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In military scenarios, agents (i.e., troops of soldiers, convoys, and unmanned vehicles) may often have to traverse environments with only a limited intelligence about the locations of adversaries. We study a particular instance of this problem that we refer to as path clearance problem.This article presents a survey of our work on scalable and suitable for real-time use approaches to solving the path clearance problem. In particular, in the first part of the article, we show that the path clearance problem exhibits clear preferences on uncertainty. It turns out that these clear preferences can be used to develop an efficient algorithm called probabilistic planning with clear preferences (PPCP). The algorithm is anytime usable, converges to an optimal solution under certain conditions, and scales well to large-scale problems. We briefly describe the PPCP algorithm and show how it can be used to solve the path clearance problem when no scouts are present. In the second part of the article, we show several strategies for how to use the PPCP algorithm in case multiple scouting unmanned aerial vehicles (UAVs) are available. The experimental analysis shows that planning with PPCP results in a substantially smaller execution cost than when ignoring uncertainty, and employing scouts can decrease this execution cost even further.
aerospace robotics, military vehicles, mobile robots, path planning, probability, remotely operated vehicles, military scenarios, path clearance problem, probabilistic planning with clear preferences, scouting unmanned aerial vehicles
Maxim Likhachev and Anthony Stentz, "Path Clearance", . June 2009.
Date Posted: 01 October 2009