Robotic Swarms

Robotic Swarms

There are tasks that cannot be done by a single robot alone. A group of robots collaborating on a task has the potential of being highly efficient, flexible and robust. If one robot fails, another robot could take its position. However, coordinating a large group of robots through a centralized control unit is difficult as it would require the centralized unit to talk to all robots and compute next actions for a possibly huge number of team members. We investigate decentralized control strategies, where each robot is a self-contained unit able to communicate with or observe its closest neighbor robots and make decisions based on its own observations. The goal is that such a team of self-contained robot units is capable to achieving a joint goal. Such a decentralized approach scales to robot teams of any size. Our research in this area particularly focuses on:

  • Decentralized learning strategies that enable a team of robots to improve over time and
  • Learning approaches that help us find decentralized control strategies for complex problems that we know how to solve in a centralized way but finding a decentralized strategy from intuition turns out to be difficult.

 

Related Publications

Trajectory generation for multiagent point-to-point transitions via distributed model predictive control
C. E. Luis and A. P. Schoellig
IEEE Robotics and Automation Letters, 2019. Submitted.
[View BibTeX] [View Abstract] [Download PDF] [More Information]
This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control (DMPC). By predicting future states and sharing this information with their neighbours, the agents are able to detect and avoid collisions while moving towards their goals. The proposed algorithm computes transition trajectories for dozens of vehicles in a few seconds. It reduces the computation time by more than 85\% compared to previous optimization approaches based on sequential convex programming (SCP), with only causing a small impact on the optimality of the plans. We replaced the previous compatibility constraints in DMPC, which limit the motion of the agents in order to avoid collisions, by relaxing the collision constraints and enforcing them only when required. The approach was validated both through extensive simulations for a wide range of randomly generated transitions and with teams of up to 25 quadrotors flying in confined indoor spaces.

@article{luis-ral19,
title={Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control},
author={Carlos E. Luis and Angela P. Schoellig},
journal = {{IEEE Robotics and Automation Letters}},
year = {2019},
note={Submitted},
urllink = {https://arxiv.org/abs/1809.04230},
abstract = {This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control (DMPC). By predicting future states and sharing this information with their neighbours, the agents are able to detect and avoid collisions while moving towards their goals. The proposed algorithm computes transition trajectories for dozens of vehicles in a few seconds. It reduces the computation time by more than 85\% compared to previous optimization approaches based on sequential convex programming (SCP), with only causing a small impact on the optimality of the plans. We replaced the previous compatibility constraints in DMPC, which limit the motion of the agents in order to avoid collisions, by relaxing the collision constraints and enforcing them only when required. The approach was validated both through extensive simulations for a wide range of randomly generated transitions and with teams of up to 25 quadrotors flying in confined indoor spaces.}
}

Fast and in sync: periodic swarm patterns for quadrotors
X. Du, C. E. Luis, M. Vukosavljev, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2019. Submitted.
[View BibTeX] [View Abstract] [Download PDF] [View Video] [More Information]

This paper aims to design quadrotor swarm performances, where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.

@INPROCEEDINGS{du-icra19,
author = {Xintong Du and Carlos E. Luis and Marijan Vukosavljev and Angela P. Schoellig},
title = {Fast and in sync: periodic swarm patterns for quadrotors},
booktitle = {{Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}},
year = {2019},
note = {Submitted},
urllink = {https://arxiv.org/abs/1810.03572},
urlvideo = {https://drive.google.com/file/d/1D9CTpYSjdFHNjiYsFWeOI3--Ve1-3Bfp/view},
abstract = {This paper aims to design quadrotor swarm performances, where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.},
}

[DOI] Distributed iterative learning control for a team of quadrotors
A. Hock and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2016, pp. 4640-4646.
[View BibTeX] [View Abstract] [Download PDF] [View Video] [View 2nd Video] [Download Slides] [More Information]

The goal of this work is to enable a team of quadrotors to learn how to accurately track a desired trajectory while holding a given formation. We solve this problem in a distributed manner, where each vehicle has only access to the information of its neighbors. The desired trajectory is only available to one (or few) vehicles. We present a distributed iterative learning control (ILC) approach where each vehicle learns from the experience of its own and its neighbors’ previous task repetitions, and adapts its feedforward input to improve performance. Existing algorithms are extended in theory to make them more applicable to real-world experiments. In particular, we prove stability for any causal learning function with gains chosen according to a simple scalar condition. Previous proofs were restricted to a specific learning function that only depends on the tracking error derivative (D-type ILC). Our extension provides more degrees of freedom in the ILC design and, as a result, better performance can be achieved. We also show that stability is not affected by a linear dynamic coupling between neighbors. This allows us to use an additional consensus feedback controller to compensate for non-repetitive disturbances. Experiments with two quadrotors attest the effectiveness of the proposed distributed multi-agent ILC approach. This is the first work to show distributed ILC in experiment.

@INPROCEEDINGS{hock-cdc16,
author = {Andreas Hock and Angela P. Schoellig},
title = {Distributed iterative learning control for a team of quadrotors},
booktitle = {{Proc. of the IEEE Conference on Decision and Control (CDC)}},
year = {2016},
pages = {4640-4646},
doi = {10.1109/CDC.2016.7798976},
urllink = {http://arxiv.org/ads/1603.05933},
urlvideo = {https://youtu.be/Qw598DRw6-Q},
urlvideo2 = {https://youtu.be/JppRu26eZgI},
urlslides = {../../wp-content/papercite-data/slides/hock-cdc16-slides.pdf},
abstract = {The goal of this work is to enable a team of quadrotors to learn how to accurately track a desired trajectory while holding a given formation. We solve this problem in a distributed manner, where each vehicle has only access to the information of its neighbors. The desired trajectory is only available to one (or few) vehicles. We present a distributed iterative learning control (ILC) approach where each vehicle learns from the experience of its own and its neighbors’ previous task repetitions, and adapts its feedforward input to improve performance. Existing algorithms are extended in theory to make them more applicable to real-world experiments. In particular, we prove stability for any causal learning function with gains chosen according to a simple scalar condition. Previous proofs were restricted to a specific learning function that only depends on the tracking error derivative (D-type ILC). Our extension provides more degrees of freedom in the ILC design and, as a result, better performance can be achieved. We also show that stability is not affected by a linear dynamic coupling between neighbors. This allows us to use an additional consensus feedback controller to compensate for non-repetitive disturbances. Experiments with two quadrotors attest the effectiveness of the proposed distributed multi-agent ILC approach. This is the first work to show distributed ILC in experiment.},
}

University of Toronto Institute for Aerospace Studies