Decentralized feedback controllers for multi-agent teams in environments with obstacles

Ayanian, Nora; Kumar, Vijay

doi:10.1109/robot.2008.4543490

Cited by 31 publications

(21 citation statements)

References 23 publications

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“…These techniques include using a set of reactive behaviors (Balch and Arkin 1998), potential fields (Balch and Hybinette 2000;Sabattini et al 2011), abstractions (Michael et al 2008;Ayanian and Kumar 2010a), decentralized feedback laws with graph theory (Desai et al 2001), proximity constraints (Ayanian and Kumar 2010b) and stochastic planning (Urcola et al 2017), to name a few. In contrast, our method automatically optimizes for the formation parameters natively in three-dimensional dynamic environments.…”

Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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This paper presents a distributed method for formation control of a homogeneous team of aerial or ground mobile robots navigating in environments with static and dynamic obstacles. Each robot in the team has a finite communication and visibility radius and shares information with its neighbors to coordinate. Our approach leverages both constrained optimization and multi-robot consensus to compute the parameters of the multi-robot formation. This ensures that the robots make progress and avoid collisions with static and moving obstacles. In particular, via distributed consensus, the robots compute (a) the convex hull of the robot positions, (b) the desired direction of movement and (c) a large convex region embedded in the four dimensional position-time free space. The robots then compute, via sequential convex programming, the locally optimal parameters for the formation to remain within the convex neighborhood of the robots. The method allows for reconfiguration. Each robot then navigates towards its assigned position in the target collision-free formation via an individual controller that accounts for its dynamics. This approach is efficient and scalable with the number of robots. We present an extensive evaluation of the communication requirements and verify the method in simulations with up to sixteen quadrotors. Lastly, we present experiments with four real quadrotors flying in formation in an environment with one moving human.

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Section: Related Workmentioning

confidence: 99%

Distributed multi-robot formation control in dynamic environments

et al. 2018

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“…Ayanian and Kumar [1], Desaraju and How [12], and Bhattacharya et al [7] developed CPP planners that could plan for systems where multiple teams form and persist for significant durations. Ayanian and Kumar [1] solved problems where robots must remain in close proximity to the other robots in its team by searching the prepares graph of controllers in a sequential composition framework [8], but this algorithm did not scale well to larger numbers of robots.…”

Section: Prior Workmentioning

confidence: 99%

Constraint Manifold Subsearch for multirobot path planning with cooperative tasks

Wagner

Kim

Urban³

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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The cooperative path planning problem seeks to determine a path for a group of robots which form temporary teams to perform tasks that require multiple robots. The multi-scale effects of simultaneously coordinating many robots distributed across the workspace while also tightly coordinating robots in cooperative teams increases the difficulty of planning. This paper describes a new approach to cooperative path planning called Constraint Manifold Subsearch (CMS). CMS builds upon M*, a high performance multirobot path planning algorithm, by modifying the search space to restrict teams of robots performing a task to the constraint manifold of the task. CMS can find optimal solutions to the cooperative path planning problem, or near optimal solutions to problems involving large numbers of robots.

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“…3 shows a MATLAB simulation of the automaton synthesized for Example 1; a video accompanies this paper. The naive controllers used for simulating motion for each robot set the robot's velocity towards the centroid of the next region -in future work, these will be replaced in physical experiments with atomic controllers such as those constructed in [2].…”

Section: Simulationsmentioning

confidence: 99%

“…The authors provided a technique for constructing atomic controllers that guarantee collision-avoidance and deadlock prevention for teams of robots. This work combined the production of high-level control in [12] with the low-level controller synthesis described in [2]. The atomic controllers described guide multiple robots to a goal set while avoiding collisions with obstacles and other robots, and can be reused to accommodate many different high-level tasks in the same workspace.…”

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confidence: 99%

Synthesis for multi-robot controllers with interleaved motion

Raman

Kress-Gazit

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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This paper addresses the problem of designing control schemes for teams of robots engaged in complex highlevel tasks. It presents a method for automatically creating hybrid controllers that ensure that a team of possibly heterogeneous robots satisfies a user-defined high-level task. The proposed approach relaxes constraints on the simultaneous and interleaved motion of the robots, while maintaining constraints on their relative location to guarantee collision-avoidance and prevent deadlock. The approach is demonstrated in the context of a team of robots engaged in sorting objects for recycling.

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Decentralized feedback controllers for multi-agent teams in environments with obstacles

Cited by 31 publications

References 23 publications

Distributed multi-robot formation control in dynamic environments

Distributed multi-robot formation control in dynamic environments

Constraint Manifold Subsearch for multirobot path planning with cooperative tasks

Synthesis for multi-robot controllers with interleaved motion

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