Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion
and manipulation which leverages trajectory optimization at each control step. While the …

Computing the gradient of rigid body dynamics is a central operation in many state-of-the-art
planning and control algorithms in robotics. Parallel computing platforms such as GPUs and …

Decentralized optimization algorithms are of interest in different contexts, eg, optimal power
flow or distributed model predictive control, as they avoid central coordination and enable …

A parallel Newton-type method for nonlinear model predictive control is presented that
exploits the particular structure of the associated discrete-time Euler–Lagrange equations …

Parallelism can be used to significantly increase the throughput of computationally
expensive algorithms. With the widespread adoption of parallel computing platforms such as …

Our quality of life, the world's productivity and its sustainability become more and more
determined by the outcome and benefits of process automation. In this domain of automatic …

Rigid body dynamics is a core technology in the robotics field. In trajectory optimization and
model predictive control algorithms, there are usually a large number of rigid body dynamics …

Automated vehicle coordination can be used to control vehicles across traffic intersections
safely and efficiently. This paper proposes a novel parallelizable algorithm, which solves the …

There has been a growing interest in parallel strategies for solving trajectory optimization
problems. One key step in many algorithmic approaches to trajectory optimization is the …

Safe and economic operation of networked systems is challenging. Optimization-based
schemes are frequently considered, since they achieve near-optimality while ensuring safety …