Distributed area coverage using robot flocks

Cheng, Ke; Wang, Yi; Dasgupta, Prithviraj

doi:10.1109/nabic.2009.5393461

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…New practices such as geofencing, which designate specific "nofly" zones to restrict aerial vehicles from causing unintended damage to nearby objects and humans [25] have been implemented. Previous task planning and coverage methods such as [20], [26], [27] do not present the ability to account for such discontinuities in the environment.…”

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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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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“…Other authors, as in Cheng & Dasgupta (2010); Cheng et al (2009), address the area coverage problem with multiple robots using a team formation technique, i.e., a formation control instead of patrolling motion coordination.…”

Section: Related Workmentioning

confidence: 99%

Distributed Approach for Coverage and Patrolling Missions with a Team of Heterogeneous Aerial Robots under Communication Constraints

Acevedo

Arrue

Maza

et al. 2013

International Journal of Advanced Robotic Systems

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Using aerial robots in area coverage applications is an emerging topic. These applications need a coverage path planning algorithm and a coordinated patrolling plan. This paper proposes a distributed approach to coordinate a team of heterogeneous UAVs cooperating efficiently in patrolling missions around irregular areas, with low communication ranges and memory storage requirements. Hence it can be used with small-scale UAVs with limited and different capabilities. The presented system uses a modular architecture and solves the problem by dividing the area between all the robots according to their capabilities. Each aerial robot performs a decomposition based algorithm to create covering paths and a 'one-to-one' coordination strategy to decide the path segment to patrol. The system is decentralized and fault-tolerant. It ensures a finite time to share information between all the robots and guarantees convergence to the desired steady state, based on the maximal minimum frequency criteria. A set of simulations with a team of quad-rotors is used to validate the approach.

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Multi-Robot Task Allocation for Performing Cooperative Foraging Tasks in an Initially Unknown Environment

Dasgupta¹

2011

Studies in Computational Intelligence

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Distributed area coverage using robot flocks

Cited by 11 publications

References 9 publications

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

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

Distributed Approach for Coverage and Patrolling Missions with a Team of Heterogeneous Aerial Robots under Communication Constraints

Multi-Robot Task Allocation for Performing Cooperative Foraging Tasks in an Initially Unknown Environment

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