40 research outputs found
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Biomimetic Algorithms for Coordinated Motion: Theory and Implementation
Drawing inspiration from flight behavior in biological settings (e.g.
territorial battles in dragonflies, and flocking in starlings), this paper
demonstrates two strategies for coverage and flocking. Using earlier
theoretical studies on mutual motion camouflage, an appropriate steering
control law for area coverage has been implemented in a laboratory test-bed
equipped with wheeled mobile robots and a Vicon high speed motion capture
system. The same test-bed is also used to demonstrate another strategy (based
on local information), termed topological velocity alignment, which serves to
make agents move in the same direction. The present work illustrates the
applicability of biological inspiration in the design of multi-agent robotic
collectives
A Roomful of Robovacs: How to Think About Genetic Programs
The notion of a genetic program has been widely criticized by both biologists and philosophers. But the debate has revolved around a narrow conception of what programs are and how they work, and many criticisms are linked to this same conception. To remedy this, I outline a modern and more apt idea of a program that possesses many of the features critics thought missing from programs. Moving away from over-simplistic conceptions of programs opens the way to a more fruitful interplay of ideas between the complexity of biology and our most complex engineering discipline
Autonomous Swarming Unmanned Aerial Vehicles for Multiple Perspective Observation
There is much research being done in the field of swarming robotics and more specifically swarming unmanned aerial vehicles or UAVs. The most common platform used is the quadcopter because of its versatility. The problem to be solved is how to have autonomous swarming quadcopters that can orient themselves towards a point in space while maintaining their position relative to one another and there position relative to the point. It would be necessary to be able to send a command to these UAVs so they can change their pattern around this point in real time while avoiding each other. These quadcopters would also have video recording capabilities in order to observe the point. If this problem was solved, quadcopters could be used for tasks such as recording shots for movie directors, recording shots for athletic events, or used for surveillance. One day these quadcopters could be used to improve people’s lives and even their safety. These quadcopters could be sent into a burning building to cooperatively search for a firefighter or to search for someone missing in a forest or to monitor the border. This is what I would like to achieve
Online Flocking Control of UAVs with Mean-Field Approximation
We present a novel approach to the formation controlling of aerial robot
swarms that demonstrates the flocking behavior. The proposed method stems from
the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable
control inputs from being produced and subsequently leads to feasible
trajectories. By modeling the inter-agent relationships using a pairwise energy
function, we show that interacting robot swarms constitute a Markov Random
Field. Our algorithm builds on the Mean-Field Approximation and incorporates
the collective behavioral rules: cohesion, separation, and velocity alignment.
We follow a distributed control scheme and show that our method can control a
swarm of UAVs to a formation and velocity consensus with real-time collision
avoidance. We validate the proposed method with physical and high-fidelity
simulation experiments.Comment: To appear in the proceedings of IEEE International Conference on
Robotics and Automation (ICRA), 202