Decentralized simultaneous multi-target exploration using a connected network of multiple robots

Nestmeyer, Thomas; Giordano, Paolo Robuffo; Bülthoff, HH; Franchi, Antonio

doi:10.1007/s10514-016-9578-9

Cited by 51 publications

(39 citation statements)

References 29 publications

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“…In order to cooperatively plan, the robots must be able to share information across the team. In the case where robots have limited communication range, line-of-sight visibility, and operate in a cluttered environment, it can be difficult to maintain connectivity across the team [166].…”

Section: A Target Search and Trackingmentioning

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: A Target Search and Trackingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Decentralized solutions with local sensing and communication in environments with obstacles were treated by Mosteo et al (2008) and Nestmeyer et al (2017). Both approaches dealt with the problem of task assignment for the team of robots under the assumption of a known map, taking into account connectivity constraints in the plan execution.…”

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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“…The resulting generalized connectivity is also able to embed mild formation control constrains and obstacle avoidance if needed. Exogenous control actions coming from human operators or from a distributed target visiting algorithm, like the one presented in [37], can be also included. Using a feedback-force collaborative scheme one or more human operators can interact with the group of robots and immersively feel, through haptics, the connectivity force generated by the group.…”

Section: Group-property Preservation Schemesmentioning

confidence: 99%

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

Franchi

2017

Trends in Control and Decision-Making for Human–Robot Collaboration Systems

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Abstract-In this chapter we show and compare several representative examples of human-collaborative schemes in the control of mobile robots, with a particular emphasis on the aerial robot case. We first provide a simplified yet descriptive model of the robot and its interactions. We then use this model to define a taxonomy that highlights the main aspects of these collaboration schemes, such as: the physical domain of the robots, the degree of autonomy, the force interaction with the operator (e.g., the unilateral versus the bilateral haptic shared control), the near-operation versus the teleoperation, the contact-free versus the physically interactive situation, the use of onboard sensors, and the presence of a time-horizon in the operator reference. We then specialize the proposed taxonomy to the multi-robot case in which we further distinguish the methods depending on their level of centralization, the presence of leader-follower schemes, of formation control schemes, the ability to preserve graph theoretical properties, and to perform cooperative physical interaction. The common denominator of all the examples presented in this chapter is the presence of a human operator in the control loop. The main goal of the chapter is to introduce the reader and provide a firstlevel analysis on the several ways to effectively include human operators in the control of both single and multiple aerial robots and, by extension, of more generic mobile robots.

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Decentralized simultaneous multi-target exploration using a connected network of multiple robots

Cited by 51 publications

References 29 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Distributed multi-robot formation control in dynamic environments

Human-Collaborative Schemes in the Motion Control of Single and Multiple Mobile Robots

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