27,841 research outputs found
A Software Suite for the Control and the Monitoring of Adaptive Robotic Ecologies
Adaptive robotic ecologies are networks of heterogeneous robotic devices (sensors, actuators, automated appliances) pervasively embedded in everyday environments, where they learn to cooperate towards the achievement of complex tasks. While their flexibility makes them an increasingly popular way to improve a system’s reliability, scalability, robustness and autonomy, their effective realisation demands integrated control and software solutions for the specification, integration and management of their highly heterogeneous and computational constrained components. In this extended abstract we briefly illustrate the characteristic requirements dictated by robotic ecologies, discuss our experience in developing adaptive robotic ecologies, and provide an overview of the specific solutions developed as part of the EU FP7 RUBICON Project
Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance
Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a
reliable and robust collision avoidance technique. In this paper we address the
problem of multi-MAV reactive collision avoidance. A model-based controller is
employed to achieve simultaneously reference trajectory tracking and collision
avoidance. Moreover, we also account for the uncertainty of the state estimator
and the other agents position and velocity uncertainties to achieve a higher
degree of robustness. The proposed approach is decentralized, does not require
collision-free reference trajectory and accounts for the full MAV dynamics. We
validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40
A Decentralized Mobile Computing Network for Multi-Robot Systems Operations
Collective animal behaviors are paradigmatic examples of fully decentralized
operations involving complex collective computations such as collective turns
in flocks of birds or collective harvesting by ants. These systems offer a
unique source of inspiration for the development of fault-tolerant and
self-healing multi-robot systems capable of operating in dynamic environments.
Specifically, swarm robotics emerged and is significantly growing on these
premises. However, to date, most swarm robotics systems reported in the
literature involve basic computational tasks---averages and other algebraic
operations. In this paper, we introduce a novel Collective computing framework
based on the swarming paradigm, which exhibits the key innate features of
swarms: robustness, scalability and flexibility. Unlike Edge computing, the
proposed Collective computing framework is truly decentralized and does not
require user intervention or additional servers to sustain its operations. This
Collective computing framework is applied to the complex task of collective
mapping, in which multiple robots aim at cooperatively map a large area. Our
results confirm the effectiveness of the cooperative strategy, its robustness
to the loss of multiple units, as well as its scalability. Furthermore, the
topology of the interconnecting network is found to greatly influence the
performance of the collective action.Comment: Accepted for Publication in Proc. 9th IEEE Annual Ubiquitous
Computing, Electronics & Mobile Communication Conferenc
Position and Orientation Based Formation Control of Multiple Rigid Bodies with Collision Avoidance and Connectivity Maintenance
This paper addresses the problem of position- and orientation-based formation
control of a class of second-order nonlinear multi-agent systems in a D
workspace with obstacles. More specifically, we design a decentralized control
protocol such that each agent achieves a predefined geometric formation with
its initial neighbors, while using local information based on a limited sensing
radius. The latter implies that the proposed scheme guarantees that the
initially connected agents remain always connected. In addition, by introducing
certain distance constraints, we guarantee inter-agent collision avoidance as
well as collision avoidance with the obstacles and the boundary of the
workspace. The proposed controllers employ a novel class of potential functions
and do not require a priori knowledge of the dynamical model, except for
gravity-related terms. Finally, simulation results verify the validity of the
proposed framework
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