Sampling Strategies for Path Planning under Kinematic Constraints
This work addresses a specific bottleneck in robotics and motion planning for systems with kinematic constraints, offering incremental improvements over prior methods.
The paper tackles the narrow passage problem in probabilistic path planning for kinematically-constrained systems, where existing sampling bias methods fail due to non-parametrizable manifolds, and proposes three new strategies to address this issue.
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.