We develop a new analysis of sampling-based motion planning in Euclidean space with uniform random sampling, which significantly improves upon the celebrated result of Karaman and Frazzoli and subsequent work. In particular, we prove the existence of a critical connection radius proportional to Y(n À1=d ) for n samples and d dimensions: below this value the planner is guaranteed to fail (similarly shown by Karaman and Frazzoli). More importantly, for larger radius values the planner is asymptotically (near-)optimal. Furthermore, our analysis yields an explicit lower bound of 1 À O(n À1 ) on the probability of success. A practical implication of our work is that asymptotic (near-)optimality is achieved when each sample is connected to only Y(1) neighbors. This is in stark contrast to previous work that requires Y( log n) connections, which are induced by a radius of order log n n 1=d . Our analysis applies to the probabilistic roadmap method (PRM), as well as a variety of ''PRM-based'' planners, including RRG, FMT*, and BTT. Continuum percolation plays an important role in our proofs. Lastly, we develop similar theory for all the aforementioned planners when constructed with deterministic samples, which are then sparsified in a randomized fashion. We believe that this new model, and its analysis, is interesting in its own right.
A recent paper REF establishes the existence of a critical connection radius r * = γ 1 n 1/d , where γ is constant: if r < r * then PRM is guaranteed to fail (even when n → ∞), and if r > r * then it is guaranteed to converge a.a.s.
The critical radius in sampling-based motion planning
The International Journal of Robotics Research
Computer Science Mathematics