| dc.creator |
Donald, Bruce Randall |
|
| dc.date |
2004-10-20T20:02:29Z |
|
| dc.date |
2004-10-20T20:02:29Z |
|
| dc.date |
1987-07-01 |
|
| dc.date.accessioned |
2013-10-09T02:47:13Z |
|
| dc.date.available |
2013-10-09T02:47:13Z |
|
| dc.date.issued |
2013-10-09 |
|
| dc.identifier |
AITR-982 |
|
| dc.identifier |
http://hdl.handle.net/1721.1/6851 |
|
| dc.identifier.uri |
http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
|
| dc.description |
Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, control errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. We present a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, compliant motions, and simple pushing motions. |
|
| dc.format |
310 p. |
|
| dc.format |
44428054 bytes |
|
| dc.format |
35921531 bytes |
|
| dc.format |
application/postscript |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.relation |
AITR-982 |
|
| dc.subject |
robotics |
|
| dc.subject |
motion planning |
|
| dc.subject |
uncertainty |
|
| dc.subject |
error detection andsrecovery |
|
| dc.subject |
computational geometry |
|
| dc.subject |
geometric reasoning |
|
| dc.subject |
planning withsuncertainty |
|
| dc.subject |
model error |
|
| dc.subject |
EDR |
|
| dc.subject |
failure mode analysis |
|
| dc.title |
Error Detection and Recovery for Robot Motion Planning with Uncertainty |
|