Spatial Learning for Robot Locialization
Date
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Research Projects
Organizational Units
Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.
History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.
Dates of Existence
1969-present
Related Units
- College of Liberal Arts and Sciences (parent college)
Journal Issue
Is Version Of
Versions
Series
Department
Abstract
Although evolutionary algorithms have been employed to automatically synthesize control and behavior programs for robots and even design the physical structures of the robots, it is impossible for evolution to anticipate the detailed structure of specific environments that the robot might have to deal with. Robots must thus possess mechanisms to learn and adapt to the environments they encounter. One such mechanism that is of importance to mobile robots is that of spatial learning, i.e., the ability to learn the spatial locations of objects and places in the environment, which would allow them to successfully explore and navigate in a-priori unknown environments. This paper proposes a computational model for the acquisition and use of spatial information that is inspired by the role of the hippocampal formation in animal spatial learning and navigation.