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Ph.D. in reactive planning strategies for multple drones (M/F)

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General information

Reference : UMR6074-PAOROB-003
Workplace : RENNES
Date of publication : Friday, June 26, 2020
Scientific Responsible name : Paolo Robuffo Giordano
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 1 November 2020
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

Most of the formation control algorithms are essentially local: they aim at obtaining the best control actions “right now” given the current state of the robot group, but they cannot reason about the future. However, since several years many modern control approaches for non-trivial robotics applications stress the importance of a proper trajectory planning for accomplishing a task in more robust and effective ways. Indeed, (reactive) trajectory planning allows to reason about the future consequences of local actions, to better take into account complex constraints (e.g., obstacle avoidance, limited actuation, sensing constraints), and, finally, to attain optimality w.r.t. a given criterion of interest (e.g., time, energy, control effort). While reactive trajectory planning approaches (or Model Predictive Control – MPC) have gained a lot of ground in the robotics field (one example for all, humanoid robotics), their use in the context of multi-robot formation control and localization is still quite limited. On the other hand, the complexity of controlling a multi-robot group in harsh environments (sensing constraints, limited actuation, limited communication, limited processing power, obstacle and self-collision avoidance, need to localize and estimate the group state during motion) would clearly call for the use of modern reactive trajectory planning approaches in order to better deal with the problems of formation control and localization in unstructured environments.

Our group has recently started several activities on the topic of online/reactive trajectory planning for aggressive flight of (single) quadrotor UAVs, and for optimal state estimation and execution robustness (these latter also in collaboration with the CHORALE robotics group at Inria Sophia Antipolis). These activities show a very promising potential and demonstrate our good grasp on these topics, but, so far, they have not been applied to the specific context of multi-robot formation control/localization. This is not a trivial issue, since one has to address all the typical issues/sensing/communication constraints of multi-robots, as well as comply with the requirements of decentralization and scalability (i.e., ideally, each robot should be able to plan its own future trajectory by only exploiting sensed/communicated information from the closest neighbors).

Therefore, the goal of this PhD thesis is to close this gap and develop novel reactive trajectory planning algorithms tailored to the multi-robot case. The PhD activities will naturally leverage the strong internal competences on multi-robot control/optimal estimation and on trajectory planning, and will be performed in cooperation with Inria Sophia Antipolis (in particular P. Salaris as co-encadrant). The devised algorithms will be first tested in a simulation environment and then implemented and validated on the quadrotor UAVs available in the team. If successful, this Thesis will then obtain two main goals: (1) advance the state-of-the-art in the multi-robot research by demonstrating how modern trajectory planning approaches can greatly improve the performance and execution robustness, and (2) further increase the visibility of the IRISA/Inria Rennes “drone platform” in the community by implementing the proposed approaches our quadrotor UAVs.

Work Context

The Ph.D. will be carried out in the Rainbow team common to IRISA and Inria Bretagne Atlantique

Constraints and risks


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