מירב זהבי

אקדמי בכיר

The parameterized complexity of motion planning for snake-like robots

Siddharth Gupta, Guy Sa'ar, Meirav Zehavi

We study a motion-planning problem inspired by the game Snake that models scenarios like the transportation of linked wagons towed by a locomotor to the movement of a group of agents that travel in an “ant-like” fashion. Given a “snakelike” robot with initial and final positions in an environment modeled by a graph, our goal is to decide whether the robot can reach the final position from the initial position without intersecting itself. Already on grid graphs, this problem is PSPACE-complete [Biasi and Ophelders, 2018]. Nevertheless, we prove that even on general graphs, it is solvable in time kO(k)|I|O(1) where k is the size of the robot, and |I| is the input size. Towards this, we give a novel application of color-coding to sparsify the configuration graph of the problem. We also show that the problem is unlikely to have a polynomial kernel even on grid graphs, but it admits a treewidth-reduction procedure. To the best of our knowledge, the study of the parameterized complexity of motion problems has been largely neglected, thus our work is pioneering in this regard.

שפת פרסום אנגלית
דפים 5670-5676
סטטוס פרסום פורסם - 01.01.2019

ASJC Scopus subject areas

Artificial Intelligence
גישה למסמך
10.24963/ijcai.2019/786
קבצים וקישורים אחרים
Link to publication in Scopus