Optimal exploration of terrains with obstacles

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Abstract

A mobile robot represented by a point moving in the plane has to explore an unknown flat terrain with impassable obstacles. Both the terrain and the obstacles are modeled as arbitrary polygons. We consider two scenarios: the unlimited vision, when the robot situated at a point p of the terrain explores (sees) all points q of the terrain for which the segment pq belongs to the terrain, and the limited vision, when we require additionally that the distance between p and q be at most 1. All points of the terrain (except obstacles) have to be explored and the performance of an exploration algorithm, called its complexity, is measured by the length of the trajectory of the robot. For unlimited vision we show an exploration algorithm with complexity O(P + D√k), where P is the total perimeter of the terrain (including perimeters of obstacles), D is the diameter of the convex hull of the terrain, and k is the number of obstacles. We do not assume knowledge of these parameters. We also prove a matching lower bound showing that the above complexity is optimal, even if the terrain is known to the robot. For limited vision we show exploration algorithms with complexity O(P + A + √Ak), where A is the area of the terrain (excluding obstacles). Our algorithms work either for arbitrary terrains, if one of the parameters A or k is known, or for c-fat terrains, where c is any constant (unknown to the robot) and no additional knowledge is assumed. (A terrain T with obstacles is c-fat if R/r ≤ c, where R is the radius of the smallest disc containing and r is the radius of the largest disc contained in T.) We also prove a matching lower bound Ω(P + A + √Ak) on the complexity of exploration for limited vision, even if the terrain is known to the robot. © 2010 Springer-Verlag.

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APA

Czyzowicz, J., Ilcinkas, D., Labourel, A., & Pelc, A. (2010). Optimal exploration of terrains with obstacles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6139 LNCS, pp. 1–12). Springer Verlag. https://doi.org/10.1007/978-3-642-13731-0_1

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