text: A well-known weakness of the probabilistic path planners is the so-called narrow passage problem, where a region with a relatively low probability of being sampled must be explored to find a solution path. Many strategies have been proposed to alleviate this problem, most of them based on biasing the sampling distribution. When kinematic constraints appear in the problem, the configuration space typically becomes a non-parametrizable, implicit manifold. Unfortunately, this invalidates most of the existing sampling bias approaches, which rely on an explicit parametrization of the space to explore. In this paper, we propose and evaluate three novel strategies to bias the sampling under the presence of narrow passages in kinematically-constrained systems.
  @cite_28: Randomly expanding trees are very effective in exploring high-dimensional spaces. Consequently, they are a powerful algorithmic approach to sampling-based single-query motion planning. As the dimensionality of the configuration space increases, however, the performance of tree-based planners that use uniform expansion degrades. To address this challenge, we present a utility-guided algorithm for the online adaptation of the random tree expansion strategy. This algorithm guides expansion towards regions of maximum utility based on local characteristics of state space. To guide exploration, the algorithm adjusts the parameters that control random tree expansion in response to state space information obtained during the planning process. We present experimental results to demonstrate that the resulting single-query planner is computationally more efficient and more robust than previous planners in challenging artificial and real-world environments.
  @cite_14: Sampling based planners have become increasingly efficient in solving the problems of classical motion planning and its applications. In particular, techniques based on the rapidly-exploring random trees (RRTs) have generated highly successful single-query planners. Recently, a variant of this planner called dynamic-domain RRT was introduced by (2005). It relies on a new sampling scheme that improves the performance of the RRT approach on many motion planning problems. One of the drawbacks of this method is that it introduces a new parameter that requires careful tuning. In this paper we analyze the influence of this parameter and propose a new variant of the dynamic-domain RRT, which iteratively adapts the sampling domain for the Voronoi region of each node during the search process. This allows automatic tuning of the parameter and significantly increases the robustness of the algorithm. The resulting variant of the algorithm has been tested on several path planning problems.
  @cite_17: 
summary: The information of the collisions detected while extending an Rapidly-exploring Random Tree (RRT) can be leveraged to bias the sampling @cite_28 . For instance, using this information the dynamic domain of an RRT node can be defined, i.e, the part of the configuration space where it is worth to extend the tree from that node @cite_17 @cite_14 . As we will see, this approach can be adapted to the kinematically-constrained case defining the dynamic domain in the ambient space rather than in the configuration space.