Cooperative coverage for surveillance of 3D structures

Adaldo, Antonio; Mansouri, Sina Sharif; Kanellakis, Christoforos; Dimarogonas, Dimos V.; Johansson, Karl Henrik; Nikolakopoulos, George

doi:10.1109/iros.2017.8205999

Cited by 23 publications

(15 citation statements)

References 26 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is challenging to simultaneously achieve the objectives listed above, especially when the object's surfaces are nonplanar, complex in shape and unconnected from each other [34]. There exists some methods for area decomposition, partitioning and allocation, including convex decomposition methods [35], grid graph bisection method [36], and gradient based optimizations [37]. However, these methods only focus on solving part of the APA problem considered in this work.…”

Section: F Multi-air Coverage Analysismentioning

confidence: 99%

A framework for multi-robot coverage analysis of large and complex structures

Dai

Hassan

Sun³

et al. 2021

J Intell Manuf

View full text Add to dashboard Cite

Coverage analysis is essential for many coverage tasks (e.g., robotic grit-blasting, painting and surface cleaning) performed by Autonomous Industrial Robots (AIRs). Coverage analysis enables (1) the performance evaluation (e.g., coverage rate and operation efficiency) of AIRs for a coverage task, and (2) the configuration design of a multi-AIR system (e.g., decision on the number of AIRs to be used). Multi-AIR coverage analysis of large and complex structures involves addressing various problems. Thus, a framework is presented in this paper that incorporates various modules (e.g., AIR reachability, AIR base placement, collision avoidance, and area partitioning and allocation) for appropriately addressing the associated problems. The modules within the framework provide the flexibility of utilizing different methods and algorithms, depending on the requirements of the target application. The framework is tested and validated by extensive analyses of 10 different scenarios with up-to 10 AIRs.

show abstract

Section: F Multi-air Coverage Analysismentioning

confidence: 99%

A framework for multi-robot coverage analysis of large and complex structures

Dai

Hassan

Sun³

et al. 2021

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…The Ulusoy's partitioning Algorithm (UA) is modified to add energy demands (energy, coverage) and to re-plan path utilizing the remaining energy capacity. The work in [1] proposed a control algorithm that forms the sensors to a Voronoi tessellation while score function of the coverage is increasing. The proposed work utilizes the Voronoi tessellation defining a coverage score function as a measure of the quality of the surveillance attained by the sensor network.…”

Section: Geometric Based Approachesmentioning

confidence: 99%

“…The main factors that could effect the performance of a multi-robot CPP approach include: information sharing (whether it is centralized, decentralized, or distributed), viewpoints generation, path generation, task allocation, reacting to dynamic changes (collision avoidance), and model reconstruction or mapping approach. Majority of existing approaches in literature attempt to: reduce the computational cost (time need to compute and execute the CPP mission) [61,84], avoid collision internally between team of robots and externally with the structure or environment [1,7,39], and gather information with sufficient resolution for mapping and reconstruction [69,80].…”

Section: Introductionmentioning

confidence: 99%

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

Taha²,

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…Therefore, many efforts have been devoted to developing control algorithms for such missions. Examples can be found in several different applications, such as logistic tasks in industrial warehouses, where Automated Guided Vehicles (AGV) transport and handle products [1]; search and rescue missions, where drones cooperatively search large portions of territory to aid people in distress [2,3]; and, surveillance, where UAVs gather images and information about specific targets or areas [4,5], etc.…”

Section: Introductionmentioning

confidence: 99%

Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

et al. 2021

View full text Add to dashboard Cite

In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.

show abstract

Cooperative coverage for surveillance of 3D structures

Cited by 23 publications

References 26 publications

A framework for multi-robot coverage analysis of large and complex structures

A framework for multi-robot coverage analysis of large and complex structures

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

Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

Contact Info

Product

Resources

About