TY - JOUR
AU - Eiben, Eduard
AU - Gemmell, Jonathan
AU - Kanj, Iyad
AU - Youngdahl, Andrew
PY - 2018/04/26
Y2 - 2021/10/26
TI - Improved Results for Minimum Constraint Removal
JF - Proceedings of the AAAI Conference on Artificial Intelligence
JA - AAAI
VL - 32
IS - 1
SE - AAAI Technical Track: Robotics
DO -
UR - https://ojs.aaai.org/index.php/AAAI/article/view/12100
SP -
AB - <p> Given a set of obstacles and two designated points in the plane, the Minimum Constraint Removal problem asks for a minimum number of obstacles that can be removed so that a collision-free path exists between the two designated points. It is a well-studied problem in both robotic motion planning and wireless computing that has been shown to be NP-hard in various settings. In this work, we extend the study of Minimum Constraint Removal. We start by presenting refined NP-hardness reductions for the two cases: (1) when all the obstacles are axes-parallel rectangles, and (2) when all the obstacles are line segments such that no three intersect at the same point. These results improve on existing results in the literature. As a byproduct of our NP-hardness reductions, we prove that, unless the Exponential-Time Hypothesis (ETH) fails, Minimum Constraint Removal cannot be solved in subexponential time 2<sup><em>o</em>(<em>n</em>)</sup>, where <em>n</em> is the number of obstacles in the instance. This shows that significant improvement on the brute-force 2<sup><em>O</em>(<em>n</em>)</sup>-time algorithm is unlikely. We then present a subexponential-time algorithm for instances of Minimum Constraint Removal in which the number of obstacles that overlap at any point is constant; the algorithm runs in time 2<sup><em>O</em>(√<em>N</em>)</sup>, where <em>N</em> is the number of the vertices in the auxiliary graph associated with the instance of the problem. We show that significant improvement on this algorithm is unlikely by showing that, unless ETH fails, Minimum Constraint Removal with bounded overlap number cannot be solved in time 2<sup><em>o</em>(√<em>N</em>)</sup>. We describe several exact algorithms and approximation algorithms that leverage heuristics and discuss their performance in an extensive empirical simulation. </p>
ER -