Hdmove2 [2025-2027]

[ \mathcalJ[\tau] = \int_0^T \left( \underbrace \textkinetic energy + \lambda_1 \underbrace^2 \textjerk + \lambda_2 \underbracec_obs(\tau(t))_\textcollision cost \right) dt ]

[5] T. P. Lillicrap et al., "Continuous control with deep reinforcement learning," International Conference on Learning Representations (ICLR) , 2016.

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

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.

[ \exists t: | q_actual(t) - \tau_planned(t) | > \sigma \cdot \textVar s \in [t-\delta,t] \left[ \frac\partial c obs\partial q(s) \right] ] The lower level is solved using a fast

[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.

[ q^* = \arg\min_q \in Q | q - D(z^*) |^2 \quad \texts.t. \quad q \in Q_free ] [ \exists t: | q_actual(t) - \tau_planned(t) |

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