A vision-based formation control framework

By Aveek K. Das, Rafael B. Fierro, Vijay R. Kumar, J. Ostrowski, John R. Spletzer, Camillo Jose Taylor. IEEE Trans. Robotics Autom., 2002

1,195 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/40e65a95350802dbf754701677dcf0f7afe65faa

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Citations in the corpus

On the controllability of nearest neighbor interconnections (2004, methodology): “Leader-follower local control laws with vision-based feedback were employed in [16] to stabilize a formation to a particular shape.”
Formation control: a review and a new consideration (2005, background): “A similar ideal was presented in [38], [39], where the shape of the formation is expressed in shape coordinates.”
Region-based shape control for a swarm of robots (2009, positive): “In leader-following approach (Consolini, Morbidi, Prattichizzo, & Tosques, 2008; Das et al., 2002; Desai, Kumar, & Ostrowski, 2001; Dimarogonas, Egerstedt, & Kyriakopoulos, 2006; Fredslund & Mataric, 2002; Ji, Ferrari-Trecate, Egerstedt, & Buffa, 2008; Ogren, Egerstedt, & Hu, 2002; Wang, 1991), the…”
Cooperative Multi-agent Control Using Deep Reinforcement Learning (2017, background): “Multi-agent control has also been studied in extensive detail from the dynamical systems perspective in problems like formation control [10], coverage control [9], and consensus [27].”
Optimal scheduling for refueling multiple autonomous aerial vehicles (2006, methodology): “Other works include using Lie group for abstraction and control for robot teams [29] and vision-based mobile robots formation control [30].”
Research Advance in Swarm Robotics (2013, background): “[144] proposed an approach that switches between several controllers, depending on the state of the robot for obstacle avoidance.”
Sliding-Mode Formation Control for Cooperative Autonomous Mobile Robots (2008, methodology): “Various control strategies for mobile robot formations have been reported in the literature, including behaviorbased methods [3], [4], virtual structure techniques [2], [5], and leader–follower schemes [1], [6]–[9].”
Trajectory tracking for non-holonomic cars: A linear approach to controlled leader-follower formation (2010, background): “In addition, it is well known that in practical situations one has to deal with uncertainty, nonlinearities and external disturbances acting on the robots [5], [6] In this article, we present a decentralized formation controller for a team of two autonomous mobile robots with non-holonomic constrain”
Distributed tracking control of leader-follower multi-agent systems under noisy measurement (2011, methodology): “The leader-follower approach has been widely used in many practical applications such as formation control in robotic systems (Wang P. K. C. (1991); Das , Fierro , &Kumar (2002)), unmanned aerial vehicle (UAV) formation (Vanek , Peni , et al. (2005); Anderson , Fidan , et al. (2008)), target…”
Observer-Based Leader-Following Formation Control Using Onboard Sensor Information (2008, methodology): “In [1], leader-following formation controls based on a unicycle model are proposed.”
A Differential Game Approach to Formation Control (2008, background): “The leader-follower structure is constructed by a string of chain where each robot follows one ( model) or two robots ( model) [12]–[14].”
Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics (2013, methodology): “2181513 [1]–[4], virtual structure method [5], behavioral approach [6], [7], graph theory-based method [8]–[11], artificial potential mechanism [12]–[14], and so on.”
Bounded Controllers for Formation Stabilization of Mobile Agents With Limited Sensing Ranges (2007, background): “Three popular approaches to formation control are leader-following (e.g. [ 1 ] and [2]), behavioral (e.g.”
Modeling Swarm Robotic Systems: a Case Study in Collaborative Distributed Manipulation (2004, methodology): “(7) for different delay durations and different probabilities of encountering an obstacle in the 40 cm arena (the greater the swarm size, the larger pa is).”