Collaborative Articulated-Object Manipulation with Multi-drone Systems

In the last decade, new aerial robots with enhanced actuation capabilities and equipped with articulated arms are investigated for challenging physical interaction tasks. However, because of their limited payload, researchers are trying to conceive teams of aerial manipulators  that are able to perform physical work for new applications domains like in construction, inspection, maintenance, etc.
Current works on Aerial Physical Interaction (AphI) consider one drone  with single point contacts and focus on the control of the single interaction force of the robot, assuming the contact remains constant. However, real manipulation tasks often involve multiple contacts and interaction forces, hence the need for multiple aerial manipulators working cooperatively.
Some practical examples of collaborative aerial physical interactions with fleets of drones may be: the opening of doors and removal of debris, e.g., in post-disaster areas; turning valves or stacking materials in a construction site.

The goal of this Master Thesis is to exploit recent advancements in the optimal control theory to design a manipulation planner that can effectively handle complex interactions and extend those results to multiple collaborating robots in the context of object manipulation. More precisely, we will rely Sampling-based MPC methods, also known as Model Predictive Path-Integral Control (MPPI), which should be adapted to multi-robot systems.

List of 5 bibliographical references:

1. M. Tognon, H. A. Tello Chávez, E. Gasparin, Q. Sablé, D. Bicego, A. Mallet, M. Lany, G. Santi, B. Revaz, J. Cortés, and A. Franchi, “A truly redundant aerial manipulator system with application to push-and-slide inspection in industrial plants,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1846–1851, 2019.
2. M. .Brunner, G. Rizzi, M. Studiger, R. Siegwart, and M. Tognon, “A planning-and-control framework for aerial manipulation of articulated objects,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 689–10 696, 2022.
3. G. Nava, Q. Sablé, M. Tognon, D. Pucci, and A. Franchi, “Direct force feedback control and online multi-task optimization for aerial manipulators,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 331–338, 2020.
4. O. Shorinwa and M. Schwager, "Distributed Contact-Implicit Trajectory Optimization for Collaborative Manipulation," 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), Cambridge, United Kingdom, 2021, pp. 56-65.
5. O. Shorinwa and M. Schwager, "Scalable Collaborative Manipulation with Distributed Trajectory Planning," 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020, pp. 9108-9115.