582 research outputs found
Distributed Monitoring of Robot Swarms with Swarm Signal Temporal Logic
In this paper, we develop a distributed monitoring framework for robot swarms
so that the agents can monitor whether the executions of robot swarms satisfy
Swarm Signal Temporal Logic (SwarmSTL) formulas. We define generalized moments
(GMs) to represent swarm features. A dynamic generalized moments consensus
algorithm (GMCA) with Kalman filter (KF) is proposed so that each agent can
estimate the GMs. Also, we obtain an upper bound for the error between an
agent's estimate and the actual GMs. This bound is independent of the motion of
the agents. We also propose rules for monitoring SwarmSTL temporal and logical
operators. As a result, the agents can monitor whether the swarm satisfies
SwarmSTL formulas with a certain confidence level using these rules and the
bound of the estimation error. The distributed monitoring framework is applied
to a swarm transporting supplies example, where we also show the efficacy of
the Kalman filter in the dynamic generalized moments consensus process
A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots
Living cells exhibit both growth and regeneration of body tissues. Epigenetic
Tracking (ET), models this growth and regenerative qualities of living cells
and has been used to generate complex 2D and 3D shapes. In this paper, we
present an ET based algorithm that aids a swarm of identically-programmed
robots to form arbitrary shapes and regenerate them when cut. The algorithm
works in a distributed manner using only local interactions and computations
without any central control and aids the robots to form the shape in a
triangular lattice structure. In case of damage or splitting of the shape, it
helps each set of the remaining robots to regenerate and position themselves to
build scaled down versions of the original shape. The paper presents the shapes
formed and regenerated by the algorithm using the Kilombo simulator.Comment: 8 pages, 9 figures, GECCO-18 conferenc
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Quantifying Robotic Swarm Coverage
In the field of swarm robotics, the design and implementation of spatial
density control laws has received much attention, with less emphasis being
placed on performance evaluation. This work fills that gap by introducing an
error metric that provides a quantitative measure of coverage for use with any
control scheme. The proposed error metric is continuously sensitive to changes
in the swarm distribution, unlike commonly used discretization methods. We
analyze the theoretical and computational properties of the error metric and
propose two benchmarks to which error metric values can be compared. The first
uses the realizable extrema of the error metric to compute the relative error
of an observed swarm distribution. We also show that the error metric extrema
can be used to help choose the swarm size and effective radius of each robot
required to achieve a desired level of coverage. The second benchmark compares
the observed distribution of error metric values to the probability density
function of the error metric when robot positions are randomly sampled from the
target distribution. We demonstrate the utility of this benchmark in assessing
the performance of stochastic control algorithms. We prove that the error
metric obeys a central limit theorem, develop a streamlined method for
performing computations, and place the standard statistical tests used here on
a firm theoretical footing. We provide rigorous theoretical development,
computational methodologies, numerical examples, and MATLAB code for both
benchmarks.Comment: To appear in Springer series Lecture Notes in Electrical Engineering
(LNEE). This book contribution is an extension of our ICINCO 2018 conference
paper arXiv:1806.02488. 27 pages, 8 figures, 2 table
The landscape of Collective Awareness in multi-robot systems
The development of collective-aware multi-robot systems is crucial for
enhancing the efficiency and robustness of robotic applications in multiple
fields. These systems enable collaboration, coordination, and resource sharing
among robots, leading to improved scalability, adaptability to dynamic
environments, and increased overall system robustness. In this work, we want to
provide a brief overview of this research topic and identify open challenges.Comment: Submitted to workshop titled "Designing Aware Robots: The EIC
Pathfinder Challenge - Explore Awareness Inside" at the European Robotics
Forum 202
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