1,015 research outputs found
A macroscopic analytical model of collaboration in distributed robotic systems
In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased
A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments
Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended Hā filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009
Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
We consider cooperative localization technique for mobile agents with
communication and computation capabilities. We start by provide and overview of
different decentralization strategies in the literature, with special focus on
how these algorithms maintain an account of intrinsic correlations between
state estimate of team members. Then, we present a novel decentralized
cooperative localization algorithm that is a decentralized implementation of a
centralized Extended Kalman Filter for cooperative localization. In this
algorithm, instead of propagating cross-covariance terms, each agent propagates
new intermediate local variables that can be used in an update stage to create
the required propagated cross-covariance terms. Whenever there is a relative
measurement in the network, the algorithm declares the agent making this
measurement as the interim master. By acquiring information from the interim
landmark, the agent the relative measurement is taken from, the interim master
can calculate and broadcast a set of intermediate variables which each robot
can then use to update its estimates to match that of a centralized Extended
Kalman Filter for cooperative localization. Once an update is done, no further
communication is needed until the next relative measurement
Experiments in cooperative human multi-robot navigation
In this paper, we consider the problem of a
group of autonomous mobile robots and a human moving
coordinately in a real-world implementation. The group
moves throughout a dynamic and unstructured environment.
The key problem to be solved is the inclusion of a human in a
real multi-robot system and consequently the multiple robot
motion coordination. We present a set of performance metrics
(system efficiency and percentage of time in formation) and a
novel flexible formation definition whereby a formation
control strategy both in simulation and in real-world
experiments of a human multi-robot system is presented. The
formation control proposed is stable and effective by means of
its uniform dispersion, cohesion and flexibility
MOMA: Visual Mobile Marker Odometry
In this paper, we present a cooperative odometry scheme based on the
detection of mobile markers in line with the idea of cooperative positioning
for multiple robots [1]. To this end, we introduce a simple optimization scheme
that realizes visual mobile marker odometry via accurate fixed marker-based
camera positioning and analyse the characteristics of errors inherent to the
method compared to classical fixed marker-based navigation and visual odometry.
In addition, we provide a specific UAV-UGV configuration that allows for
continuous movements of the UAV without doing stops and a minimal
caterpillar-like configuration that works with one UGV alone. Finally, we
present a real-world implementation and evaluation for the proposed UAV-UGV
configuration
Experimental Testbed for Large Multirobot Teams
Experimental validation is particularly important in multirobot systems research. The differences between models and real-world conditions that may not be apparent in single robot experiments are amplified because of the large number of robots, interactions between robots, and the effects of asynchronous and distributed control, sensing, and actuation. Over the last two years, we have developed an experimental testbed to support research in multirobot systems with the goal of making it easy for users to model, design, benchmark, and validate algorithms. In this article, we describe our approach to the design of a large-scale multirobot system for the experimental verification and validation of a variety of distributed robotic applications in an indoor environment
Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization
In this study, we address multi-robot localization issues, with a specific
focus on cooperative localization and observability analysis of relative pose
estimation. Cooperative localization involves enhancing each robot's
information through a communication network and message passing. If odometry
data from a target robot can be transmitted to the ego robot, observability of
their relative pose estimation can be achieved through range-only or
bearing-only measurements, provided both robots have non-zero linear
velocities. In cases where odometry data from a target robot are not directly
transmitted but estimated by the ego robot, both range and bearing measurements
are necessary to ensure observability of relative pose estimation. For
ROS/Gazebo simulations, we explore four sensing and communication structures.
We compare extended Kalman filtering (EKF) and pose graph optimization (PGO)
estimation using different robust loss functions (filtering and smoothing with
varying batch sizes of sliding windows) in terms of estimation accuracy. In
hardware experiments, two Turtlebot3 equipped with UWB modules are used for
real-world inter-robot relative pose estimation, applying both EKF and PGO and
comparing their performance.Comment: 20 pages, 21 figure
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