Publications by Co-Author: Grotli

F. Stoican, Prodan, I., and Grotli, E. I., “A mixed-integer implementation of the corner cutting problem in a multi-obstacle environment”, 4th European Conference on Computational Optimization. 2016.
I. Prodan, Stoican, F., and Grotli, E., “Some remarks on potential field constructions in a multi-obstacle environment”, in 10th IFAC Conference on Control Applications in Marine Systems, Trondheim, Norway, 2016.Abstract
This paper addresses a novel combination between mixed-integer representations and potential field constructions for typical multi-agent marine control problems. First, we prove that for any kind of repulsive functions applied over a function which we denote as sum function, the feasible domain is piece-wise affine (PWA). Next, concepts like hyperplane arrangements together with potential field approaches are used for providing an efficient description of the feasible non-convex domain. This combination offers an original and beneficent computation of control laws under non-convex constraints. Simulation results over a common application of obstacle avoidance, which can be extended for unmanned surface vehicles, prove the effectiveness of the proposed approach.
F. Stoican, Grotli, E., Prodan, I., and Oara, C., “On corner cutting in multi-obstacle avoidance problems”, in 5th IFAC Conference on Nonlinear Model Predictive Control, Seville, Spain, 2015.Abstract
One challenging and not extensively studied issue in obstacle avoidance is the corner cutting problem. Avoidance constraints are usually imposed at the sampling time without regards to the intra-sample behavior of the dynamics. This paper improves upon state of the art by describing a multi-obstacle environment via a hyperplane arrangement, provides a piecewise description of the forbidden regions and represents them into a combined mixed integer and predictive control formulation. Furthermore, over-approximation constraints which reduce to strictly binary conditions are discussed in detail. Illustrative proofs of concept, comparisons with the state of the art and simulation results over a classical multi-obstacle avoidance problem validate the bene ts of the proposed approach.