This thesis discusses applying AI planning tools for generating plans for manual
operations. Expertise of motion economy domain is used to select good plans among
feasible ones. Motion economy is a field of industrial engineering, which deals with
observing, reporting and improving manual operations. Motion economy knowledge
is organized in principles regarding the sequences and characteristics of motions,
arrangement of workspace, design of tools etc. A representation scheme is developed
for products, workspace and hand motions of manual operations. Operation plans are
generated using a forward chaining planner (TLPLAN). Planner and representation
of domain have extensions compared to a standard forward chaining planner, for
supporting concurrency, actions with resources and actions with durations. We
formulated principles of motion economy as search control temporal formulas. In
addition to motion economy rules, we developed rules for simulating common sense
of humans and goal-related rules for preventing absurd sequences of actions in the
plans. Search control rules constrain the problem and reduce search complexity.
Plans are evaluated during search. Paths, which are not in conformity with the
principles of motion economy, are pruned with motion economy rules. Sample
problems are represented and solved. Diversity of types of these problems shows the
generality of representation scheme. In experimental runs, effects of motion
economy principles on the generation of plans are observed and analyzed.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12606524/index.pdf |
| Date | 01 September 2005 |
| Creators | Canan, Ozgen |
| Contributors | Birturk, Aysenur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | M.S. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
Page generated in 0.0016 seconds