Autonomous Drones for Emergency Responders

People

Dennis Benders - PhD candidate

Max Lodel - PhD candidate

Thijs Niesten - Research Engineer

Prof. Laura Ferranti - Reliable Control (R2C) Lab TU Delft

Prof. Javier Alonso-Mora - Autonomous Multi-Robot Lab (AMR) TU Delft

Prof. Robert Babuska - Autonomous Multi-Robot Lab (AMR) TU Delft

Funding

This project is funded by the National Police (Politie) of the Netherlands.

About the Project

How can autonomous drones support operations of emergency responders such as the police? This project targets scenarios such as search and rescue or reconnaissance in large, unknown and potentially hazardous environments, where it can be difficult or even dangerous for policemen to operate and fulfil the task themselves. In this project we aim to enable drones to operator in such remote environments and gather information required by police operators. We develop methods to control entire teams of drones, so they can fly safely between obstacles and are robust to unexpected disturbances, and can navigate unknown environments to provide the required information.

Related Publications

Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
M. Lodel, B. Brito, A. Serra-Gomez, L. Ferranti, R. Babuska, J. Alonso-Mora. In Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2022.

abstract web pdf video

Abstract: Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.