Rendez-vous and Autonomous Landing of a Quadrotor on an Catamaran robot boat
Experiment realized by University of Genova
Experiment realized by University of Genova
Emulation experiments in ROS Gazebo of Autonomous Landing of a Quadrotor on a Catamaran robot boat
The video shows experiments done in emulation and realized in ROS Gazebo with a marine plug-in. Movement of the catamaran robot boat is reproduced from an an experiment with the real boat, while the movement of the quadrotor is real-time computed using a hardware in the loop setup.
Experiment realized by University of Genova
The video shows experiments done in emulation and realized in ROS Gazebo with a marine plug-in. Movement of the catamaran robot boat is reproduced from an an experiment with the real boat, while the movement of the quadrotor is real-time computed using a hardware in the loop setup.
Experiment realized by University of Genova
Swarm algorithm for the collective estimation of a bathymetry map, emulation experiment.
A swarm algorithm for the collective estimation of a bathymetry map. The simulated robots learn a Gaussian process modeling a regression map of a bathymetric profile. Robots can enjoy full connectivity in the communication network, but act independently. The system is fully distributed. Each robot, iteratively, identifies an elliptical region where to gather observation data. The experiment, with five water surface robots (USVs), is an emulation one: robots' sensors are fed with real data (from the GEBCO repository) and corrupted by Gaussian noise. Robot's move according to a purely kinematic model. The learned map is a 6x6 Km squared region in front of Genova, in Italy.
Details of the algorithm are in: G.A. Di Caro and A.W. Ziaullah Yousaf. Map learning via adaptive region-based sampling in multi-robot systems, 15th International Symposium on Distributed Autonomous Robotic Systems (DARS), (Online) Kyoto, Japan, June 1–4, 2021.
Experiment realized by CMU-Q
A swarm algorithm for the collective estimation of a bathymetry map. The simulated robots learn a Gaussian process modeling a regression map of a bathymetric profile. Robots can enjoy full connectivity in the communication network, but act independently. The system is fully distributed. Each robot, iteratively, identifies an elliptical region where to gather observation data. The experiment, with five water surface robots (USVs), is an emulation one: robots' sensors are fed with real data (from the GEBCO repository) and corrupted by Gaussian noise. Robot's move according to a purely kinematic model. The learned map is a 6x6 Km squared region in front of Genova, in Italy.
Details of the algorithm are in: G.A. Di Caro and A.W. Ziaullah Yousaf. Map learning via adaptive region-based sampling in multi-robot systems, 15th International Symposium on Distributed Autonomous Robotic Systems (DARS), (Online) Kyoto, Japan, June 1–4, 2021.
Experiment realized by CMU-Q
Leader-Follower algorithm for the collective estimation of a bathymetry map, emulation experiment.
A distributed algorithm for the cooperative estimation of a data/bathymetry map. The simulated robots learn a Gaussian process modeling a regression map of a bathymetric profile. Robots act as a group, where a leader robot orchestrate team's actions. The team, iteratively, identifies an elliptical region where to gather observation data and move there, sharing a common GP map representing the target bathymetry map. The experiment, with five water surface robots (USVs), is an emulation one: robots' sensors are fed with real data (from the GEBCO repository) and corrupted by Gaussian noise. Robot's move according to a purely kinematic model. The learned map is a 6x6 Km squared region in front of Genova, in Italy.
Details of the algorithm are in: G.A. Di Caro and A.W. Ziaullah Yousaf. Multi-robot informative path planning using a leader-follower architecture, IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 30 - June 5, 2021
Experiment realized by CMU-Q
A distributed algorithm for the cooperative estimation of a data/bathymetry map. The simulated robots learn a Gaussian process modeling a regression map of a bathymetric profile. Robots act as a group, where a leader robot orchestrate team's actions. The team, iteratively, identifies an elliptical region where to gather observation data and move there, sharing a common GP map representing the target bathymetry map. The experiment, with five water surface robots (USVs), is an emulation one: robots' sensors are fed with real data (from the GEBCO repository) and corrupted by Gaussian noise. Robot's move according to a purely kinematic model. The learned map is a 6x6 Km squared region in front of Genova, in Italy.
Details of the algorithm are in: G.A. Di Caro and A.W. Ziaullah Yousaf. Multi-robot informative path planning using a leader-follower architecture, IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 30 - June 5, 2021
Experiment realized by CMU-Q
Integration of Leader-Follower for planning, Cooperative control for pattern formation, collision-free navigation, and connectivity maintenance, emulation experiment.
The video shows the first phase of a mission simulated in ROS Gazebo (with a marine plugin) with a team of 4 water surface robots gathering water depth data to estimate a bathymetry map. Robots move from their initial locations to the first selected region (of elliptical shape) where to take observations.
The adaptive planning strategy for selecting the ellipse and plan the sampling paths is defined by the Leader-Follower algorithm described in:G.A. Di Caro and A.W. Ziaullah Yousaf. Multi-robot informative path planning using a leader-follower architecture, IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 30 - June 5, 2021.
Motion is controlled by a cooperative control strategy that provides formation control for effective & collision-free navigation and supports maintenance of local global connectivity in the robot team.
Experiment realized by CMU-Q & University of Cassino and Southern Lazio
The video shows the first phase of a mission simulated in ROS Gazebo (with a marine plugin) with a team of 4 water surface robots gathering water depth data to estimate a bathymetry map. Robots move from their initial locations to the first selected region (of elliptical shape) where to take observations.
The adaptive planning strategy for selecting the ellipse and plan the sampling paths is defined by the Leader-Follower algorithm described in:G.A. Di Caro and A.W. Ziaullah Yousaf. Multi-robot informative path planning using a leader-follower architecture, IEEE International Conference on Robotics and Automation (ICRA), Xi’an, China, May 30 - June 5, 2021.
Motion is controlled by a cooperative control strategy that provides formation control for effective & collision-free navigation and supports maintenance of local global connectivity in the robot team.
Experiment realized by CMU-Q & University of Cassino and Southern Lazio
Non-linear Model Prediction Control (NMPC) for the Autonomous Landing of a Quadrotor on a water surface robot, simulation experiment.
The video shows the use of a strategy based on Non-linear Model Predictive Control (NMPC) for the autonomous landing of an aerial vehicle on a catamaran-shaped robot. The mission is simulated in ROS Gazebo with a marine plugin.
Details of the control algorithm can be found in: G. Gillini, F. Arrichiello, Nonlinear Model Predictive Control for the Landing of a Quadrotor on a Marine Surface Vehicle, IFAC World Congress, Germany, July 2020
Experiment University of Cassino and Southern Lazio
The video shows the use of a strategy based on Non-linear Model Predictive Control (NMPC) for the autonomous landing of an aerial vehicle on a catamaran-shaped robot. The mission is simulated in ROS Gazebo with a marine plugin.
Details of the control algorithm can be found in: G. Gillini, F. Arrichiello, Nonlinear Model Predictive Control for the Landing of a Quadrotor on a Marine Surface Vehicle, IFAC World Congress, Germany, July 2020
Experiment University of Cassino and Southern Lazio
