Image and animation display with multiple mobile robots

Alonso–Mora, Javier; Breitenmoser, Andreas; Rufli, Martin; Siegwart, Roland; Beardsley, Paul

doi:10.1177/0278364912442095

Cited by 87 publications

(57 citation statements)

References 44 publications

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“…The presented system extends our work [1] for image and animation display in 2D to flying displays in 3D. The system consists of a set of N aerial vehicles, a motion tracking system, and a central computer that wirelessly sends motion commands to the robots.…”

Section: System Overviewmentioning

confidence: 99%

“…This method is equivalent to the Voronoi coverage method used in [1] to distribute N robots in a planar image.…”

Section: Iterative Optimizationmentioning

confidence: 99%

“…In this paper we present a method to display visual content with a swarm of aerial vehicles. In [1] a method and setup to display images and animations with multiple ground vehicles was presented. We build on that work and extend it to a 3D display with aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Object and animation display with multiple aerial vehicles

Alonso–Mora

Schoch

Breitenmoser

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a fully automated method to display objects and animations in 3D with a group of aerial vehicles. The system input is a single object or an animation (sequence of objects) created by an artist. The first stage is to generate physical goal configurations and robot colors to represent the objects with the available number of robots. The run-time system includes algorithms for goal assignment, path planning and local reciprocal collision avoidance that guarantee smooth, fast and oscillation-free motion. The presented algorithms are tested in simulations and verified with real quadrotor helicopters and scale to large robot swarms.

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Section: System Overviewmentioning

confidence: 99%

“…This method is equivalent to the Voronoi coverage method used in [1] to distribute N robots in a planar image.…”

Section: Iterative Optimizationmentioning

confidence: 99%

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Object and animation display with multiple aerial vehicles

Alonso–Mora

Schoch

Breitenmoser

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

Self Cite

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“…The restriction of having a central planner can be easily lifted using nearest neighbors information [7]. These type of solutions usually rely on consensus-type controllers, such as [8], [9], or optimization methods [10], and assume the environment is obstacle-free. Compared to them, we exploit consensus algorithms to obtain all the necessary information to compute the optimal formation in the presence of obstacles.…”

Section: A Related Workmentioning

confidence: 99%

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Alonso–Mora¹,

Montijano

Schwager

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-This paper presents a distributed method for navigating a team of robots in formation in 2D and 3D environments with static and dynamic obstacles. The robots are assumed to have a reduced communication and visibility radius and share information with their neighbors. Via distributed consensus the robots compute (a) the convex hull of the robot positions and (b) the largest convex region within free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation within this convex neighborhood of the robots. Reconfiguration is allowed, when required, by considering a set of target formations. The robots navigate towards the target collision-free formation with individual local planners that account for their dynamics. The approach is efficient and scalable with the number of robots and performs well in simulations with up to sixteen quadrotors. I. INTRODUCTIONMuti-robot teams can be employed for various tasks, such as surveillance, inspection, or automated factories. In these scenarios, robots may be required to navigate in formation, for example for maintaining a communication network, for collaboratively handling an object, for surveilling an area or to improve navigation.Multi-robot navigation in formation has received extensive attention in the past, with many works considering obstaclefree scenarios. In [1] we leveraged efficient optimization techniques, namely quadratic programming, semi-definite programming and (non-linear) sequential quadratic programming to devise a centralized method for local navigation in formation. These techniques provided good computational efficiency, local guarantees and generality, and were employed at different stages of the method for local motion planning in formation among static and dynamic obstacles, albeit centralized.In this work we combine the optimization concepts presented in [1] with distributed consensus and geometric reasoning to achieve similar results in a distributed schema, where robots are no more centrally controlled, but instead have a limited field of view, and communicate with their immediate neighbors.

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“…Instead of the traditional view of guidance of multi-agent systems which deals with an indexed collection of agents, we adopt an Eulerian view in this paper as we control the swarm density distribution of a large number of index-free agents over disjoint bins [9]- [14]. Reconfiguration of such large swarms is achieved using probabilistic swarm guidance using inhomogeneous Markov chains (PSG) [15].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic guidance of distributed systems using sequential convex programming

Morgan

Subramanian

Bandyopadhyay

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, we integrate, implement, and validate formation flying algorithms for large number of agents using probabilistic swarm guidance with inhomogeneous Markov chains and model predictive control with sequential convex programming. Using an inhomogeneous Markov chain, each agent determines its target position during each time step in a statistically independent manner while the swarm converges to the desired formation. Moreover, the swarm is robust to external disturbances or damages to the formation. An optimal control problem is formulated to ensure that the agents reach the target positions while avoiding collisions. This problem is solved using sequential convex programming to determine optimal, collisionfree trajectories and model predictive control is implemented to update these trajectories as new state information becomes available. Finally, we validate the probabilistic swarm guidance and model predictive control algorithms using the formation flying testbed.

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Image and animation display with multiple mobile robots

Cited by 87 publications

References 44 publications

Object and animation display with multiple aerial vehicles

Object and animation display with multiple aerial vehicles

Distributed multi-robot formation control among obstacles: A geometric and optimization approach with consensus

Probabilistic guidance of distributed systems using sequential convex programming

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