CEE 4810

Robot Perception

Course Description

Course information provided by the 2026-2027 Catalog.

An introductory course on robot perception techniques for modeling, fusing, and interpreting heterogeneous and dynamic sensor measurements in the context of robot motion and uncertain environments. The course covers sensor modeling, artificial vision, acoustic sensing, and probabilistic filtering methods. Emphasis is placed on intelligent sensor fusion, object detection and classification, tracking, localization and mapping, exploration, and information-driven motion planning. Algorithms inspired by neural networks, Bayesian networks, graphical models, and information theory are examined. Students investigate perception-driven decision making through benchmark problems such as coverage, target search, tracking, and pursuit-evasion. Applications are drawn from environmental monitoring, surveillance, sensing-and-pursuit games, and human-robot interaction.

Prerequisites ENGRD 2112; MATH 2940; MATH 4710 (or ENGRD 2700 or ENGRD 2720); or graduate standing in a technical field.

Last 4 Terms Offered 2024FA, 2023FA, 2021FA

Learning Outcomes

• Design and implement algorithms for object detection, classification, tracking, localization, and mapping using heterogeneous sensor data.
• Develop and analyze probabilistic sensor fusion methods for multi-modal perception systems.
• Formulate and implement perception-driven planning and decision-making algorithms based on information-theoretic and graphical modeling frameworks.

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