2026-09-21T07:22:06Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/21087 2023-07-10 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Artificial Intelligence Computer Science Omohundro, Stephen M. Mel, Bartlett W. 2011-05-07T12:57:59Z 2011-05-07T12:57:59Z 10000-01-01 1989 "Many aspects of intelligent animal behavior require an understanding of the complex spatial relationships between the body and its parts and the coordinate systems of the external world. This thesis deals specifically with the problem of guiding a multi-link arm to a visual target in the presence of obstacles. A simple vision-based kinematic controller and motion planner based on a connectionist network architecture has been developed, called MURPHY. The physical setup consists of a video camera and a Rhino XR-3 robot arm with three joints that move in the image plane of the camera. We assume no a priori model of arm kinematics or of the imaging characteristics of the camera/visual system, and no sophisticated built-in algorithms for obstacle avoidance. Instead, MURPHY builds a model of his arm through a combination of physical and ""mental"" practice, and then uses simple heuristic search with mental images of his arm to solve visually-guided reaching problems in the presence of obstacles whose traditional algorithmic solutions are extremely complex. MURPHY differs from previous approaches to robot motion-planning primarily in his use of an explicit full-visual-field representation of the workspace. Several other aspects of MURPHY's design are unusual, including the sigma-pi synaptic learning rule, the teacherless training paradigm, and the integration of sequential control within an otherwise connectionist architecture. In concluding sections we outline a series of strong correspondences between the representations and algorithms used by MURPHY, and the psychology, physiology, and neural bases for the programming and control of directed, voluntary arm movements in humans and animals." Made available in DSpace on 2011-05-07T12:57:59Z (GMT). No. of bitstreams: 2 license.txt: 4922 bytes, checksum: 910b249b4beec47e7ab768910c8f966f (MD5) 8924899.pdf: 6746397 bytes, checksum: 8ab3717e4516ed68ed50e98a1b56ef8b (MD5) Previous issue date: 1989 Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Howard Ding (hding2@illinois.edu) on 2011-05-07T14:48:23Z Item is restricted indefinitely. Restriction data tranferred 2014-07-01T11:21:54-05:00 Original Data Group with Access UIUC Users [automated] Release Date: none Reason: ETDs are only available to UIUC Users without author permission ETDs are only available to UIUC Users without author permission U of I Only AAI8924899 (UMI)AAI8924899 http://hdl.handle.net/2142/21087 eng Copyright 1989 Mel, Bartlett W. Biology, Neuroscience MURPHY: A neurally-inspired connectionist approach to learning and performance in vision-based robot motion planning text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.