analysis and comparison of the recent research work in this area, and concludes with a critical analysis of the field, and future research perspectives.

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Section: Discussion and Future Research Directionsmentioning

confidence: 99%

Section: Model-basedmentioning

confidence: 99%

A survey on multi-robot coverage path planning for model reconstruction and mapping

Taha²,

et al. 2019

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show abstract

“…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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“…Visual monitoring and surveillance using UAVs has been an active area of research over the past decade. Various lines of work have addressed aspects related to target monitoring with differential priorities [9], multi-robot surveillance [1], [3], [10], [11], persistent monitoring [3], [10], [12], mission planning while addressing robot kinematics [13], [14] and so on. There has been limited effort towards addressing the visual monitoring problem in the presence of visibility restrictions due to terrain features.…”

Section: Related Workmentioning

confidence: 99%

“…The problem of line-of-sight coverage within a polygon by a watchman (or a robot), known as the Watchman Routing Problem (WRP), is also well studied in the literature ( [18] and the references within). Variations of WRP, including the homogeneous [19]- [21] and heterogeneous [10], [11] multiple-robot versions (both within restricted sub-domains) have also been studied extensively. Recently, Maini et.…”

Section: Related Workmentioning

confidence: 99%

Visual Monitoring for Multiple Points of Interest on a 2.5D Terrain using a UAV with Limited Field-of-View Constraint

Maini¹,

PB²,

Tokekar³

2019

Preprint

Self Cite

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Varying terrain conditions and limited field-ofview restricts the visibility of aerial robots while performing visual monitoring operations. In this paper, we study the multipoint monitoring problem on a 2.5D terrain using an unmanned aerial vehicle (UAV) with limited camera field-of-view. This problem is NP-Hard and hence we develop a two phase strategy to compute an approximate tour for the UAV. In the first phase, visibility regions on the flight plane are determined for each point of interest. In the second phase, a tour for the UAV to visit each visibility region is computed by casting the problem as an instance of the Traveling Salesman Problem with Neighbourhoods (TSPN). We design a constant-factor approximation algorithm for the TSPN instance. Further, we reduce the TSPN instance to an instance of the Generalized Traveling Salesman Problem (GTSP) and devise an ILP formulation to solve it. We present a comparative evaluation of solutions computed using a branch-and-cut implementation and an off-the-shelf GTSP tool -GLNS, while varying the points of interest density, sampling resolution and camera field-of-view. We also show results from preliminary field experiments.

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Persistent Monitoring with Refueling on a Terrain Using a Team of Aerial and Ground Robots

Cited by 29 publications

References 13 publications

A survey on multi-robot coverage path planning for model reconstruction and mapping

A survey on multi-robot coverage path planning for model reconstruction and mapping

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

Visual Monitoring for Multiple Points of Interest on a 2.5D Terrain using a UAV with Limited Field-of-View Constraint

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