Optimal Kinodynamic Motion Planning Through Anytime Bidirectional Heuristic Search with Tight Termination Condition
Yi Wang, Bingxian Mu, Shahab Shokouhi, May-Win Thein
TLDR
BTIT* is an anytime, asymptotically optimal kinodynamic motion planning algorithm using bidirectional heuristic search for faster solutions and convergence.
Key contributions
- Introduces BTIT*, an asymptotically optimal kinodynamic sampling-based motion planning algorithm.
- Integrates anytime bidirectional heuristic search (Bi-HS) with meet-in-the-middle optimality.
- First anytime MEET-style algorithm with efficient, on-the-fly termination conditions.
- Demonstrates faster time-to-first-solution and improved convergence on kinodynamic benchmarks.
Why it matters
This paper introduces BTIT*, significantly improving kinodynamic motion planning. Its efficient termination and faster convergence make it highly practical for complex robotic systems, setting a new state-of-the-art for informed batch planners.
Original Abstract
This paper introduces Bidirectional Tight Informed Trees (BTIT*), an asymptotically optimal kinodynamic sampling-based motion planning algorithm that integrates an anytime bidirectional heuristic search (Bi-HS) and ensures the \emph{meet-in-the-middle} property (MMP) and optimality (MM-optimality). BTIT* is the first anytime MEET-style algorithm to utilize termination conditions that are efficient to evaluate and enable early termination \emph{on-the-fly} in batch-wise sampling-based motion planning. Experiments show that BTIT* achieves strongly faster time-to-first-solution and improved convergence than representative \emph{non-lazy} informed batch planners on two kinodynamic benchmarks: a 4D double-integrator model and a 10D linearized Quadrotor. The source code is available here.
📬 Weekly AI Paper Digest
Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.