Just-in-Time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
Cai, Kuanqi Zhang, Liding Su, Xinwen Chen, Kejia Wang, Chaoqun Haddadin, Sami Knoll, Alois Ajoudani, Arash Figueredo, Luis
Cai, Kuanqi
Zhang, Liding
Su, Xinwen
Chen, Kejia
Wang, Chaoqun
Haddadin, Sami
Knoll, Alois
Ajoudani, Arash
Figueredo, Luis
Supervisor
Department
Robotics
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Type
Journal article
Date
2025
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Language
English
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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 \mathbb {R}^{4} to \mathbb {R}^{16} dimensions. © 1996-2012 IEEE.
Citation
K. Cai et al., "Just-in-Time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning," in IEEE/ASME Transactions on Mechatronics, doi: 10.1109/TMECH.2025.3570573
Source
IEEE/ASME Transactions on Mechatronics
Conference
Keywords
Collision avoidance, Manipulable, Optimal planning, Sampling-based path planning
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Source
Publisher
IEEE