Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams

Ramachandran, Ragesh K.; Preiss, James A.; Sukhatme, Gaurav S.

doi:10.1109/iros40897.2019.8968611

Cited by 37 publications

(26 citation statements)

References 27 publications

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“…Resilience of robots can be affected by different factors. This can be by hardware or software (or a combination of both), realised either structurally or systematically by means of control or configuration [38,39]. It generally refers to the robots' capability to recover from a damaged, hazardous, or deviated status [40].…”

Section: Methodsmentioning

confidence: 99%

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

Wang¹,

Post²,

Tyrrell³

2022

Electronics

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Resilience plays an important role in improving robustness for robots in harsh environments such as planetary exploration and unstructured terrains. As a naturally compliant structure, tensegrity presents advantageous flexibility for enhancing resilience in robotic applications according to existing research. However, tensegrity robots to date are normally based on monolithic morphologies and are slow in locomotion. In this paper, we demonstrate how we adopt such flexibility to improve the robustness of wheeled robots by articulating modules with tensegrity mechanisms. The test results reveal the robot is resistant and resilient to external hazards in a fully passive manner owing to the continuous elasticity in the structure network. It possesses a good number of DoFs and can adapt to various terrains easily either with actuation or not. The robot is also capable of crawling locomotion aside from wheeled locomotion to traverse uneven surfaces and provide self-recovery from rollovers. It demonstrates good robustness and mobility at the same time compared with existing tensegrity robots and shows the competitiveness with conventional rigid robots in harsh scenarios. The proposed robot presents the capability of tensegrity robots with resilience, robustness, agility, and mobility without compromise. In a broader perspective, it widens the potential of tensegrity robots in practical applications.

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Section: Methodsmentioning

confidence: 99%

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

Wang¹,

Post²,

Tyrrell³

2022

Electronics

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“…Such frameworks, however, do not account for drastic unexpected failures in the capabilities of the robots, adversarial attacks, or varying environmental conditions that might affect the operations of the robots. Such considerations point to the question of resilience in multi-robot systems, which has been explored in the context of coordinated control tasks [32], as well as resource-availability in heterogeneous systems [8].…”

Section: B Related Workmentioning

confidence: 99%

“…where f and g are locally Lipschitz continuous vector fields, x i ∈ X ⊆ R nx is the state of the robot, and u i ∈ U ⊆ R nu is the input. Note that, in this paper, we assume that all robots obey the same dynamics given in (8), however, the entire formulation can be extended in a straightforward fashion to the case where individual robots have different dynamics.…”

Section: Task Execution and Prioritizationmentioning

confidence: 99%

“…Heterogeneity can also contribute favorably to another desirable property of a MRTA framework: resilience, typically interpreted as the ability of the allocation algorithm to react to component failures on the robots, varying environmental conditions, and other non-idealities in the operating conditions [8]. Consider an example scenario where a heterogeneous multi-robot system consisting of ground and aerial mobile robots, is tasked with carrying objects to specified locations in the environment.…”

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confidence: 99%

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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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“…The analysis of a possible solution depends on the specific criteria in hand. For example, how the placement of the robots optimize their resilient to failures [4] or survivability of threats [5].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Formations of Multiple Robots

Cohen

Agmon

2021

Curr Robot Rep

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Purpose of Review Formation control is a canonical problem in multi-robot systems, which focuses on the ability of a group of robots to travel in coordination through an area, while maintaining a certain shape or a particular behavior. The robot groups vary in their communication, computation, and sensing capabilities. Moreover, the formation control task itself may have various objectives. These divergences force the use of different models for controlling the formation and for analyzing the task performance. In this paper, we describe the formation control problem and survey recent advances focusing on aspects of maintaining a formation by a group of robots distinguished by the means of analysis.Recent Findings Various approaches may be applied for the sake of formation maintenance, whereas each approach possesses a different perspective in regard with formation control. Recent research focuses on combining those approaches, due to their applicability regarding certain scenarios. For instance, consensus-based control and collision avoidance are usually intertwined together for the sake of reaching a consensus in a manner which is collision-free. Furthermore, machine learning (ML)-based methods for navigating a robot team through unknown complex environments can be incorporated, where the robot team aims to reach a goal position while avoiding collisions and maintaining connectivity. Moreover, recent approaches focus on developing new mechanisms or adapt existing ones for formation control for tolerating limitations in sensing, communication, and coordination, preferably distributively while providing performance guarantees. ConclusionSuch combined approaches yield that the means of analysis, which can be applied to each one separately, can also be utilized in an intertwined manner, and thus provide us with novel methods for preserving formation. Whereas some approaches were vastly investigated (e.g., consensus-based formation control) and need to be adapted to distributed imperfect settings, others still require further insight for unveiling brand new architectures and tools (e.g., ML-based formation control).

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Resilience by Reconfiguration: Exploiting Heterogeneity in Robot Teams

Cited by 37 publications

References 27 publications

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

Articulating Resilience: Adaptive Locomotion of Wheeled Tensegrity Robot

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

Recent Advances in Formations of Multiple Robots

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