Flocking with obstacle avoidance: cooperation with limited communication in mobile networks

By Reza Olfati-Saber, Richard M. Murray. 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475), 2003

340 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/340d34a33cc2b8f3c7a48ac3d82785775e9d35fe

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In Computer Networks and Communications

In Mechanical Engineering

Citations in the corpus

A class of attractions/repulsion functions for stable swarm aggregations (2004, background): “For example, in Giulietti et al. (2000) the authors describe formation control strategies for autonomous air vehicles, whereas Balch and Arkin (1998), Desai et al. (2001), Leonard and Fiorelli (2001), Ögren et al. (2001) and Olfati-Saber and Murray (2002) describe different approaches for formation”
A connectivity-preserving flocking algorithm for multi-agent systems based only on position measurements (2009, background): “…rules have been added to the flocking algorithms, such as obstacle avoidance (Khatib 1986; Chang, Shadden, Marsden, and Olfati-Saber 2003; Olfati-Saber and Murray 2003; Tanner 2004) and goal seeking (Leonard and Friorelli 2003; Shi, Wang, and Chu 2006; Su and Wang 2008; Su, Wang and Lin 2009).”
Formation Tracking Control of Unicycle-Type Mobile Robots With Limited Sensing Ranges (2008, methodology): “A nontrivial bump function was used in [24] for obstacle avoidance in flocking cooperation control of multiple agents with limited communication.”
Flocking for multi-agent dynamic systems: algorithms and theory (2006, methodology): “One possible choice is the following bump function introduced in [44]:”
Bilateral Teleoperation of Multiple Mobile Agents: Coordinated Motion and Collision Avoidance (2010, background): “For instance, work in coordination control includes behavioral-based approaches [3], optimization-based techniques [4], and other nature-inspired, such as the nearest neighbor rule [5]; while research on obstacle avoidance has been addressed through the use of potential field [6] and collision avoid”
Consensus and Cooperation in Networked Multi-Agent Systems (2007, background): “In many scenarios, networked systems can possess a dynamic topology that is time-varying due to node and link failures/creations, packet-loss [40], [98], asynchronous consensus [41], state-dependence [64], formation reconfiguration [53], evolution [96], and flocking [19], [99].”
On the controllability of nearest neighbor interconnections (2004, background): “Potential fields have been combined with synchronization control inputs to yield coordinated formation and decentralized flocking and swarming motion [6], [7], [8], [9].”
Flocking of Multi-Agents With a Virtual Leader (2009, methodology): “For example, graph theory [14], [15] and Lyapunov-based approach [16] have been used to investigate consensus (alignment) problem, local attractive/repulsive potential between neighboring agents to deal with separation and cohesion problem [17], [18], and artificial potentials [19]–[21] and gyroscop”
An overview of emerging results in cooperative UAV control (2004, methodology): “For a loosely defined formation such as a flock, reconfiguration may be inherent in the formation flight algorithm [ 22 ].”
Flocking in Fixed and Switching Networks (2007, background): “Recent results in [5], [6] include that the switched linear system, possibly with a switching path constraint, is uniformly stable (and contractive) if and only if the union of an increasing family of linear matrix inequality conditions holds.”
Decentralized policies for geometric pattern formation (2007, background): “Olfati-Saber and Murray [6] and Leonard et al. [7] used potential function theory to prescribe ﬂocking behavior.”