Motion planning for mobile robots using uncertain estimations about the environment
Zoltán Gyenes, Emese Gincsainé Szádeczky-Kardoss · 2020
The motion planning for mobile robots is a challenging task even if the agent has to reach the target position in a dense, dynamic environment. In this paper, our goal is to develop a motion planning algorithm using the changing uncertainties of the sensor-based data of the obstacles. The collision-free motion must be ensured by the algorithm using a cost function optimization method. As an assumption, some of the data of the obstacles (e.g. positions of the static obstacles) are already known at the beginning of the planning, and the other information (e.g. velocity vectors of moving obstacles) must be measured using the sensors. The algorithm is tested in simulations.