The Mars Surveyor Challenge was introduced by MathWorks as a way to challenge the community of algorithm developers by asking them to come up with a solution to explore a set of given maps using five vehicles, called rovers. The challenge at hand is to find an optimal strategy using MATLAB to explore as much of a map as possible in a limited number of instructions, or 'moves'. This strategy can be used in designing search algorithms for UAVs or other surveillance vehicles in order to perform searches more efficiently. Multiple types of algorithms were studied in order to find their effect on the solution through the Research Experience for Undergraduate Students at the University of Cincinnati. The goal of this project was to develop algorithms that scale well for larger maps and more rovers than those specified for use in the original competition. In particular, this paper explores the use of a rule based algorithm which combines the concepts of stigmergy and receding horizon algorithms to solve the problem. Simulation results show that the algorithm discussed in this document is highly competitive compared to the competition winners and scales to larger maps and number of vehicles maintaining the same level of performance as observed with the original competition conditions.
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机译：MathWorks引入了“火星测量者挑战赛”，以此来挑战算法开发者社区，该方法是要求算法开发者提出一种解决方案，以使用五辆称为“漫游者”的车辆探索一组给定的地图。当前面临的挑战是使用MATLAB寻找一种最优策略，以有限的指令或“动作”来探索尽可能多的地图。该策略可用于设计无人机或其他监视车辆的搜索算法，以便更有效地执行搜索。为了研究辛辛那提大学（University of Cincinnati）本科生的研究经验，对多种算法进行了研究，以发现它们对解决方案的影响。该项目的目的是开发一种算法，该算法可比原始比赛中指定的算法更好地适用于较大的地图和更多的漫游车。尤其是，本文探讨了将基于规则的算法与混合算法和后退视野算法相结合来解决该问题的方法。仿真结果表明，与竞赛获胜者相比，本文中讨论的算法具有很高的竞争力，并且可以缩放到更大的地图和数量的车辆，从而保持与原始比赛条件相同的性能水平。
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