151,315 research outputs found
Decentralized shape formation and force-based interactive formation control in robot swarms
Swarm robotic systems utilize collective behaviour to achieve goals that
might be too complex for a lone entity, but become attainable with localized
communication and collective decision making. In this paper, a behaviour-based
distributed approach to shape formation is proposed. Flocking into strategic
formations is observed in migratory birds and fish to avoid predators and also
for energy conservation. The formation is maintained throughout long periods
without collapsing and is advantageous for communicating within the flock.
Similar behaviour can be deployed in multi-agent systems to enhance
coordination within the swarm. Existing methods for formation control are
either dependent on the size and geometry of the formation or rely on
maintaining the formation with a single reference in the swarm (the leader).
These methods are not resilient to failure and involve a high degree of
deformation upon obstacle encounter before the shape is recovered again. To
improve the performance, artificial force-based interaction amongst the
entities of the swarm to maintain shape integrity while encountering obstacles
is elucidated.Comment: 6 pages, 10 figure
Mobile Formation Coordination and Tracking Control for Multiple Non-holonomic Vehicles
This paper addresses forward motion control for trajectory tracking and
mobile formation coordination for a group of non-holonomic vehicles on SE(2).
Firstly, by constructing an intermediate attitude variable which involves
vehicles' position information and desired attitude, the translational and
rotational control inputs are designed in two stages to solve the trajectory
tracking problem. Secondly, the coordination relationships of relative
positions and headings are explored thoroughly for a group of non-holonomic
vehicles to maintain a mobile formation with rigid body motion constraints. We
prove that, except for the cases of parallel formation and translational
straight line formation, a mobile formation with strict rigid-body motion can
be achieved if and only if the ratios of linear speed to angular speed for each
individual vehicle are constants. Motion properties for mobile formation with
weak rigid-body motion are also demonstrated. Thereafter, based on the proposed
trajectory tracking approach, a distributed mobile formation control law is
designed under a directed tree graph. The performance of the proposed
controllers is validated by both numerical simulations and experiments
Developing Successful Global Health Alliances
Examines the circumstances that call for alliance formation to reduce the burdens of AIDS, tuberculosis, malaria, polio, river blindness, and many other diseases; the utility of various alliance models; and the characteristics of successful alliances
A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)
This work presents a novel framework for the formation control of multiple
autonomous ground vehicles in an on-road environment. Unique challenges of this
problem lie in 1) the design of collision avoidance strategies with obstacles
and with other vehicles in a highly structured environment, 2) dynamic
reconfiguration of the formation to handle different task specifications. In
this paper, we design a local MPC-based tracking controller for each individual
vehicle to follow a reference trajectory while satisfying various constraints
(kinematics and dynamics, collision avoidance, \textit{etc.}). The reference
trajectory of a vehicle is computed from its leader's trajectory, based on a
pre-defined formation tree. We use logic rules to organize the collision
avoidance behaviors of member vehicles. Moreover, we propose a methodology to
safely reconfigure the formation on-the-fly. The proposed framework has been
validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1
Dynamic similarity promotes interpersonal coordination in joint-action
Human movement has been studied for decades and dynamic laws of motion that
are common to all humans have been derived. Yet, every individual moves
differently from everyone else (faster/slower, harder/smoother etc). We propose
here an index of such variability, namely an individual motor signature (IMS)
able to capture the subtle differences in the way each of us moves. We show
that the IMS of a person is time-invariant and that it significantly differs
from those of other individuals. This allows us to quantify the dynamic
similarity, a measure of rapport between dynamics of different individuals'
movements, and demonstrate that it facilitates coordination during interaction.
We use our measure to confirm a key prediction of the theory of similarity that
coordination between two individuals performing a joint-action task is higher
if their motions share similar dynamic features. Furthermore, we use a virtual
avatar driven by an interactive cognitive architecture based on feedback
control theory to explore the effects of different kinematic features of the
avatar motion on the coordination with human players
Team Learning, Development, and Adaptation
[Excerpt] Our purpose is to explore conceptually these themes centered on team learning, development, and adaptation. We note at the onset that this chapter is not a comprehensive review of the literature. Indeed, solid conceptual and empirical work on these themes are sparse relative to the vast amount of work on team effectiveness more generally, and therefore a thematic set of topics that are ripe for conceptual development and integration. We draw on an ongoing stream of theory development and research in these areas to integrate and sculpt a distinct perspective on team learning, development, and adaptation
Navigation, localization and stabilization of formations of unmanned aerial and ground vehicles
A leader-follower formation driving algorithm developed for control of heterogeneous groups of unmanned micro aerial and ground vehicles stabilized under a top-view relative localization is presented in this paper. The core of the proposed method lies in a novel avoidance function, in which the entire 3D formation is represented by a convex hull projected along a desired path to be followed by the group. Such a representation of the formation provides non-collision trajectories of the robots and respects requirements of the direct visibility between the team members in environment with static as well as dynamic obstacles, which is crucial for the top-view localization. The algorithm is suited for utilization of a simple yet stable visual based navigation of the group (referred to as GeNav), which together with the on-board relative localization enables deployment of large teams of micro-scale robots in environments without any available global localization system. We formulate a novel Model Predictive Control (MPC) based concept that enables to respond to the changing environment and that provides a robust solution with team members' failure tolerance included. The performance of the proposed method is verified by numerical and hardware experiments inspired by reconnaissance and surveillance missions
Emergent velocity agreement in robot networks
In this paper we propose and prove correct a new self-stabilizing velocity
agreement (flocking) algorithm for oblivious and asynchronous robot networks.
Our algorithm allows a flock of uniform robots to follow a flock head emergent
during the computation whatever its direction in plane. Robots are
asynchronous, oblivious and do not share a common coordinate system. Our
solution includes three modules architectured as follows: creation of a common
coordinate system that also allows the emergence of a flock-head, setting up
the flock pattern and moving the flock. The novelty of our approach steams in
identifying the necessary conditions on the flock pattern placement and the
velocity of the flock-head (rotation, translation or speed) that allow the
flock to both follow the exact same head and to preserve the flock pattern.
Additionally, our system is self-healing and self-stabilizing. In the event of
the head leave (the leading robot disappears or is damaged and cannot be
recognized by the other robots) the flock agrees on another head and follows
the trajectory of the new head. Also, robots are oblivious (they do not recall
the result of their previous computations) and we make no assumption on their
initial position. The step complexity of our solution is O(n)
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