Graph rigidity and distributed formation stabilization of multi-vehicle systems

By Reza Olfati-Saber, Richard M. Murray. 2002

417 citations recorded by the source corpus.

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

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Citations in the corpus

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “Motivated by the graph rigidity, research has been conducte in [190]–[194] to drive a group of agents to the desired inter-agent configuration by ensuring that a certain number of edge distances are identical to the desired ones.”
Control and Communication Challenges in Networked Real-Time Systems (2007, background): “(See [52] and [53] for the basics of this theory; see [14], [19], [40], and [ 97 ] for applications to decentralized control of formations of autonomous vehicles.) Research along these lines has also been reported by J. M. Hendrickx, B. D. O. Anderson, and V. D. Blondel and coworkers [54] and [55] w”
Control of Minimally Persistent Formations in the Plane (2009, background): “As for the determination of the actual law, it is common [1, 2, 3, 4, 5, 6, 7, 12, 21] when distance constraints are bidirectional to select a type of Lyapunov function, typically reflecting the distance errors, and to choose the control laws to ensure that the derivative is nonpositive.”
Flocking with obstacle avoidance: cooperation with limited communication in mobile networks (2003, methodology): “We provide examples of ∞ocking for n = 100 agents that avoid m = 6 obstacles and are forced to perform split/rejoin maneuver [ 12 ] and squeezing maneuver.”
Autonomous rigid body attitude synchronization (2007, neutral): “Most existing consensus results are valid for Euclidean state spaces ([12], [16], [17], [25], [26], [41]-[44]), but recent work also considers non-Euclidean spaces ([8][11], [18], [27], [32], [45]), mainly the circle.”
Rigid graph control architectures for autonomous formations (2008, background): “An early examination of formation reshaping is provided in [14], and splitting and merging are addressed in [31].”
Observer-Based Leader-Following Formation Control Using Onboard Sensor Information (2008, methodology): “A different approach is taken and a neighborhood-based control algorithm is used for formation control in [12], [13], and many papers that followed.”
Distributed Control of Robotic Networks (2009, background): “, 2005), formation control using rigidity and persistence theory (Olfati-Saber and Murray, 2002; Baillieul and Suri, 2003; Hendrickx et al., 2007; Krick, 2007; Yu et al., 2009), formation stability (Tanner et al.”
Consensus and Cooperation in Networked Multi-Agent Systems (2007, methodology): “In many scenarios, networked systems can possess a dynamic topology that is time-varying due to node and link failures/creations, packet-loss [83, 33] , asynchronous consensus [34], state-dependence [56], formation reconfiguration [ 69 ], evolution [4], and flocking [77, 66].”
Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control (2014, neutral): “We note that the assumptions we make are diﬀerent than the most common assumptions in formation control, wherein one often assumes that agents are able to measure converge to formations deﬁned through distances (e.g., [27, 63]).”
On the controllability of nearest neighbor interconnections (2004, background): “One class focuses on the effect of interconnection topology on the type of formation equilibrium configurations [1], [2], where it was shown that the rigidity of the interconnection graph plays a crucial role.”
Formation control using range-only measurements (2011, neutral): “…problem in similar settings, control is secured through maintenance of distances, a richer measurement set is required than simply those distances (Cao, Morse, Yu, Anderson, & Dasgupta, 2011; Krick, Broucke, & Francis, 2009; Olfati-Saber & Murray, 2002; Yu, Anderson, Dasgupta, & Fidan, 2009).”
Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms (2009, background): “, 2005), formation control using rigidity and persistence theory (Olfati-Saber and Murray, 2002; Baillieul and Suri, 2003; Hendrickx et al., 2007; Krick, 2007; Yu et al., 2009), formation stability (Tanner et al.”
Coverage control for mobile sensing networks (2002, background): “itnessed a large research eﬀort focused on motion planning and coordination problems for multi-vehicle systems. Issues include geometric patterns [15], [16], formation control [17], [18], [19], [20], [21], and conﬂict avoidance [22], [23]. Algorithms for robotic sensing tasks are presented for examp”