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Found 5 papers, 1 papers with code
Progressive Smoothing for Motion Planning in Real-Time NMPC
Nonlinear model predictive control (NMPC) is a popular strategy for solving motion planning problems, including obstacle avoidance constraints, in autonomous driving applications.
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning
For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based trajectory planning.
Friction-Adaptive Stochastic Nonlinear Model Predictive Control for Autonomous Vehicles
The estimators output the estimate of the tire-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes.
acados: a modular open-source framework for fast embedded optimal control
The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications.
Optimization and Control
Approximate Dynamic Programming For Linear Systems with State and Input Constraints
Enforcing state and input constraints during reinforcement learning (RL) in continuous state spaces is an open but crucial problem which remains a roadblock to using RL in safety-critical applications.
