11 research outputs found
Decentralized collaborative transport of fabrics using micro-UAVs
Small unmanned aerial vehicles (UAVs) have generally little capacity to carry
payloads. Through collaboration, the UAVs can increase their joint payload
capacity and carry more significant loads. For maximum flexibility to dynamic
and unstructured environments and task demands, we propose a fully
decentralized control infrastructure based on a swarm-specific scripting
language, Buzz. In this paper, we describe the control infrastructure and use
it to compare two algorithms for collaborative transport: field potentials and
spring-damper. We test the performance of our approach with a fleet of
micro-UAVs, demonstrating the potential of decentralized control for
collaborative transport.Comment: Submitted to 2019 International Conference on Robotics and Automation
(ICRA). 6 page
Bearing-based formation control with second-order agent dynamics
We consider the distributed formation control problem for a network of agents using visual measurements. We propose solutions that are based on bearing (and optionally distance) measurements, and agents with double integrator dynamics. We assume that a subset of the agents can track, in addition to their neighbors, a set of static features in the environment. These features are not considered to be part of the formation, but they are used to asymptotically control the velocity of the agents. We analyze the convergence properties of the proposed protocols analytically and through simulations.Published versionSupporting documentatio
Proofs of Control of a Quadrotor and a Ground Vehicle Manipulating an Object
This paper focuses on the control of a cooperative system composed of an
Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV) manipulating
an object. The two units are subject to input saturations and collaborate to
move the object to a desired pose characterized by its position and
inclination. The dynamics are derived using Euler-Lagrange method. A
pre-stabilizing control law is proposed where the UGV is tasked to deploy the
object to a certain position whereas the UAV adjusts its inclination. In
particular, a proportional-derivative control law is proposed for the UGV, and
a cascade control approach is used for the UAV, where the inner loop controls
the attitude of the UAV and the outer loop stabilizes the inclination of the
object. Then, we prove the stability of the points of equilibrium using small
gain arguments. To ensure constraints satisfaction at all times, a reference
governor unit is added to the pre-stabilizing control scheme. Finally,
numerical results combined with experimental results are provided to validate
the effectiveness of the proposed control scheme in practice.Comment: 16 pages, 7 figure
Degenerate Gaussian factors for probabilistic inference
In this paper, we propose a parametrised factor that enables inference on
Gaussian networks where linear dependencies exist among the random variables.
Our factor representation is effectively a generalisation of traditional
Gaussian parametrisations where the positive-definite constraint of the
covariance matrix has been relaxed. For this purpose, we derive various
statistical operations and results (such as marginalisation, multiplication and
affine transformations of random variables) that extend the capabilities of
Gaussian factors to these degenerate settings. By using this principled factor
definition, degeneracies can be accommodated accurately and automatically at
little additional computational cost. As illustration, we apply our methodology
to a representative example involving recursive state estimation of cooperative
mobile robots.Comment: Accepted by International Journal of Approximate Reasoning on 17
January 202
A Survey on Aerial Swarm Robotics
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas