Autonomous exploration by drones in unknown environments has traditionally focused on maximizing spatial coverage without semantic understanding. This thesis presents a framework that integrates vision-language models (VLMs) with adaptive path planning to enable anomaly-aware exploration and inspection. The system employs a three-phase approach: frontier-based exploration, continuous VLM-based anomaly detection, and inspection of detected anomalies. Comparative experiments demonstrated that YOLO+CLIP with negative embeddings achieved the highest F1 score of 0.7218 on the SegmentifyMeIfYouCan benchmark. Experiments showed that dedicated inspection yielded improvements over solely exploration observations. However, system-level evaluation across nine experimental runs revealed that the inspection phase took up most of the mission time (85.9% average), with varying anomaly detection consistency across anomaly instances. False positive analysis identified VLM error as the primary limitation (52% of false positives), followed by simulation artifacts (37%) and semantic ambiguity (11%). The framework successfully demonstrated the feasibility of coupling VLM-based anomaly detection with adaptive planning, though precision limitations and large inspection inefficiencies show opportunities for future work.

Solutions for reducing greenhouse gas emissions are paramount under the current environmental circumstances. With methane and carbon dioxide being the most critical emission gasses, SigmaSat set out to find a way to reduce these emissions and simultaneously fulfill its scientific mission. While executing the scientific mission of designing a small satellite mission to demonstrate the latest advances in artificial intelligence, SigmaSat managed to devise a design that allows players in the energy production industry (such as refineries) to drastically reduce their methane and CO2 emissions.