We present a Multi-Criteria Decision Making (MCDM) framework specifically designed for planetary exploration. Our work is based on PROMETHEE II, which allows operators to add task-specific criteria and conditions. We extended this algorithm to improve its resource usage by reducing the number of candidate exploration goals that have to be evaluated and compared. This is crucial when given a large number of goals, as is typical for outdoor environments. In addition, we identified five different criteria for planetary exploration, including a novel criterion that we call Direction of Interest (DOI), and use a categorization of these for further resource optimizations. We thereby ensure that the CPU usage of our decision making method can meet the limited budget of a space rover. We present simulated and real-world experiments with the Lightweight Rover Unit (LRU) and show a reduction of the processing time for decision making of approx. 70%.

A robotic system for search and rescue missions needs to efficiently explore and map new areas. In this paper, we present an integrated exploration strategy with active loop closing, which balances between the exploration speed and map quality. Specifically, it finds a trade-off between moving towards unknown space to gather new information and revisiting previous locations to improve localization accuracy and map quality through loop closures. Our integrated exploration is built upon a submap-based 6D SLAM system. Loop closure constraints originate from pairwise submap matches, which allow the optimization of an underlying SLAM graph. During exploration, we employ the expected information gain as well as the robot's localization uncertainty estimates to weigh exploration and revisiting actions online. We introduce the match effect as the expected impact of a loop closure on global optimization and consider this novel criterion together with the match likelihood and cost when evaluating the utility of revisiting previous locations. To demonstrate our approach, we present simulated and real-world experiments, comparing two variants of our novel method to a frontier-based exploration.