Evan Derse
Papers
1
Total Citations
13
H-Index
1
About
Evan Derse is a researcher at the intersection of robotics, computer vision, and renewable energy systems. His primary contributions lie in developing intelligent perception and energy-aware navigation strategies for autonomous mobile robots operating in unstructured outdoor environments. Derse’s most cited work, “Vision-Based Terrain Classification and Solar Irradiance Mapping for Solar-Powered Robotics” (2018, 13 citations), introduces a novel sequential pipeline that uses an artificial neural network to classify terrain from visual features in real time. This classification is then integrated with solar irradiance mapping, enabling a robot to predict energy availability and plan efficient, power-sustainable routes. By fusing terrain understanding with energy forecasting, Derse’s research directly addresses a critical bottleneck for long-duration, solar-powered field robotics—balancing locomotion cost against energy harvesting potential. His work has practical implications for environmental monitoring, precision agriculture, and planetary exploration, where robots must operate autonomously without human intervention. Derse’s approach stands out for its computational efficiency and real-time capability, making it deployable on resource-constrained platforms. Through this foundational study, he has helped pave the way for more resilient, self-sufficient robotic systems that can intelligently manage their own energy budgets in the wild.
Research Focus
Key Achievements
Top Papers
- 1