Frontier Based Multiple Goal Search in Unknown Environments

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We present a frontier based algorithm for searching multiple goals in a fully unknown environment, with only information about the regions where the goals are most likely to be located. Our algorithm chooses an “active goal” from the “active goal list” generated by running a Traveling Salesman Problem (TSP) routine with the given centroid locations of the goal regions. We use the concept of “goal switching” which helps not only in reaching more number of goals in given time, but also prevents unnecessary search around the goals that are not accessible (surrounded by walls). The simulation study shows that our algorithm outperforms Multi-Heuristic LRTA* (MHLRTA*) which is a significant representative of multiple goal search approaches in an unknown environment, especially in environments with wall like obstacles.