564 research outputs found
Fault-tolerant formation driving mechanism designed for heterogeneous MAVs-UGVs groups
A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper
CrazyChoir: Flying Swarms of Crazyflie Quadrotors in ROS 2
This paper introduces CrazyChoir, a modular Python framework based on the
Robot Operating System (ROS) 2. The toolbox provides a comprehensive set of
functionalities to simulate and run experiments on teams of cooperating
Crazyflie nano-quadrotors. Specifically, it allows users to perform realistic
simulations over robotic simulators as, e.g., Webots and includes bindings of
the firmware control and planning functions. The toolbox also provides
libraries to perform radio communication with Crazyflie directly inside ROS 2
scripts. The package can be thus used to design, implement and test planning
strategies and control schemes for a Crazyflie nano-quadrotor. Moreover, the
modular structure of CrazyChoir allows users to easily implement online
distributed optimization and control schemes over multiple quadrotors. The
CrazyChoir package is validated via simulations and experiments on a swarm of
Crazyflies for formation control, pickup-and-delivery vehicle routing and
trajectory tracking tasks. CrazyChoir is available at
https://github.com/OPT4SMART/crazychoir
Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
We present a novel method for guiding a large-scale swarm of autonomous
agents into a desired formation shape in a distributed and scalable manner. Our
Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC)
algorithm adopts an Eulerian framework, where the physical space is partitioned
into bins and the swarm's density distribution over each bin is controlled.
Each agent determines its bin transition probabilities using a
time-inhomogeneous Markov chain. These time-varying Markov matrices are
constructed by each agent in real-time using the feedback from the current
swarm distribution, which is estimated in a distributed manner. The PSG-IMC
algorithm minimizes the expected cost of the transitions per time instant,
required to achieve and maintain the desired formation shape, even when agents
are added to or removed from the swarm. The algorithm scales well with a large
number of agents and complex formation shapes, and can also be adapted for area
exploration applications. We demonstrate the effectiveness of this proposed
swarm guidance algorithm by using results of numerical simulations and hardware
experiments with multiple quadrotors.Comment: Submitted to IEEE Transactions on Robotic
AMSwarmX: Safe Swarm Coordination in CompleX Environments via Implicit Non-Convex Decomposition of the Obstacle-Free Space
Quadrotor motion planning in complex environments leverage the concept of
safe flight corridor (SFC) to facilitate static obstacle avoidance. Typically,
SFCs are constructed through convex decomposition of the environment's free
space into cuboids, convex polyhedra, or spheres. However, when dealing with a
quadrotor swarm, such SFCs can be overly conservative, substantially limiting
the available free space for quadrotors to coordinate. This paper presents an
Alternating Minimization-based approach that does not require building a
conservative free-space approximation. Instead, both static and dynamic
collision constraints are treated in a unified manner. Dynamic collisions are
handled based on shared position trajectories of the quadrotors. Static
obstacle avoidance is coupled with distance queries from the Octomap, providing
an implicit non-convex decomposition of free space. As a result, our approach
is scalable to arbitrary complex environments. Through extensive comparisons in
simulation, we demonstrate a improvement in success rate, an average
reduction in mission completion time, and an average
reduction in per-agent computation time compared to SFC-based approaches. We
also experimentally validated our approach using a Crazyflie quadrotor swarm of
up to 12 quadrotors in obstacle-rich environments. The code, supplementary
materials, and videos are released for reference.Comment: Submitted to ICRA 202
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