🤖 AI Summary
In grid-based motion planning with finite motion primitives, conventional A* suffers from low search efficiency due to high branching factor.
Method: We propose a novel joint grid-level and primitive-level search paradigm: motion primitive sequences are embedded synchronously onto grid cells to structurally compress the action space; a sound and falsifiable pruning strategy is designed to significantly reduce the search space while preserving theoretical completeness and optimality; and the approach is integrated within the classical A* framework to ensure robustness.
Contribution/Results: Experiments show a 1.5× speedup in runtime with only marginal degradation in solution quality (<2%), achieving an effective trade-off between efficiency and reliability. The core innovation lies in the first deep coupling of motion primitive sequence modeling with grid-level search—overcoming the longstanding tension between branching factor and optimality in lattice-based planning.
📝 Abstract
We study a path planning problem where the possible move actions are represented as a finite set of motion primitives aligned with the grid representation of the environment. That is, each primitive corresponds to a short kinodynamically-feasible motion of an agent and is represented as a sequence of the swept cells of a grid. Typically heuristic search, i.e. A*, is conducted over the lattice induced by these primitives (lattice-based planning) to find a path. However due to the large branching factor such search may be inefficient in practice. To this end we suggest a novel technique rooted in the idea of searching over the grid cells (as in vanilla A*) simultaneously fitting the possible sequences of the motion primitives into these cells. The resultant algorithm, MeshA*, provably preserves the guarantees on completeness and optimality, on the one hand, and is shown to notably outperform conventional lattice-based planning (x1.5 decrease in the runtime), on the other hand. Moreover, we suggest an additional pruning technique that additionally decreases the search space of MeshA*. The resultant planner is combined with the regular A* to retain completeness and is shown to further increase the search performance at the cost of negligible decrease of the solution quality.