Vision-Based Distributed Formation Control Without an External Positioning System

Montijano, Eduardo; Cristofalo, Eric; Zhou, Dingjiang; Schwager, Mac; Sagüés, Carlos

doi:10.1109/tro.2016.2523542

Cited by 142 publications

(92 citation statements)

References 40 publications

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“…A recent major breakthrough in this area is the use of visualinertial odometry (VIO) [210] for real-time state estimation and feedback control. On-board camera sensors can also be used to localize other members of the swarm [220]- [225]. This can be used to enable distributed formation control without the need for any explicit communication between agents.…”

Section: Pose and State Estimationmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: Pose and State Estimationmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Simultaneously, on the formation control layer, we implement the bearing-only control law proposed earlier in Problem 1 in each agent's local frame to achieve the desired formation. Note that this two-layer control strategy was also used in distance-based formation control problems with different setups [5], [6], [21], [47].…”

Section: B Proposed Control Strategymentioning

confidence: 99%

“…Unlike [5], [6], [21], [47] where the interaction graphs are assumed to be undirected, the alignment (35) is performed in a directed graph G built up via a Henneberg construction, i.e., a rooted directed graph with a root at vertex v 1 . This setup leads to a different result.…”

Section: B Proposed Control Strategymentioning

confidence: 99%

“…Consider a small quadcopter, the relative bearing, which is the unit vector obtained from a relative position vector by normalizing its length, can be acquired from the onboard cameras thanks to vision-based techniques [6], [11]. Since the camera is a passive sensor, in applications where exchanging signals is prohibited, bearing-only control is preferred [12].…”

Section: Introductionmentioning

confidence: 99%

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Bearing-Based Formation Control of A Group of Agents with Leader-First Follower Structure

Trinh

Zhao

Sun

et al. 2018

IEEE Trans. Automat. Contr.

101

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“…We show experiments with a team of quadrotors. In terms of the sensing model, Franchi et al (2012) considered relative bearing measurements, Oh and Ahn (2011) considered inter-robot distances and Mostagh et al (2009) and Montijano et al (2016) employed explicit vision measurements to estimate the relative positions of neighboring robots and reach the formation. A common assumption in these approaches is the lack of obstacles in the environment, focusing on the design of low-level controllers for each robot to reach the desired formation pattern.…”

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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Vision-Based Distributed Formation Control Without an External Positioning System

Cited by 142 publications

References 40 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Bearing-Based Formation Control of A Group of Agents with Leader-First Follower Structure

Distributed multi-robot formation control in dynamic environments

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