An increasing number of open-source, affordable, and compact robotic platforms built from commercial off-the-shelf components are being developed to approach the capabilities of complex space rovers. This trend is motivated by the fact that traditional space rovers take many years to develop, are extremely expensive, and are typically mission-specific, making them inaccessible as learning platforms for students and researchers. This thesis presents an open-source, modular state machine framework that integrates a utility-frontier-based exploration strategy with real time 3D object localization for low-cost autonomous rovers, and validates the approach on DLR’s Lunar Rover Mini (LRM).

Exploration is decomposed into reusable low-level state machines within a hierarchical architecture that handles frontier detection, clustering, and filtering, as well as position and orientation monitoring, implemented in RAFCON, DLR's open-source software tool to manage autonomous tasks. A utility function balances information gain, computed by performing 3D ray casting, and travel cost, determined by the estimated travel time to a frontier centroid, to decide the next frontier centroid.

Moreover, a frontier coverage algorithm is employed to determine the most efficient set of orientations that maximize information gain, based on the normalized cumulative entropy within the camera’s iFOV across the full azimuth range around each frontier centroid.

A parallel perception pipeline runs, in real time, a quantized, custom-trained YOLOv7 model on the LRM’s Intel NUC to detect objects of interest, and compute their 3D coordinates in the global map frame using stereo depth data and the camera’s intrinsic and extrinsic parameters.

The design was tested in DLR's Planetary Exploration Laboratory across a set of benchmarks and mission scenarios. Results show the proposed Utility With Edges strategy performs better than classical Closest-frontier and Entropy-only methods, with the Utility With Edges strategy improving exploration efficiency by 27% over the Closest-frontier baseline, while achieving accurate real time CPU-only object detection and localization.

Key limitations of this implementation include the computational cost of ray casting, the use of a full OctoMap that stores the full range of occupancy probabilities instead of a binary map, and drift in the visual odometry estimates.

Future work recommendation entail studying how more complex utility functions affect selecting the next frontier centroid, building a fully autonomous mission pipeline with autonomous object grasping, and testing the implementation of the open-source ready-to-use exploration strategy on other robotic platforms with user-defined parameters.