Optimistic planning algorithms for state-constrained optimal control problems

In this work, we study optimistic planning methods to solve some state-constrained optimal control problems in finite horizon. While classical for calculating the value function are generally based on a discretization state space, algorithms have advantage of using adaptive space. These approaches therefore very suitable where dimension variable is low and allow deal with space can be high. Our also providing, given computing resources, best strategy whose performance as close possible optimality while its corresponding trajectory comply constraints up accuracy.