Mission adaptable autonomous vehicles
The authors discuss lessons learned on a neural autonomous simulator project that can be applied to autonomous underwater vehicles (AUVs). They developed a neural network (NN)-based unmanned air vehicle (UAV) navigation demonstration. The UAV simulation shows friendly flight corridors, enemy air-def...
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| creator | Schiller, I. Draper, J.S. |
| description | The authors discuss lessons learned on a neural autonomous simulator project that can be applied to autonomous underwater vehicles (AUVs). They developed a neural network (NN)-based unmanned air vehicle (UAV) navigation demonstration. The UAV simulation shows friendly flight corridors, enemy air-defense sites and the UAV mission targets. The UAV navigates in this hostile environment and reacts to unexpected threats. The study concentrated on the feasibility for noncomputer experts to prepare the UAVs for the specialized missions dictated by mission requirements and the battle situation, such as SAM sites and goal locations, corridors or way points. It was shown that NNs are successful in operating UAVs, and that the mission success rate is improved over fixed way point to way point flying. The simulation shows the potential for enhancing AUV survivability in hostile environments.< > |
| doi_str_mv | 10.1109/ICNN.1991.163340 |
| format | Conference Proceeding |
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They developed a neural network (NN)-based unmanned air vehicle (UAV) navigation demonstration. The UAV simulation shows friendly flight corridors, enemy air-defense sites and the UAV mission targets. The UAV navigates in this hostile environment and reacts to unexpected threats. The study concentrated on the feasibility for noncomputer experts to prepare the UAVs for the specialized missions dictated by mission requirements and the battle situation, such as SAM sites and goal locations, corridors or way points. It was shown that NNs are successful in operating UAVs, and that the mission success rate is improved over fixed way point to way point flying. The simulation shows the potential for enhancing AUV survivability in hostile environments.< ></description><subject>Expert systems</subject><subject>Land vehicles</subject><subject>Mobile robots</subject><subject>Navigation</subject><subject>Neural networks</subject><subject>Remotely operated vehicles</subject><subject>Robot sensing systems</subject><subject>Robustness</subject><subject>Underwater vehicles</subject><subject>Unmanned aerial vehicles</subject><isbn>0780302052</isbn><isbn>9780780302051</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1991</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj01rwkAURQekoLXupatsukz63rz5yrKEtgrWbnQtLzMjnRKNOLHQf1_B3s3ZHc4VYo5QIUL9vGzW6wrrGis0RApG4h6sAwIJWo7FLOdvuE5rp62aiKePlHPqjwUHPg3cdrHgy9Af-0N_ycVP_Eq-i_lB3O25y3H2z6nYvr1umkW5-nxfNi-rMqGVQxmgptDa6CJ7T4EVyOBbxexa5zFYbYPhaxPtPWstjXFgkIxybJzyMtBUPN68Kca4O53Tgc-_u9sR-gNt2D3V</recordid><startdate>1991</startdate><enddate>1991</enddate><creator>Schiller, I.</creator><creator>Draper, J.S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1991</creationdate><title>Mission adaptable autonomous vehicles</title><author>Schiller, I. ; Draper, J.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-d093db7e8eacc3da402dcb4aa8b8c1d757d6a3403fca5526680613648a684c2d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1991</creationdate><topic>Expert systems</topic><topic>Land vehicles</topic><topic>Mobile robots</topic><topic>Navigation</topic><topic>Neural networks</topic><topic>Remotely operated vehicles</topic><topic>Robot sensing systems</topic><topic>Robustness</topic><topic>Underwater vehicles</topic><topic>Unmanned aerial vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Schiller, I.</creatorcontrib><creatorcontrib>Draper, J.S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Schiller, I.</au><au>Draper, J.S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Mission adaptable autonomous vehicles</atitle><btitle>[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering</btitle><stitle>ICNN</stitle><date>1991</date><risdate>1991</risdate><spage>143</spage><epage>150</epage><pages>143-150</pages><isbn>0780302052</isbn><isbn>9780780302051</isbn><abstract>The authors discuss lessons learned on a neural autonomous simulator project that can be applied to autonomous underwater vehicles (AUVs). They developed a neural network (NN)-based unmanned air vehicle (UAV) navigation demonstration. The UAV simulation shows friendly flight corridors, enemy air-defense sites and the UAV mission targets. The UAV navigates in this hostile environment and reacts to unexpected threats. The study concentrated on the feasibility for noncomputer experts to prepare the UAVs for the specialized missions dictated by mission requirements and the battle situation, such as SAM sites and goal locations, corridors or way points. It was shown that NNs are successful in operating UAVs, and that the mission success rate is improved over fixed way point to way point flying. The simulation shows the potential for enhancing AUV survivability in hostile environments.< ></abstract><pub>IEEE</pub><doi>10.1109/ICNN.1991.163340</doi><tpages>8</tpages></addata></record> |
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| identifier | ISBN: 0780302052 |
| ispartof | [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering, 1991, p.143-150 |
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| language | eng |
| recordid | cdi_ieee_primary_163340 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Expert systems Land vehicles Mobile robots Navigation Neural networks Remotely operated vehicles Robot sensing systems Robustness Underwater vehicles Unmanned aerial vehicles |
| title | Mission adaptable autonomous vehicles |
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