  Sampling-based motion planners perform exceptionally well in robotic
applications that operate in high-dimensional space. However, most works often
constrain the planning workspace rooted at some fixed locations, do not
adaptively reason on strategy in narrow passages, and ignore valuable local
structure information. In this paper, we propose Rapidly-exploring Random
Forest (RRF*) -- a generalised multi-trees motion planner that combines the
rapid exploring property of tree-based methods and adaptively learns to deploys
a Bayesian local sampling strategy in regions that are deemed to be
bottlenecks. Local sampling exploits the local-connectivity of spaces via
Markov Chain random sampling, which is updated sequentially with a Bayesian
proposal distribution to learns the local structure from past observations. The
trees selection problem is formulated as a multi-armed bandit problem, which
efficiently allocates resources on the most promising tree to accelerate
planning runtime. RRF* learns the region that is difficult to perform tree
extensions and adaptively deploys local sampling in those regions to maximise
the benefit of exploiting local structure. We provide rigorous proofs of
completeness and optimal convergence guarantees, and we experimentally
demonstrate that the effectiveness of RRF*'s adaptive multi-trees approach
allows it to performs well in a wide range of problems.
