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Found 5 papers, 2 papers with code
Collision-free Motion Planning for Mobile Robots by Zero-order Robust Optimization-based MPC
This paper presents an implementation of robust model predictive control (MPC) for collision-free reference trajectory tracking for mobile robots.
End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control
These models need to precisely capture the robot dynamics, which consist of well-understood components, e. g., rigid body dynamics, and effects that remain challenging to capture, e. g., stick-slip friction and mechanical flexibilities.
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning
We adopt a recent probabilistic recurrent neural network architecture, called Re-current Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions.
A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph
A common mitigation approach is to re-solve the MAPF using the current, delayed AGV positions.
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
