Distributed Intermittent Connectivity Control of Mobile Robot Networks

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2018

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Abstract

Wireless communication is known to play a pivotal role in enabling teams of robots to successfully accomplish global coordinated tasks. In fact, network connectivity is an underlying assumption in every distributed control and optimization algorithm. For this reason, in recent years, there is growing research in designing controllers that ensure point-to-point or end-to-end network connectivity for all time. Nevertheless, all these methods severely restrict the robots from accomplishing their tasks, as motion planning is always restricted by connectivity constraints on the network. Instead, a much preferred solution is to enable robots to communicate in an intermittent fashion, and operate in disconnect mode the rest of the time giving rise to an intermittently connected communication network. While in disconnect mode, the robots can accomplish their tasks free of communication constraints. The goal of this dissertation is to design a distributed intermittent connectivity framework that (i) ensures that the communication network is connected over time, infinitely often (ii) is flexible enough to account for arbitrary dynamic tasks, and (iii) can be applied to large-scale networks.

The great challenge in developing intermittent connectivity protocols for networks of mobile robots is to decide (i) which robots talk to which, (ii) where, and (iii) when, so that the communication network is connected over time infinitely often. To address these challenges, we decompose the network into small groups of robots, also called teams, so that every robot belongs to at least one team and that there is a path, i.e., a sequence of teams, where consecutive teams have non-empty intersections, connecting every two teams of robots, so that information can propagate in the network. First, given such fixed teams, we design infinite sequences of communication events for all robots, also called communication schedules, independent of the tasks assigned to the robots, that determine when every team should communicate, so that the communication network is connected over time infinitely often. The designed communication schedules ensure that all teams communicate infinitely often, i.e., that the communication network is connected over time infinitely often. Between communication events the robots can move in the workspace free of communication constraints to accomplish their assigned tasks. Theoretical guarantees and numerical experiments corroborate the proposed framework. This is the first distributed intermittent connectivity framework that can be applied to large-scale networks and is flexible enough to account for arbitrary dynamic robot tasks.

Next, given user-specified fixed teams, we integrate the respective communication schedules with task planning. Specifically, we consider high-level complex tasks captured by temporal logic formulas, state-estimation tasks, and time-critical dynamic tasks. The proposed distributed integrated path planning and intermittent connectivity frameworks determine both where and when every team should communicate so that the assigned task is accomplished, the communication network is connected over time infinitely often, and a user-specified metric, such as total traveled distance or consumed energy, is minimized. We show that employing the proposed intermittent connectivity framework for such tasks results in significant performance gains compared to the existing solutions in the literature that maintain connectivity for all time. Theoretical guarantees, numerical and experimental studies support the proposed distributed control algorithms.

Finally, we propose a fully autonomous intermittent connectivity framework that can handle arbitrary dynamic tasks and also allows the robots to locally and online update the structure of the teams and the communication schedules, effectively allowing them to decide who they should talk to, so that they can better accomplish newly assigned tasks. The structure of the teams, the associated communication locations, and the time instants when communication within teams will occur are integrated online with task planning giving rise to paths, i.e., sequences of waypoints, that ensure that the assigned task is accomplished, the communication network is connected over time infinitely often, and a user specified metric is minimized. This is the first fully autonomous, distributed, and

online intermittent connectivity framework that can handle arbitrary dynamic tasks and also controls the topology of the intermittently connected robot network to better accomplish these tasks. At the same time, the proposed framework scales well with the size of the robot network. Theoretical guarantees and numerical experiments corroborate the proposed distributed control scheme.

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Kantaros, Yiannis (2018). Distributed Intermittent Connectivity Control of Mobile Robot Networks. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/16813.

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