During the seminar, we will present our work on multi-vehicle monitoring which
utilizes model predictive control combined with dynamical reward model to
provide vehicle trajectories.

F. Nekovář, J. Faigl and M. Saska, "Multi-Vehicle Dynamic Water Surface
Monitoring," in IEEE Robotics and Automation Letters, vol. 8, no. 10, pp.
6323-6330, Oct. 2023, doi: 10.1109/LRA.2023.3304533.

Abstract:
Repeated exploration of a water surface to detect objects of interest and their
subsequent monitoring is important in search-and-rescue or ocean clean-up
operations. Since the location of any detected object is dynamic, we propose to
address the combined surface exploration and monitoring of the detected objects
by modeling spatio-temporal reward states and coordinating a team of vehicles
to
collect the rewards. The model characterizes the dynamics of the water surface
and enables the planner to predict future system states. The state reward value
relevant to the particular water surface cell increases over time and is
nullified by being in a sensor range of a vehicle. Thus, the proposed
multi-vehicle planning approach is to minimize the collective value of the
dynamic model reward states. The purpose is to address vehicles' motion
constraints by using model predictive control on receding horizon and fully
exploiting the utilized vehicles' motion capabilities. Based on the evaluation
results, the approach indicates improvement in a solution to the kinematic
orienteering problem and the team orienteering problem in the monitoring task
compared to the existing solutions. The proposed approach has been
experimentally verified, supporting its feasibility in real-world monitoring
tasks.

Link: https://mrs.felk.cvut.cz/imp-dr

The seminar will be of a short length (20 minutes + 10 minutes discussion).