340 research outputs found
Optimal Network Topology for Effective Collective Response
Natural, social, and artificial multi-agent systems usually operate in
dynamic environments, where the ability to respond to changing circumstances is
a crucial feature. An effective collective response requires suitable
information transfer among agents, and thus is critically dependent on the
agents' interaction network. In order to investigate the influence of the
network topology on collective response, we consider an archetypal model of
distributed decision-making---the leader-follower linear consensus---and study
the collective capacity of the system to follow a dynamic driving signal (the
"leader") for a range of topologies and system sizes. The analysis reveals a
nontrivial relationship between optimal topology and frequency of the driving
signal. Interestingly, the response is optimal when each individual interacts
with a certain number of agents which decreases monotonically with the
frequency and, for large enough systems, is independent of the size of the
system. This phenomenology is investigated in experiments of collective motion
using a swarm of land robots. The emergent collective response to both a slow-
and a fast-changing leader is measured and analyzed for a range of interaction
topologies. These results have far-reaching practical implications for the
design and understanding of distributed systems, since they highlight that a
dynamic rewiring of the interaction network is paramount to the effective
collective operations of multi-agent systems at different time-scales
Adaptive Navigation Control for Swarms of Autonomous Mobile Robots
This paper was devoted to developing a new and general coordinated adaptive navigation scheme for large-scale mobile robot swarms adapting to geographically constrained environments. Our distributed solution approach was built on the following assumptions: anonymity, disagreement on common coordinate systems, no pre-selected leader, and no direct communication. The proposed adaptive navigation was largely composed of four functions, commonly relying on dynamic neighbor selection and local interaction. When each robot found itself what situation it was in, individual appropriate ranges for neighbor selection were defined within its limited sensing boundary and the robots properly selected their neighbors in the limited range. Through local interactions with the neighbors, each robot could maintain a uniform distance to its neighbors, and adapt their direction of heading and geometric shape. More specifically, under the proposed adaptive navigation, a group of robots could be trapped in a dead-end passage,but they merge with an adjacent group to emergently escape from the dead-end passage. Furthermore, we verified the effectiveness of the proposed strategy using our in-housesimulator. The simulation results clearly demonstrated that the proposed algorithm is a simple yet robust approach to autonomous navigation of robot swarms in highlyclutteredenvironments. Since our algorithm is local and completely scalable to any size, it is easily implementable on a wide variety of resource-constrained mobile robots andplatforms. Our adaptive navigation control for mobile robot swarms is expected to be used in many applications ranging from examination and assessment of hazardous environments to domestic applications
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Flexible Distributed Flocking Control for Multi-agent Unicycle Systems
Currently, the general aim of flocking and formation control laws for
multi-agent systems is to form and maintain a rigid configuration, such as, the
alpha-lattices in flocking control methods, where the desired distance between
each pair of connected agents is fixed. This introduces a scalability issue for
large-scale deployment of agents due to unrealizable geometrical constraints
and the constant need of centralized orchestrator to ensure the formation graph
rigidity. This paper presents a flexible distributed flocking cohesion
algorithm for nonholonomic multi-agent systems. The desired geometry
configuration between each pair of agents is adaptive and flexible. The
distributed flocking goal is achieved using limited information exchange (i.e.,
the local field gradient) between connected neighbor agents and it does not
rely on any other motion variables measurements, such as (relative) position,
velocity, or acceleration. Additionally, the flexible flocking scheme with
safety is considered so that the agents with limited sensing capability are
able to maintain the connectedness of communication topology at all time and
avoid inter-agent collisions. The stability analysis of the proposed methods is
presented along with numerical simulation results to show their effectiveness.Comment: 9 pages, 2 figure
A Conceptual Framework for Adapation
This paper presents a white-box conceptual framework for adaptation that promotes a neat separation of the adaptation logic from the application logic through a clear identification of control data and their role in the adaptation logic. The framework provides an original perspective from which we survey archetypal approaches to (self-)adaptation ranging from programming languages and paradigms, to computational models, to engineering solutions
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