WASP Optimization for Learning and Autonomy 

Shervin Parvini and Anders Hansson

Within the WASP research environment Optimization for Learning and Autonomy within the Division of Automatic Control we work with both development and applications of optimization.

The development of optimization methods is targeting structure-exploitation of classes of optimization problems, e.g. for learning and control, in order to increase the efficiency of optimization solvers.

This development also includes utilization of modern parallel hardware. Furthermore, researched is performed to tightly combine planning methods known from AI with methods known from optimal control. Moreover, ongoing theoretical and algorithmic developments include real-time certification of optimization methods. Applications include motion planning and control for autonomous vehicles as well as implementations of optimization methods.

The work is carried out within the WASP cluster Large-Scale Optimization and Control. We collaborate with universities both in Sweden and abroad, among others UCLA, ETH, TUDelft, DTU, University of Siena, KTH, and Lund University. Industrial research partners include Scania and SAAB.


WASP research optimization