Task-Oriented Exploration: A Multi-Criteria Decision Making Approach for Robotic Exploration

In robotic planetary exploration missions, robots are deployed to autonomously explore and map the large and unstructured environments of planetary surfaces. While a robot should be able to execute a mission task mainly autonomously, for space exploration missions, it is important to have the opportunity to observe and adapt the robotic exploration task. Operators and scientists require to supervise the robot at the available communication time slots and understand the decisions made by the robot. For this we propose a generalized concept for robotic exploration based on Multi-Criteria Decision Making (MCDM) to model, implement and conduct exploration tasks. Our general formulation supports scientists by designing the autonomous exploration behavior of a robot to reach specific missions goals. In robotic exploration tasks, robots repeatedly decide where to move next. We define locations at the boundary to unknown areas - exploration goals - and locations in already visited areas - revisiting goals - to be the solution space of this decision problem. To model a certain exploration behavior, the goal locations are evaluated by a set of criteria and conditions. The criteria and condition values for each goal location are compared, applying a MCDM method to find the next goal location, which best matches the defined mission goal. Thereby, we introduce two novel multi-attribute utility functions and transfer the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II) to solve decision making in robotic exploration. To cope with the limited computational resources of space rovers, we extend the PROMETHEE II algorithm to decrease the required computational resources. Applying our generalized concept, we examine four exploration use cases, deduced from the Exploration Roadmap of the International Space Exploration Coordination Group (ISECG). In the first use case, the robot has to autonomously survey a region of interest. To tackle the trade-off between exploration efficiency and map quality, we implement an integrated exploration, which applies active loop closing to optimize an underlying SLAM graph. In our second use case, we implement a directed exploration to increase the scientific output while exploring a region of interest. It incorporates knowledge about the probability of detecting a feature of interest, i.e., a specific type of rock requested by the scientists. As our third use case, we implement an exploration behavior in the fashion of drive-by science, whereby the robot is directed to a predefined point of interest, while simultaneously gathering new information about the environment on its way. For our fourth use case, we apply the same concept to model a multi-robot exploration task, which coordinates a heterogeneous team of two robots. We demonstrate all four use cases on real or simulated space rover prototype hardware. In a total of more than sixty experiments, we evaluate our methods and analyze the implemented exploration behavior