Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment

By Petter Ögren, Edward Fiorelli, Naomi Ehrich Leonard. 2004

1,344 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Mechanical Engineering

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In Computer Vision and Pattern Recognition

In Mechanical Engineering

Citations in the corpus

Collective behavior coordination with predictive mechanisms (2008, background): “Meanwhile, collective predictive protocols are beginning to find their way into engineering areas such as autonomous robot formations, sensor networks, and UAVs [11], [12], [13].”
Consensus Filters for Sensor Networks and Distributed Sensor Fusion (2005, background): “Sensor networks have broad applications in surveillance & monitoring of an environment, collaborative processing of information, and gathering scientific data from spatially distributed sources for environmental modeling and protection [19], [4], [8], [10], [2], [1], [3], [13], [14], [17], [23], [25”
Cooperative Filters and Control for Cooperative Exploration (2010, methodology): “Our method extends the adaptive scheme previously developed in [1] to minimize estimation error based on instantaneous measurements.”
Trajectory tracking for non-holonomic cars: A linear approach to controlled leader-follower formation (2010, background): “Other approaches are based on generalized coordinates [21], graph theory [14], synchronization [22], and artificial potentials [19].”
Avoiding Collisions in Flocks (2010, background): “Models initially intended to mimic the collective motion of bird flocks, fish schools, or colonies of bacteria [1], [7], [10], [16], [23] haverecentlybeen lookedas controlmechanismsprovidingbasic coordination and consensus algorithms for systems of mobile autonomous agents, such as groups of robotic”
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (2009, neutral): “, 2006), swarm aggregation (Gazi and Passino, 2003), gradient climbing (Ögren et al., 2004; Cortés, 2007), cyclic-pursuit (Bruckstein et al.”
Distributed Control of Robotic Networks (2009, positive): “, 2006), swarm aggregation (Gazi and Passino, 2003), gradient climbing (Ögren et al., 2004; Cortés, 2007), cyclic-pursuit (Bruckstein et al.”
Recent Research in Cooperative Control of Multivehicle Systems (2007, background): “A representative body of research in this area is the work of Fiorelli and Leonard, who use the concept of " virtual leaders " that guide the motion of the other vehicles [29] [32].”
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]”
Predictive Output Feedback Control for Networked Control Systems (2014, background): “2248339 together with fruitful applications in industry, such as traffic and communication systems, aviation, and space flight [11], [18], [24], [25], [27]–[29], [33], the insertion of the communication network in the feedback control loop makes the analysis and the design of NCSs complex.”
Underwater acoustic sensor networks: research challenges (2005, background): “In [20], results are reported on the application of the theory in [34] on a fleet of autonomous underwater gliders during the experiment on Monterey Bay in 2003 [4].”
Distributed Kalman Filter with Embedded Consensus Filters (2005, background): “Sensor networks and intelligent arrays of micro-sensors have broad range of applications including information gathering and data fusion for modeling an environment, surveillance, active monitoring of forests & agricultural lands, health-care applications, collaborative information processing, and c”
Tracking and formation control of multiple autonomous agents: A two-level consensus approach (2007, methodology): “For example, the agents may represent vehicles traveling in an environment that possesses a temperature, chemical, or magnetic field, e.g. Li, Farrell, Pang, and Arrieta (2006) and Ögren, Fiorelli, and Leonard (2004).”