Just-in-Time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
🤖 AI Summary
Path planning for high-dimensional robotic systems in complex, cluttered environments is often hindered by kinematic singularities and self-collision constraints, making it challenging to simultaneously ensure feasibility and optimality. This work proposes JIT*, a sampling-based planner that integrates just-in-time edge connection optimization, dynamic resampling in bottleneck regions, and a manipulability-aware motion quality evaluation module. By dynamically balancing trajectory cost against manipulability, JIT* achieves safe, efficient, and asymptotically optimal path planning. Experimental results demonstrate that the method significantly outperforms conventional sampling-based planners in configuration spaces ranging from ℝ⁴ to ℝ¹⁶ and validates its effectiveness on both single-arm and dual-arm manipulation tasks.
Application Category
📝 Abstract
In high-dimensional robotic path planning, traditional sampling-based methods often struggle to efficiently identify both feasible and optimal paths in complex, multiobstacle environments. This challenge is intensified in robotic manipulators, where the risk of kinematic singularities and self-collisions further complicates motion efficiency and safety. To address these issues, we introduce the just-in-time informed trees (JIT*) algorithm, an enhancement over effort informed trees, designed to improve path planning through two core modules: 1) the just-in-time module; and 2) the motion performance module. The just-in-time module includes “Just-in-Time Edge,” which dynamically refines edge connectivity, and “Just-in-Time Sample,” which adjusts sampling density in bottleneck areas to enable faster initial path discovery. The motion performance module balances manipulability and trajectory cost through dynamic switching, optimizing motion control while reducing the risk of singularities. Comparative analysis shows that JIT* consistently outperforms traditional sampling-based planners across <inline-formula><tex-math notation="LaTeX">$\mathbb{R}^{4}$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathbb{R}^{16}$</tex-math></inline-formula> dimensions.
Problem
Research questions and friction points this paper is trying to address.
motion planning
manipulability
kinematic singularities
self-collision
high-dimensional robotics
Innovation
Methods, ideas, or system contributions that make the work stand out.
Just-in-Time Informed Trees
manipulability-aware planning
asymptotically optimal motion planning
adaptive sampling
kinematic singularities avoidance
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