מירב זהבי

אקדמי בכיר

The parameterized complexity of motion planning for snake-like robots

Siddharth Gupta, Guy Sa'Ar, Meirav Zehavi

We study the parameterized complexity of a variant of the classic video game Snake that models real-world problems of motion planning. Given a snake-like robot with an initial position and a final position in an environment (modeled by a graph), our objective is to determine whether the robot can reach the final position from the initial position without intersecting itself. Naturally, this problem models a wide-variety of scenarios, ranging from the transportation of linked wagons towed by a locomotor at an airport or a supermarket to the movement of a group of agents that travel in an "ant-like"fashion and the construction of trains in amusement parks. Unfortunately, already on grid graphs, this problem is PSPACE-complete. Nevertheless, we prove that even on general graphs, the problem is solvable in FPT time with respect to the size of the snake. In particular, this shows that the problem is fixed-parameter tractable (FPT). Towards this, we show how to employ color-coding to sparsify the configuration graph of the problem to reduce its size significantly. We believe that our approach will find other applications in motion planning. Additionally, we show that the problem is unlikely to admit 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 planning problems (where the intermediate configurations of the motion are of importance) has so far been largely overlooked. Thus, our work is pioneering in this regard.

שפת פרסום אנגלית
דפים 191-229
כתב עת Journal of Artificial Intelligence Research
כרך 69
סטטוס פרסום פורסם - 01.01.2020

ASJC Scopus subject areas

Artificial Intelligence
גישה למסמך
10.1613/jair.1.11864
קבצים וקישורים אחרים
Link to publication in Scopus