LiDAR-Based Navigation of Tethered Drone Formations in an Unknown Environment

The problem of navigating a formation of interconnected tethered drones, named STEM (System of TEthered Multicopters), in an unknown environment is considered. The tethers feed electrical power from a ground station to the drones and also serve as communication links. The presence of more than one interconnected drone provides enough degrees of freedom to navigate in a cluttered area. The leader drone in the formation must reach a given point of interest, while the followers must move accordingly, avoiding interference with the obstacles. The challenges are the uncertainty in the environment, with obstacles of unknown shape and position, the use of LiDAR (Light Detection And Ranging) sensors, providing only partial information of the surroundings of each drone, and the presence of the tethers, which must not impact with the obstacles and pose additional constraints to how the drones can move. To cope with these problems, a novel real-time planning algorithm based on numerical optimization is proposed: the reference position of each drone is chosen in a centralized way via a convex program, where the LiDAR scans are used to approximate the free space and the drones are moved towards suitably defined intermediate goals in order to eventually reach the point of interest. The approach is successfully tested in numerical simulations with a realistic model of the system.

[1]  Taeyoung Lee,et al.  Geometric controls for a tethered quadrotor UAV , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[2]  Kaustubh Pathak,et al.  Approaches for a tether-guided landing of an autonomous helicopter , 2006, IEEE Transactions on Robotics.

[3]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1986 .

[4]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[5]  Andrea Gasparri,et al.  Cooperative pose stabilization of an aerial vehicle through physical interaction with a team of ground robots , 2012, 2012 IEEE International Conference on Control Applications.

[6]  Lorenzo Fagiano,et al.  Systems of Tethered Multicopters: Modeling and Control Design , 2017 .

[7]  Yoshifumi Kitamura,et al.  Real-time path planning in a dynamic 3-D environment , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[8]  R. Zapata,et al.  Flying among obstacles , 1999, 1999 Third European Workshop on Advanced Mobile Robots (Eurobot'99). Proceedings (Cat. No.99EX355).

[9]  Yang Wang,et al.  Fast Model Predictive Control Using Online Optimization , 2008 .

[10]  Alberto Bemporad,et al.  A Quadratic Programming Algorithm Based on Nonnegative Least Squares With Applications to Embedded Model Predictive Control , 2016, IEEE Transactions on Automatic Control.

[11]  Antonio Franchi,et al.  Observer-Based Control of Position and Tension for an Aerial Robot Tethered to a Moving Platform , 2016, IEEE Robotics and Automation Letters.

[12]  Stephen P. Boyd,et al.  Fast Model Predictive Control Using Online Optimization , 2010, IEEE Transactions on Control Systems Technology.

[13]  Antoine Collin,et al.  Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques , 2017, Coral Reefs.

[14]  T. Rakha,et al.  Review of Unmanned Aerial System (UAS) applications in the built environment: Towards automated building inspection procedures using drones , 2018, Automation in Construction.

[15]  Chun T. Rim,et al.  Tethered aerial robots using contactless power systems for extended mission time and range , 2014, 2014 IEEE Energy Conversion Congress and Exposition (ECCE).

[16]  Gokhan Izbirak,et al.  Post-earthquake response by small UAV helicopters , 2016, Natural Hazards.

[17]  Takeo Kanade,et al.  Efficient Two-phase 3D Motion Planning for Small Fixed-wing UAVs , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[18]  Kostas E. Bekris,et al.  Asymptotically optimal sampling-based kinodynamic planning , 2014, Int. J. Robotics Res..

[19]  Raffaello D'Andrea,et al.  Stabilization of a flying vehicle on a taut tether using inertial sensing , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Dario Floreano,et al.  Fly-inspired visual steering of an ultralight indoor aircraft , 2006, IEEE Transactions on Robotics.