Problem
Research questions and friction points this paper is trying to address.
Real-time motion planning
RRT*
high-dimensional environments
parallelism
end-to-end complexity
ScaleMPA: Rethinking Scalable RRT* Acceleration With a Grid-Native Representation
🤖 AI Summary
为解决大规模高维度环境下的实时运动规划问题，本文提出ScaleMPA，采用基于网格的原生表示方法加速RRT*算法，实现毫秒级规划延迟。
📝 Abstract
Real-time motion planning remains challenging in large and high-dimensional environments. Prior acceleration of RRT* follows tree-centric state organization, which reduces per-query cost but preserves superlinear end-to-end complexity and limits parallelism through structural dependencies. This paper presents ScaleMPA, a motion-planning accelerator that rethinks RRT* with a grid-native representation. By replacing hierarchical traversal with direct grid-based access, ScaleMPA reduces the planner critical path and exposes fine-grained parallelism. To make this reformulation practical under sparse high-dimensional planning, ScaleMPA further proposes a multi-resolution grid search engine and a hash-grid memory system. Implemented in 28 nm CMOS, ScaleMPA achieves millisecond-level planning latency and delivers 4.7$\times$--44.4$\times$ speedup over state-of-the-art motion-planning accelerators.
Innovation
Methods, ideas, or system contributions that make the work stand out.
grid-native representation
fine-grained parallelism
multi-resolution grid search engine
hash-grid memory system
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