| LDR | | 01902nmm uu200433 4500 |
| 001 | | 000000333533 |
| 005 | | 20240805173218 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438325012 |
| 035 | |
▼a (MiAaPQ)AAI10817396 |
| 035 | |
▼a (MiAaPQ)berkeley:17914 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Akametalu, Anayo K. |
| 245 | 12 |
▼a A Learning-based Approach to Safety for Uncertain Robotic Systems. |
| 260 | |
▼a [S.l.] :
▼b University of California, Berkeley.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 95 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Claire J. Tomlin. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a Robotic systems are becoming more pervasive, and have the potential to significantly improve human lives. However, for these benefits to be realized it is critical that the safe operation of these systems be guaranteed. Reachability analysis has |
| 520 | |
▼a This thesis uses Hamilton-Jacobi (HJ) reachability analysis to robustly guarantee safety for systems with uncertainty. In the presence of uncertainty there must be a balance between conservativeness as it pertains to safety and performance as it |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Robotics. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0771 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Electrical Engineering & Computer Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998357
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |