AI & ML
SCET-CBF: Speed-Calibrated Soft Guidance for One-Step Dynamic Navigation
arXiv cs.RO0 views
arXiv:2610.33471v1 Announce Type: new Abstract: Control barrier functions (CBFs) support collision avoidance, yet dense dynamic encounters can leave a vehicle with no feasible command. Geometric refinements and soft constraints help preserve motion choices. Using these choices also requires accounting for the vehicle's changing turning response. For a steering-limited bicycle, braking can lower closing speed while slowing the lateral motion needed before an obstacle arrives. We propose SCET-CBF, a speed-calibrated soft guidance law for one-step dynamic navigation. A short lateral escape maneuver gives an allowable approach speed. Its speed derivative allows a quadratic program to weigh both effects of braking when choosing acceleration and steering. Soft penalties accommodate competing obstacle preferences within the input set permitted by segment and braking constraints. We derive the guidance and establish protection over each accepted motion segment, together with conditional braking invariance for a single obstacle. On 2500 paired tasks with a 60 s deadline, SCET achieves 72.8% completion, compared with 49.8% for CBF/velocity-obstacle control and 69.8% for an enhanced 1 s CBF-MPC. At 100 obstacles, SCET and the enhanced MPC complete 52.4% and 33.6% of tasks. Paired studies identify the contributions of speed feedback and early activation. Further evaluations examine the operating range under lateral acceleration limits and bounded motion and execution errors.
Source:https://arxiv.org/abs/2610.33471PreviousMOTIO: Agentic Skill Engineering for Reality-Grounded Humanoid Loco-ManipulationNextA Differentiable Chassis Safety Layer for Intelligent Vehicle Motion Control
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