Year: 2018
Type: conference-paper
Abstract: We employ the proximal averaged Newton-type method for optimal control (PANOC) to solve obstacle avoidance problems in real time. We introduce a novel modeling framework for obstacle avoidance which allows us to easily account for generic, possibly nonconvex, obstacles involving polytopes, ellipsoids, semialgebraic sets and generic sets described by a set of nonlinear inequalities. PANOC is particularly well-suited for embedded applications as it involves simple steps, its implementation comes with a low memory footprint and its fast convergence meets the tight runtime requirements of fast dynamical systems one encounters in modern mechatronics and robotics. The proposed obstacle avoidance scheme is tested on a lab-scale autonomous vehicle.
Institution: KU Leuven
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: Structured Low-Rank matrix / tensor approximation: numerical optimization-based algorithms and applications, A unifying algorithmic framework for structured optimization, G4576982880, Distributed algorithms for large-scale optimization over graphs, Risk-averse Control and Dynamic Optimization: Bridging the Gap Between Robust and Stochastic Control, G6659609114, G6927371360, G7564167306
OpenAlex ID: W2950312551