Communication-Free Multi-Agent Control Under Local Temporal Tasks and Relative-Distance Constraints

Guo, Meng; Tůmová, Jana; Dimarogonas, Dimos V.

doi:10.1109/tac.2016.2527731

Cited by 31 publications

(36 citation statements)

References 31 publications

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“…The main difficulty lies in the limited communication range for both source and relay robots, meaning that both data transfer and coordination are only possible when two robots are within each other's communication range. As discussed in Section I, instead of imposing all-time connectivity as in most related work [14], [16], [21]- [23], we propose here a distributed online coordination scheme where the communication network is allowed to become disconnected.…”

Section: B Coordination Of Intermittent Meeting-eventsmentioning

confidence: 99%

“…In most of these problems, the property of interest is connectivity of the communication network as this allows reliable delivery of information between any pair of robots. Approaches that ensure connectivity for all time either maintain all initial communication links between the robots provided that the initial communication network is connected [14], [19]- [21], or allow for addition and removal of communication links while ensuring that the connectivity requirement is not violated [15]- [18], [22], [23]. Realistic communication models have recently been proposed in [24]- [26] that take into account path loss, shadowing, and multipath fading.…”

Section: Introductionmentioning

confidence: 99%

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Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication

Guo

Zavlanos

2018

IEEE Trans. Robot.

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We consider a team of heterogeneous robots which are deployed within a common workspace to gather different types of data. The robots have different roles due to different capabilities: some gather data from the workspace (source robots) and others receive data from source robots and upload them to a data center (relay robots). The data-gathering tasks are specified locally to each source robot as high-level Linear Temporal Logic (LTL) formulas, that capture the different types of data that need to be gathered at different regions of interest. All robots have a limited buffer to store the data. Thus the data gathered by source robots should be transferred to relay robots before their buffers overflow, respecting at the same time limited communication range for all robots. The main contribution of this work is a distributed motion coordination and intermittent communication scheme that guarantees the satisfaction of all local tasks, while obeying the above constraints. The robot motion and inter-robot communication are closely coupled and coordinated during run time by scheduling intermittent meeting events to facilitate the local plan execution. We present both numerical simulations and experimental studies to demonstrate the advantages of the proposed method over existing approaches that predominantly require all-time network connectivity. of data-gathering actions. The work in [34] considers a single robot transferring data between locations. The proposed approach minimizes the time interval between two consecutive data-uploading time instants. But it does not explicitly model the evolution of the robot's buffer or the inter-robot communication. Similar buffer constraints are considered in [35] for multi-robot frontier-based exploration. However locallyassigned data-gathering tasks described by LTL formulas are not considered there, nor are communication constraints. Another related area is temporal logic task planning under resource constraints. The work in [36] considers a global surveillance task performed by multiple aerial vehicles subject to battery charging constraints. The multi-vehicle routing problem considered in [37] proposes a solution based on Mixed-Integer Linear Programming (MILP), which can potentially be extended to include resource constraints.The main contribution of this work lies in the development of an online distributed framework that jointly controls local data-gathering tasks and data transfer communication events, so that the buffers at every robot never overflow. The proposed framework guarantees the satisfaction of all local tasks specified as LTL formulas, without imposing all-time connectivity on the communication network. The efficiency of the proposed framework compared to a centralized approach and two static approaches is demonstrated via numerical simulations and experimental studies. To the best of our knowledge, this is the first distributed data-gathering framework under intermittent communication that is also online. This work is built on preliminary results presented in...

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Section: B Coordination Of Intermittent Meeting-eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication

Guo

Zavlanos

2018

IEEE Trans. Robot.

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“…2) Cost: Motivated by [17], the cost of the path P(π) is defined as the (estimated) distance travelled by the group of agents.…”

Section: Reward and Cost Of A Pathmentioning

confidence: 99%

Time-constrained multi-agent task scheduling based on prescribed performance control

Dimarogonas

2018

2018 IEEE Conference on Decision and Control (CDC)

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The problem of time-constrained multi-agent task scheduling and control synthesis is addressed. We assume the existence of a high level plan which consists of a sequence of cooperative tasks, each of which is associated with a deadline and several Quality-of-Service levels. By taking into account the reward and cost of satisfying each task, a novel scheduling problem is formulated and a path synthesis algorithm is proposed. Based on the obtained plan, a distributed hybrid control law is further designed for each agent. Under the condition that only a subset of the agents are aware of the high level plan, it is shown that the proposed controller guarantees the satisfaction of time constraints for each task. A simulation example is given to verify the theoretical results.

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“…The proposed multi-robot scheme is based on a prioritized leader-follower coordination. Prioritization in multi-agent systems for navigation-type objectives has been employed in Guo, Tumova, and Dimarogonas (2016) and Roussos and Kyriakopoulos (2013), where KRNF gain tuning-type methodologies are developed. The proposed framework, however, is substantially different from these works; Guo et al (2016) do not take into account inter-agent collisions, and use prioritization for the sequential navigation and task satisfaction subject to connectivity constraints, while Roussos and Kyriakopoulos (2013) use prioritization for directional collision-avoidance.…”

Section: Extension To Multi-robot Systemsmentioning

confidence: 99%

“…This is accomplished by differentiating the free spaces of the leader and the followers. Moreover, the aforementioned works Guo et al (2016) and Roussos and Kyriakopoulos (2013) consider simplified first-order dynamics and cannot be easily extended to the uncertain dynamics-case considered here. In fact, we note that, according to our best knowledge, there does not exist a control framework that provably guarantees decentralized safe multi-robot navigation in workspaces with obstacles and subject to uncertain 2nd-order dynamics.…”

Section: Extension To Multi-robot Systemsmentioning

confidence: 99%

Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics

Verginis

Dimarogonas

2021

Automatica

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This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for extending the control scheme to decentralized multi-robot systems. Finally, extensive simulation results verify the theoretical findings.

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Communication-Free Multi-Agent Control Under Local Temporal Tasks and Relative-Distance Constraints

Cited by 31 publications

References 31 publications

Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication

Multirobot Data Gathering Under Buffer Constraints and Intermittent Communication

Time-constrained multi-agent task scheduling based on prescribed performance control

Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics

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