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AC-SMPC: An Actor-Critic MPC Navigation Framework with State Constraints
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Chaoran Wu and other authors propose AC-SMPC, an actor-critic model predictive control framework with state constraints for safe navigation in constrained dynamic environments. The method represents the robot and obstacles as vertices and the workspace as half-spaces, locally linearizes the nonlinear dynamics, and convexifies hyperplane-based obstacle-avoidance constraints into a differentiable MPC layer; the actor adjusts MPC objective parameters through closed-loop feedback, and the critic evaluates long-term task performance. The authors state that the framework has been validated in simulation tests and experiments, involving dynamic obstacle avoidance by multiple robots in narrow scenarios.
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- AC-SMPC: Differentiable Actor-Critic MPC with State Constraints for Safe and Fast Robot Navigation
The authors propose AC-SMPC, an actor-critic model predictive control framework with state constraints for safe navigation in constrained dynamic environments. The method represents the robot and obstacles as vertices and the workspace as half-spaces, locally linearizes the nonlinear dynamics, and convexifies hyperplane-based collision avoidance constraints into a differentiable MPC layer; the actor adjusts MPC objective parameters via closed-loop feedback, while the critic evaluates long-term task performance.
arXiv cs.RO ·
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