Multi-robot formation control and object transport in dynamic environments via constrained optimization

Alonso–Mora, Javier; Baker, Stuart; Rus, Daniela

doi:10.1177/0278364917719333

Cited by 231 publications

(124 citation statements)

References 57 publications

Supporting

Mentioning

124

Contrasting

Order By: Relevance

“…The assignment of robots to the target positions in the formation is another optimization problem that was solved with a centralized algorithm by Turpin et al (2014) or with a distributed algorithm, albeit in environments without obstacles, by Montijano and Mosteo (2014) and Morgan et al (2016). Building upon the centralized, yet online, method by Alonso-Mora et al (2017), we propose an optimization and consensus based approach to reconfigure the formation in dynamic environments, which is distributed and online.…”

Section: Related Workmentioning

confidence: 99%

“…To avoid deadlocks, our method can be employed in combination with a global planner, in a manner similar to the work on centralized formation control by Alonso-Mora et al (2017). In this work we have introduced an additional step in the method, where robots compute the preferred direction of motion.…”

Section: Contributionmentioning

confidence: 99%

“…The method is general and could be applied to alternative definitions, such as for a team of mobile manipulators carrying a rigid object, as shown by Alonso-Mora et al (2017) for centralized formation control.…”

Section: Definition Of the Formationmentioning

confidence: 99%

“…We test the distributed algorithm described in this paper in two scenarios previously introduced in Alonso- Mora et al (2017) for the centralized case. This provides a direct comparison and evaluation.…”

Section: Multi-drone Formation Controlmentioning

confidence: 99%

“…In this work we leverage efficient constrained optimization, multi-robot consensus and geometric reasoning to achieve distributed formation control in environments with static and moving obstacles. In contrast to our previous work (Alonso-Mora et al 2017), we consider the case where robots are no more centrally controlled, but instead have a limited field of view and communicate with their immediate neighbors to coordinate.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Distributed multi-robot formation control in dynamic environments

et al. 2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Definition Of the Formationmentioning

confidence: 99%

Section: Multi-drone Formation Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Distributed multi-robot formation control in dynamic environments

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

Triangular formation control with mixed distance and bearing measurements under limited field of view constraints

Zhao

Yang

Pan

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article addresses the problem of triangular formation control for a three‐agent group with different measuring capabilities: agent 1 can measure local bearings but with limited field of view (FOV), and agents 2 and 3 can only obtain distance measurements. For agent 1, an orientation controller is first designed to steer its heading to the expected direction. Furthermore, a type of switching control law utilizing pure bearing measurements is proposed to reach the desired angle with respect to the other two agents. Concurrently, it is guaranteed that the flip ambiguity of the formation can be excluded. For agents 2 and 3, the extended distance‐based control algorithm is proposed by invoking the so‐called signed area strategy to determine their actuation. With this control scheme, it is proven that the triangular formation is stabilized globally even in the presence of FOV constraints. The effectiveness is validated through numerical simulations and physical experiments on three nano quadrotors.

show abstract

Cooperative target fencing of multiple double‐integrator systems with connectivity preservation

Pan,

Chen

2024

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, we study the cooperative target‐fencing problem of multiple double‐integrator systems over a state‐dependent communication network. We propose a distributed control law to asymptotically fence a moving target while achieving both collision avoidance and connectivity preservation of the vehicles. In particular, a distributed observer is employed by each vehicle to estimate the position and the velocity of the target. Compared with the existing results, our approach can accommodate a larger class of target's trajectories. Furthermore, our control law is more practical since we do not require all vehicles to know the trajectory of the target. The effectiveness of our approach is illustrated by a numerical example.

show abstract

Multi-robot formation control and object transport in dynamic environments via constrained optimization

Abstract: We present a constrained optimization method for multi-robot formation control in dynamic environments

Cited by 231 publications

References 57 publications

Distributed multi-robot formation control in dynamic environments

Distributed multi-robot formation control in dynamic environments

Triangular formation control with mixed distance and bearing measurements under limited field of view constraints

Cooperative target fencing of multiple double‐integrator systems with connectivity preservation

Contact Info

Product

Resources

About