• Motion Planning

Motion planning for car-like vehicles is a deeply felt theme in industrial and social robotics in recent times. This type of problem is very demanding because in addition to finding complicated paths in environments full of obstacles (such as warehouses), it is necessary to take into account the kinodynamic constraints that a system on wheels enforces on the state space. In this regard, the purpose of the thesis is to find a motion planning algorithm based on sampling that is able to perform in cluttered and partially-known environments without impacting the planning time, so that it does not pass too long before the robot starts performing tasks after they are sent to it.