We present a safety-critical finite-time control framework for sampled-data multi-robot system coordination.We formally define and construct a finite-time discrete control Lyapunov function(FT-DCLF)and derive sufficie...We present a safety-critical finite-time control framework for sampled-data multi-robot system coordination.We formally define and construct a finite-time discrete control Lyapunov function(FT-DCLF)and derive sufficient conditions that ensure convergence within a preset number of sampling steps,thereby enhancing both applicability and convergence speed.In parallel,we improve upon a discrete control barrier function(DCBF)constraint.This constraint addresses the continuous-discrete mismatch(“safety gap”)and ensures all-time safety,while mitigating deadlock and resolving performance-safety conflicts that are common in conventional DCBFs for obstacle avoidance.Both components,along with input bounds,are integrated into a single quadratically constrained quadratic program(QCQP),augmented with feasibility-aiding slack variables for real-time implementation.Simulation results demonstrate that the proposed method outperforms conventional DCLF-DCBF approaches.展开更多
基金supported by the Joint Fund of the National Natural Science Foundation of China(No.U24A20263)the National Natural Science Foundation of China(No.62473071).
文摘We present a safety-critical finite-time control framework for sampled-data multi-robot system coordination.We formally define and construct a finite-time discrete control Lyapunov function(FT-DCLF)and derive sufficient conditions that ensure convergence within a preset number of sampling steps,thereby enhancing both applicability and convergence speed.In parallel,we improve upon a discrete control barrier function(DCBF)constraint.This constraint addresses the continuous-discrete mismatch(“safety gap”)and ensures all-time safety,while mitigating deadlock and resolving performance-safety conflicts that are common in conventional DCBFs for obstacle avoidance.Both components,along with input bounds,are integrated into a single quadratically constrained quadratic program(QCQP),augmented with feasibility-aiding slack variables for real-time implementation.Simulation results demonstrate that the proposed method outperforms conventional DCLF-DCBF approaches.
