The Robotarium: Globally Impactful Opportunities, Challenges, and Lessons Learned in Remote-Access, Distributed Control of Multirobot Systems

Wilson, Sean; Paul, Gavin; Wang, Li; Mayya, Siddharth; Notomista, Gennaro; Mote, Mark; Egerstedt, Magnus

doi:10.1109/mcs.2019.2949973

Cited by 156 publications

(67 citation statements)

References 31 publications

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“…In order to illustrate the properties of the resilient task prioritization and execution framework developed and demonstrated in this paper, in this section we present the results of its implementation on a team of mobile robots in the Robotarium [46], a remotely accessible swarm robotics testbed. The scenario of the experiment is depicted in Fig.…”

Section: Methodsmentioning

confidence: 99%

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Notomista¹,

Mayya²,

Emam³

et al. 2021

Preprint

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In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Towards this end, the task prioritization and execution-through which the allocation of tasks to robots is effectively realizedare encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots, and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

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Section: Methodsmentioning

confidence: 99%

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Notomista¹,

Mayya²,

Emam³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Moreover, we assess how different choices of the target selection rule affect the overall effectiveness of the methodology we propose. Finally, we test the effectiveness of the proposed strategies to solve the herding problem firstly in simulations in ROS and then in experiments on real robots conducted via the online Robotarium platform (Pickem et al 2017;Wilson et al 2020).…”

Section: Contributions Of This Papermentioning

confidence: 99%

“…To validate in more realistic robotic settings the strategies we propose, we complemented the numerical simulations presented in Sect. 5 with simulations in ROS 1 and experiments on real robots conducted on the online Robotarium platform (Pickem et al 2017;Wilson et al 2020).…”

Section: Experimental Validationmentioning

confidence: 99%

“…Robotarium is a remotely accessible swarm robotics research platform equipped with GRITSBot robots which allows rapid deployment and testing of custom control algorithms (Pickem et al 2015;Wilson et al 2020). To comply with the limited space of the arena (3.2 m × 2 m) and safety protocols to avoid collisions between robots (robots' diameter is 11 cm) implemented in the platform, we considered a scenario with N T = 4 passive robots and N H = 2 herder robots; a herding scenario that was also considered in Rigoli et al (2020), Auletta et al (2021) to study and model the selection strategies adopted by pairs of human-driven herder agents.…”

Section: Robotarium Experimentsmentioning

confidence: 99%

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Herding stochastic autonomous agents via local control rules and online target selection strategies

et al. 2022

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We propose a simple yet effective set of local control rules to make a small group of “herder agents” collect and contain in a desired region a large ensemble of non-cooperative, non-flocking stochastic “target agents” in the plane. We investigate the robustness of the proposed strategies to variations of the number of target agents and the strength of the repulsive force they feel when in proximity of the herders. The effectiveness of the proposed approach is confirmed in both simulations in ROS and experiments on real robots.

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“…The presented approach has been implemented on a simulated 3-link planar arm and a real differential-drive robot on the Robotarium [18]. Figure 4 shows the two experiments performed using traditional and our CBF-QP.…”

Section: Robotic Experimentsmentioning

confidence: 99%

Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions

Notomista¹,

Saveriano²

2021

Preprint

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This paper presents an approach to deal with safety of dynamical systems in presence of multiple nonconvex unsafe sets. While optimal control and model predictive control strategies can be employed in these scenarios, they suffer from high computational complexity in case of general nonlinear systems. Leveraging control barrier functions, on the other hand, results in computationally efficient control algorithms. Nevertheless, when safety guarantees have to be enforced alongside stability objectives, undesired asymptotically stable equilibrium points have been shown to arise. We propose a computationally efficient optimization-based approach which allows us to ensure safety of dynamical systems without introducing undesired equilibria even in presence of multiple nonconvex unsafe sets. The developed control algorithm is showcased in simulation and in a real robot navigation application.

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The Robotarium: Globally Impactful Opportunities, Challenges, and Lessons Learned in Remote-Access, Distributed Control of Multirobot Systems

Cited by 156 publications

References 31 publications

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Herding stochastic autonomous agents via local control rules and online target selection strategies

Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions

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