3 accepted papers
Motion planning under differential constraints is one of the canonical problems in robotics. State-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs). However, there are still challenges remaining, for example, ho…
Several sampling-based algorithms have been recently proposed that ensure asymptotic optimality. The convergence of these algorithms can be improved if sampling is guided toward the most promising region of the search space where the solution is more likely to be found. In this paper we propose thre…
We propose a machine learning (ML)-inspired approach to estimate the relevant region of a motion planning problem during the exploration phase of sampling-based path-planners. The algorithm guides the exploration so that it draws more samples from the relevant region as the number of iterations incr…