Toward Robust Autonomy in Field Robotics
Field robotics seeks to tackle some of society’s most dull, dirty, and dangerous jobs, from offshore inspection and environmental monitoring to industrial operations and exploration in extreme environments. Autonomous robots are increasingly deployed in complex and unstructured environments. While remarkable progress has been achieved in robotics and AI, achieving reliable autonomy outside the laboratory remains a major challenge. Real-world environments are inherently messy and highly challenging: they are dynamic, often characterized by harsh weather, poor visibility, complex terrain, degraded sensing conditions, limited communication bandwidth, constrained onboard resources, and unforeseen events. Together, these factors introduce substantial uncertainty that can quickly degrade performance and compromise autonomous operation in the field.
This lecture on robust field autonomy explores the technologies and methods that enable robots to operate reliably and perform long-duration missions in such demanding environments. It covers core capabilities including perception, localization, mapping, planning, navigation, and control, where particular emphasis will be placed on robustness, discussing the ability of robotic systems to maintain performance despite uncertainty, disturbances, sensor failures, and environmental variability.
Through examples from diverse domains, the talk highlights how modern robotic systems combine advanced sensing, machine learning, model-based reasoning, and adaptive decision-making to enable inspection, maintenance, and intervention tasks with increasing levels of autonomy. The lecture concludes by discussing lessons learned from real-world deployments and remaining challenges toward long-term trustworthy autonomy in field robotics.