Coverage control for mobile sensing networks

By Jorge Cortés, Sonia Martínez, Timur Karatas, Francesco Bullo. 2002

2,906 citations recorded by the source corpus.

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks, where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Network Topology and Communication Data Rate for Consensusability of Discrete-Time Multi-Agent Systems (2011, background): “…interest in various scienti ﬁ c communities due to broad applications in many areas including formation control [11], [42], distributed sensor networks [7], [8], ﬂ ocking [30], [32], distributed computation [25], and synchronization of cou-Manuscript pled chaotic oscillators [2], [9], [39].”
A Distributed Algorithm for Proportional Task Allocation in Networks of Mobile Agents (2011, background): “The following definitions and notions on Voronoi partitions and Delaunay graphs are taken from [6], [7].”
Belief consensus and distributed hypothesis testing in sensor networks (2006, neutral): “signal processing, and control theory [20, 7, 10, 11, 5, 1, 6, 17, 14, 24, 28].”
A Survey on Aerial Swarm Robotics (2018, background): “This approach was introduced in [114] for sensor coverage, and generalized in [115] and [116] to cover learning (of the task distribution) and decentralized information sharing.”
Bounded Controllers for Formation Stabilization of Mobile Agents With Limited Sensing Ranges (2007, background): “An interesting work addressing geometric formation based on Voronoi partition optimization is given in [16] but the final arrangement of the agents cannot be foretold due to locality of Lloy’s algorithm.”
Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems (2011, neutral): “For example, in (Howard et al., 2002; Cortes et al., 2004; Lekien & Leonard, 2009), coverage is more a static concept.”
Stabilization of Planar Collective Motion With Limited Communication (2008, background): “The various applications, which impose diverse operational constraints and performance requirements, include formation control of unmanned aerial vehicles (UAVs) [1], [2] and spacecraft [3], cooperative robotics [4] [5], and sensor networks [6], [7].”
Control and Communication Challenges in Networked Real-Time Systems (2007, background): “In [ 31 ], the problem of distributed sensing for robotic agents acting as mobile tunable sensors is considered.”
Motion Coordination with Distributed Information (2007, positive): “flocking [15]‐[17], self-assembly [18], swarm aggregation [19], gradient climbing [20], deployment and task allocation [ 21 ]‐[24], rendezvous [25]‐[28], cyclic pursuit [29], [30], vehicle routing [31], and consensus [32]‐[34].”
Awareness coverage control over large scale domains with intermittent communications (2008, methodology): “For sensor redeployment problems, in [6], the authors apply a dynamic Voronoi partition, which is the dynamic version of the Lloyd algorithm [7].”
Ultrafast consensus in small-world networks (2005, background): “Some examples include power networks, metabolic and gene networks [17], coauthorship network of scientists [27], biological network of oscillators [44], [19], [18], [24], [39], economic networks [15], sensor networks [10], [30], [7], [38], swarms of networked unmanned autonomous vehicles (UAVs) [32]”
Control Barrier Functions: Theory and Applications (2019, background): “We can define a measure of the coverage quality by [56]:”
Advances in Studies and Applications of Centroidal Voronoi Tessellations (2010, background): “CVT-based techniques have proven to be very useful in diverse applications, including but not limited to, image processing and analysis [20, 26, 38, 51, 57, 102, 121], vector quantization and data analysis [50,55,56,58–60,78], resource optimization [31], optimal placement of sensors and actuators fo”
Convergence of the Lloyd Algorithm for Computing Centroidal Voronoi Tessellations (2006, background): “…applications that include art design, astronomy, clustering, geometric modeling, image and data analysis, resource optimization, quadrature design, sensor networks, and numerical solution of partial diﬀerential equations [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 17, 15, 26, 29, 30, 31, 39, 44, 45].”
Esaim: Control, Optimisation and Calculus of Variations Spatially-distributed Coverage Optimization and Control with Limited-range Interactions (positive): “In our previous work [ 9 ,10], we studied distributed algorithms for deployment and optimal coverage problems using tools from computational geometry, nonsmooth analysis and geometric optimization.”
Effective Coverage Control for Mobile Sensor Networks With Guaranteed Collision Avoidance (2007, methodology): “On the other hand, any other Voronoi-based approach to coverage (recall that the Voronoi-based method of [9] is only for redeployment problems and not full domain dynamic coverage) requires , where is the number of Voronoi partitions which is also equal to the number of vehicles in this paper [20].”