🤖 AI Summary
This work addresses the challenge of motion planning under dynamic constraints while simultaneously achieving rapid acquisition of high-quality initial solutions and efficient convergence to optimality. To this end, the paper proposes the BTIT* algorithm, which integrates anytime bidirectional heuristic search with a novel, strictly verifiable termination criterion—adapted for the first time into MEET-class algorithms—to enable meet-in-the-middle behavior during sampling while preserving asymptotic optimality. BTIT* further supports online early termination within batch sampling, guaranteeing MM-optimality. Experimental results demonstrate that BTIT* significantly reduces time-to-first-solution and accelerates convergence on both a 4D double-integrator system and a 10D linearized quadrotor benchmark, outperforming existing non-lazy informed batch planners.
📝 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.