6,283 research outputs found
Distributed Dynamic Density Coverage for Human-Swarm Interactions
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/ACC.2015.7170761This paper presents two approaches to externally
influence a team of robots by means of time-varying
density functions. These density functions represent rough
references for where the robots should be located. Recently
developed continuous-time algorithms move the robots so
as to provide optimal coverage of a given the time-varying
density functions. This makes it possible for a human
operator to abstract away the number of robots and
focus on the general behavior of the team of robots as a
whole. Using a distributed approximation to this algorithm
whereby the robots only need to access information from
adjacent robots allows these algorithms to scale well with
the number of robots. Simulations and robotic experiments
show that the desired behaviors are achieved
Safe, Remote-Access Swarm Robotics Research on the Robotarium
This paper describes the development of the Robotarium -- a remotely
accessible, multi-robot research facility. The impetus behind the Robotarium is
that multi-robot testbeds constitute an integral and essential part of the
multi-agent research cycle, yet they are expensive, complex, and time-consuming
to develop, operate, and maintain. These resource constraints, in turn, limit
access for large groups of researchers and students, which is what the
Robotarium is remedying by providing users with remote access to a
state-of-the-art multi-robot test facility. This paper details the design and
operation of the Robotarium as well as connects these to the particular
considerations one must take when making complex hardware remotely accessible.
In particular, safety must be built in already at the design phase without
overly constraining which coordinated control programs the users can upload and
execute, which calls for minimally invasive safety routines with provable
performance guarantees.Comment: 13 pages, 7 figures, 3 code samples, 72 reference
Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms
open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)
Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms
In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors
Connectivity-Preserving Swarm Teleoperation With A Tree Network
During swarm teleoperation, the human operator may threaten the
distance-dependent inter-robot communications and, with them, the connectivity
of the slave swarm. To prevent the harmful component of the human command from
disconnecting the swarm network, this paper develops a constructive strategy to
dynamically modulate the interconnections of, and the locally injected damping
at, all slave robots. By Lyapunov-based set invariance analysis, the explicit
law for updating that control gains has been rigorously proven to synchronize
the slave swarm while preserving all interaction links in the tree network. By
properly limiting the impact of the user command rather than rejecting it
entirely, the proposed control law enables the human operator to guide the
motion of the slave swarm to the extent to which it does not endanger the
connectivity of the swarm network. Experiment results demonstrate that the
proposed strategy can maintain the connectivity of the tree network during
swarm teleoperation
The role of neighbours selection on cohesion and order of swarms
We introduce a multi-agent model for exploring how selection of neighbours
determines some aspects of order and cohesion in swarms. The model algorithm
states that every agents' motion seeks for an optimal distance from the nearest
topological neighbour encompassed in a limited attention field. Despite the
great simplicity of the implementation, varying the amplitude of the attention
landscape, swarms pass from cohesive and regular structures towards fragmented
and irregular configurations. Interestingly, this movement rule is an ideal
candidate for implementing the selfish herd hypothesis which explains
aggregation of alarmed group of social animals.Comment: 15 pages, 9 figures, Plos One, May 201
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