Planning and exploring under uncertainty
<p>Scalable autonomy requires a robot to be able to recognize and contend with the uncertainty in its knowledge of the world stemming from its noisy sensors and actu- ators. The regions it chooses to explore, and the paths it takes to get there, must take this uncertainty into account. In this...
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| Format: | Thesis |
| Language: | English |
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2010
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| _version_ | 1797091411536904192 |
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| author | Murphy, EM |
| author2 | Newman, PM |
| author_facet | Newman, PM Murphy, EM |
| author_sort | Murphy, EM |
| collection | OXFORD |
| description | <p>Scalable autonomy requires a robot to be able to recognize and contend with the uncertainty in its knowledge of the world stemming from its noisy sensors and actu- ators. The regions it chooses to explore, and the paths it takes to get there, must take this uncertainty into account. In this thesis we outline probabilistic approaches to represent that world; to construct plans over it; and to determine which part of it to explore next.</p><p>We present a new technique to create probabilistic cost maps from overhead im- agery, taking into account the uncertainty in terrain classification and allowing for spatial variation in terrain cost. A probabilistic cost function combines the output of a multi-class classifier and a spatial probabilistic regressor to produce a probability density function over terrain for each grid cell in the map. The resultant cost map facilitates the discovery of not only the shortest path between points on the map, but also a distribution of likely paths between the points.</p><p>These cost maps are used in a path planning technique which allows the user to trade-off the risk of returning a suboptimal path for substantial increases in search speed. We precompute a probability distribution which precisely approximates the true distance between any grid cell in the map and goal cell. This distribution under- pins a number of A* search heuristics we present, which can characterize and bound the risk we are prepared to take in gaining search efficiency while sacrificing optimal path length. Empirically, we report efficiency increases in excess of 70% over standard heuristic search methods.</p><p>Finally, we present a global approach to the problem of robotic exploration, uti- lizing a hybrid of a topological data structure and an underlying metric mapping process. A ‘Gap Navigation Tree’ is used to motivate global target selection and occluded regions of the environment (‘gaps’) are tracked probabilistically using the metric map. In pursuing these gaps we are provided with goals to feed to the path planning process en route to a complete exploration of the environment.</p><p>The combination of these three techniques represents a framework to facilitate robust exploration in a-priori unknown environments.</p> |
| first_indexed | 2024-03-07T03:32:40Z |
| format | Thesis |
| id | oxford-uuid:bb3d85f6-117b-4f5e-92ab-b6acc87aef79 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:32:40Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | oxford-uuid:bb3d85f6-117b-4f5e-92ab-b6acc87aef792022-03-27T05:15:34ZPlanning and exploring under uncertaintyThesishttp://purl.org/coar/resource_type/c_db06uuid:bb3d85f6-117b-4f5e-92ab-b6acc87aef79RoboticsInformation engineeringEnglishOxford University Research Archive - Valet2010Murphy, EMNewman, PM<p>Scalable autonomy requires a robot to be able to recognize and contend with the uncertainty in its knowledge of the world stemming from its noisy sensors and actu- ators. The regions it chooses to explore, and the paths it takes to get there, must take this uncertainty into account. In this thesis we outline probabilistic approaches to represent that world; to construct plans over it; and to determine which part of it to explore next.</p><p>We present a new technique to create probabilistic cost maps from overhead im- agery, taking into account the uncertainty in terrain classification and allowing for spatial variation in terrain cost. A probabilistic cost function combines the output of a multi-class classifier and a spatial probabilistic regressor to produce a probability density function over terrain for each grid cell in the map. The resultant cost map facilitates the discovery of not only the shortest path between points on the map, but also a distribution of likely paths between the points.</p><p>These cost maps are used in a path planning technique which allows the user to trade-off the risk of returning a suboptimal path for substantial increases in search speed. We precompute a probability distribution which precisely approximates the true distance between any grid cell in the map and goal cell. This distribution under- pins a number of A* search heuristics we present, which can characterize and bound the risk we are prepared to take in gaining search efficiency while sacrificing optimal path length. Empirically, we report efficiency increases in excess of 70% over standard heuristic search methods.</p><p>Finally, we present a global approach to the problem of robotic exploration, uti- lizing a hybrid of a topological data structure and an underlying metric mapping process. A ‘Gap Navigation Tree’ is used to motivate global target selection and occluded regions of the environment (‘gaps’) are tracked probabilistically using the metric map. In pursuing these gaps we are provided with goals to feed to the path planning process en route to a complete exploration of the environment.</p><p>The combination of these three techniques represents a framework to facilitate robust exploration in a-priori unknown environments.</p> |
| spellingShingle | Robotics Information engineering Murphy, EM Planning and exploring under uncertainty |
| title | Planning and exploring under uncertainty |
| title_full | Planning and exploring under uncertainty |
| title_fullStr | Planning and exploring under uncertainty |
| title_full_unstemmed | Planning and exploring under uncertainty |
| title_short | Planning and exploring under uncertainty |
| title_sort | planning and exploring under uncertainty |
| topic | Robotics Information engineering |
| work_keys_str_mv | AT murphyem planningandexploringunderuncertainty |