We study a new version of the Euclidean TSP called VectorTSP (VTSP for short) where a mobile entity is allowed to move according to a set of physical constraints inspired from the pen-and-pencil game Racetrack (also known as Vector Racer ). In contrast to other versions of TSP accounting for physical constraints, such as Dubins TSP, the spirit of this model is that (1) no speed limitations apply, and (2) inertia depends on the current velocity. As such, this model is closer to typical models considered in path planning problems, although applied here to the visit of n cities in a non-predetermined order. We motivate and introduce the VectorTSP problem, discussing fundamental differences with previous versions of TSP. In particular, an optimal visit order for ETSP may not be optimal for VTSP. We show that VectorTSP is NP-hard, and in the other direction, that VectorTSP reduces to GroupTSP in polynomial time (although with a significant blow-up in size). On the algorithmic side, we formulate the search for a solution as an interactive scheme between a high-level algorithm and a trajectory oracle, the former being responsible for computing the visit order and the latter for computing the cost (or the trajectory) for a given visit order. We present algorithms for both, and we demonstrate and quantify through experiments that this approach frequently finds a better solution than the optimal trajectory realizing an optimal ETSP tour, which legitimates the problem itself and (we hope) motivates further algorithmic developments.
翻译:我们研究的是新版本的Euclidean TSP, 称为VectorTSP(简称VTSP ), 该版本允许移动实体根据笔和笔游戏赛车道(也称为Vector Rightr ) 引发的一系列物理限制进行移动。 与其他版本的TSP相比, TSP对实际限制进行核算,例如Dubins TSP, 这个模型的精神是:(1) 不适用速度限制,和(2) 惰性取决于当前的速度。 因此, 这个模型更接近于路径规划问题中考虑的典型模型, 尽管这里适用于非预定秩序下的新城市的访问。 我们经常激励和介绍VctorTSP问题, 讨论与TSP以前版本的基本差异。 特别是, ETP的最佳访问顺序可能不是VTSP的最佳版本。 我们显示, VectortoctorTSP 是很硬的, 在另一个方向上, VecttorkTSP 在多时会降低到 GroupTSP (但还有更大的希望 ) 。 在算学方面, 我们设计了一种更好的解决方案的动力,, 和前轨路路路路段之间 找到一个更好的解决方案,, 一种我们找到一个更好的选择, 通过前的轨道和前电子轨道, 通过一个我们 和前电子路程 的路径, 通过一个更好的选择, 一种我们找到一个更好的选择。