9 research outputs found
Distributed scaling control of rigid formations
Recently it has been reported that biased range-measurements among
neighboring agents in the gradient distance-based formation control can lead to
predictable collective motion. In this paper we take advantage of this effect
and by introducing distributed parameters to the prescribed inter-distances we
are able to manipulate the steady-state motion of the formation. This
manipulation is in the form of inducing simultaneously the combination of
constant translational and angular velocities and a controlled scaling of the
rigid formation. While the computation of the distributed parameters for the
translational and angular velocities is based on the well-known graph rigidity
theory, the parameters responsible for the scaling are based on some recent
findings in bearing rigidity theory. We carry out the stability analysis of the
modified gradient system and simulations in order to validate the main result.Comment: 6 pages In proceedings 55th Conference on Decision and Control, year
201
Taming mismatches in inter-agent distances for the formation-motion control of second-order agents
This paper presents the analysis on the influence of distance mismatches on
the standard gradient-based rigid formation control for second-order agents. It
is shown that, similar to the first-order case as recently discussed in the
literature, these mismatches introduce two undesired group behaviors: a
distorted final shape and a steady-state motion of the group formation. We show
that such undesired behaviors can be eliminated by combining the standard
formation control law with distributed estimators. Finally, we show how the
mismatches can be effectively employed as design parameters in order to control
a combined translational and rotational motion of the formation.Comment: 14 pages, conditionally accepted in Automatic Control, IEEE
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Multi-robot motion-formation distributed control with sensor self-calibration: experimental validation
In this paper, we present the design and implementation of a robust motion
formation distributed control algorithm for a team of mobile robots. The
primary task for the team is to form a geometric shape, which can be freely
translated and rotated at the same time. This approach makes the robots to
behave as a cohesive whole, which can be useful in tasks such as collaborative
transportation. The robustness of the algorithm relies on the fact that each
robot employs only local measurements from a laser sensor which does not need
to be off-line calibrated. Furthermore, robots do not need to exchange any
information with each other. Being free of sensor calibration and not requiring
a communication channel helps the scaling of the overall system to a large
number of robots. In addition, since the robots do not need any off-board
localization system, but require only relative positions with respect to their
neighbors, it can be aimed to have a full autonomous team that operates in
environments where such localization systems are not available. The
computational cost of the algorithm is inexpensive and the resources from a
standard microcontroller will suffice. This fact makes the usage of our
approach appealing as a support for other more demanding algorithms, e.g.,
processing images from onboard cameras. We validate the performance of the
algorithm with a team of four mobile robots equipped with low-cost commercially
available laser scanners.Comment: 6 pages. ICARCV 201
Distributed formation control for autonomous robots
This thesis addresses several theoretical and practical problems related to formation-control of autonomous robots. Formation-control aims to simultaneously accomplish the tasks of forming a desired shape by the robots and controlling their coordinated collective motion. This kind of robot performance is a cornerstone in the emerging field of swarm robotics, in particular with applications in precision agriculture, coverage of sport/art events, communication networks, area surveillance or vehicle platooning for energy efficiency and many others. One of the most important outcomes of this thesis is that the provided algorithms are completely distributed. This means that there is no central unit commanding the robots, but they have their own intelligence which allows them to make their own decisions based only on the local information. A distributed scheme entails a striking feature about the scalability and maintenance of a team of robots. Moreover, we also address the scenario of having wrongly calibrated sensors, which has a profound impact in the performance of the robots. The provided algorithms make the robots robust against such a practical and very common problem in real applications