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LACART: Lookahead-Augmented Collision Avoidance and Reference Tracking Strategies for Safety-Critical Robots
首次提交 2026-11-06 23:52
版本更新 2026-11-06 23:52
雷达观测 2026-11-09 13:22
摘要
Autonomous racing provides a demanding testbed for collision avoidance under tight geometric and computational constraints. Building on a recent conference contribution and a master's dissertation, LACART combines lookahead-based control barrier functions (CBFs), a relaxed control Lyapunov function (CLF), and quadratic programming (QP) to modify nominal vehicle commands for obstacle avoidance and reference tracking. The method handles circular obstacles and track boundaries in a common optimisation layer. Comparative simulation results against Huang \emph{et al.} \cite{r9} show similar full-track paths and lower median computation times: 0.31 versus 0.33 ms per step and 0.023 versus 0.025 ms per QP solve. The dissertation further demonstrates a 1:43-scale vehicle with camera-based estimation and wireless actuation at a reported 25 Hz control rate. We identify the conditions linking lookahead-point clearance to body clearance, illustrate hardware limitations, and propose a recorded demonstration of safety intervention and recovery. The contribution targets ground-robot collision avoidance and real-time safety monitoring.
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v1 · 2026-11-06 23:52 · 最新观测版本
LACART: Lookahead-Augmented Collision Avoidance and Reference Tracking Strategies for Safety-Critical Robots
Autonomous racing provides a demanding testbed for collision avoidance under tight geometric and computational constraints. Building on a recent conference contribution and a master's dissertation, LACART combines lookahead-based control barrier functions (CBFs), a relaxed control Lyapunov function (CLF), and quadratic programming (QP) to modify nominal vehicle commands for obstacle avoidance and reference tracking. The method handles circular obstacles and track boundaries in a common optimisation layer. Comparative simulation results against Huang \emph{et al.} \cite{r9} show similar full-track paths and lower median computation times: 0.31 versus 0.33 ms per step and 0.023 versus 0.025 ms per QP solve. The dissertation further demonstrates a 1:43-scale vehicle with camera-based estimation and wireless actuation at a reported 25 Hz control rate. We identify the conditions linking lookahead-point clearance to body clearance, illustrate hardware limitations, and propose a recorded demonstration of safety intervention and recovery. The contribution targets ground-robot collision avoidance and real-time safety monitoring.
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