Hdmove2 Fix Info

[ z^* = \arg\min_z(t) \in Z \mathcalJ[D(z(t))] \quad \texts.t. \quad \texthomotopy constraints ]

hdmove2 achieves a 94% success rate on the narrow passage benchmark (64 DoF), compared to 12% for RRT* and 68% for CHOMP. The event-driven controller reduces average planning frequency by 63%, enabling 95 Hz control updates.

The lower level is solved using a fast alternating direction method of multipliers (ADMM) that converges in under 5 ms for ( n \leq 128 ). Re-planning is triggered when: hdmove2

[3] A. Sterling and J. Liu, "hdmove1: Latent motion primitives for high-DoF planning," arXiv preprint arXiv:2401.04567 , 2024.

[ \mathcalJ[\tau] = \int_0^T \left( \underbrace^2_\mathbfM \textkinetic energy + \lambda_1 \underbrace \dddot\tau(t) \textjerk + \lambda_2 \underbracec_obs(\tau(t))_\textcollision cost \right) dt ] [ z^* = \arg\min_z(t) \in Z \mathcalJ[D(z(t))] \quad \texts

where ( \mathbfM ) is a configuration-dependent inertia matrix and ( c_obs ) is a smooth barrier function. Instead of solving directly in ( Q ), hdmove2 solves:

[4] L. E. Kavraki, P. Svestka, J. C. Latombe, and M. H. Overmars, "Probabilistic roadmaps for path planning in high-dimensional configuration spaces," IEEE Transactions on Robotics and Automation , vol. 12, no. 4, pp. 566–580, 1996. The lower level is solved using a fast

| Algorithm | Success Rate (Bench B) | Planning Time (ms) | Cumulative Jerk (m²/s⁵) | Real-time feasible (>30 Hz) | |-----------|------------------------|--------------------|--------------------------|-------------------------------| | RRT* | 0.12 ± 0.05 | 3420 ± 450 | 18.4 ± 3.2 | No | | CHOMP | 0.68 ± 0.12 | 520 ± 85 | 9.2 ± 1.8 | No (for n>30) | | hdmove1 | 0.71 ± 0.10 | 88 ± 12 | 5.3 ± 0.9 | Yes (at 35 Hz) | | | 0.94 ± 0.04 | 41 ± 6 | 1.4 ± 0.3 | Yes (at 95 Hz) |