碩士 / 國立臺灣大學 / 電機工程學研究所 / 106 / The ability of navigation is a necessity for service robots.For example, we may ask a service to deliver objects, or take something in another room for us.In hotels, guests may order some meal in their room.Service robots need to be capable of carrying the meal from the kitchen to the room.Or in hospitals, service robots need to help deliver medicine or chemical containers.We can imagine that service robots are around us in every public area in the future.In these scenarios, service robots need to understand environments and navigate to destination safely and robustly. Navigation methods nowadays are mainly based on metric maps.Metric maps are normally generated by SLAM (Simultaneous Localization and Mapping) methods.With metric maps provided, robots can plan a shortest to destination with ease.However, these planned paths may not be the most human preferable ones.Some paths may be too close to corner, or passing through some unwanted areas.In other words, metric maps only record information of space occupation.Information of available and human preferable paths are not included.This can be a disadvantage once we want to make a service robot be fastly applied in any indoor environment.Moreover, metric maps are lack of semantic meaning.Metric maps are normally composed of precise coordinates.Once we want a service robot to do navigation, we also need to give it a set of coordinate.This situation is similar to providing longitude and latitude when someone wants to go to a place.In this case, semantic meaning is ignored and not suitable for intrinsic understanding.Without semantic meaning, a service robot may struggling in reaching a precise coordinate.For example, if we want a service robot to go to ``living room", we need to provide a set of coordinate when using metric maps.The robot will try its best reaching the goal coordinate.Therefore, once there is an obstacle stop the robot from reaching the goal coordinate, the robot will judge that it hasn''t reached the goal even if it has already be in the ``living room".Although metric maps are rich in details and suitable for navigation, it is hard to label abstract concept on it.For example, it is difficult to define an appropriate region on metric maps to represent a ``living room".In the other hand, topological maps can store any data in their topological nodes, such as images or object labels.This can make it easier to integrate semantic meaning into maps.Nevertheless, topological maps are not precise enough.They cannot indicate precise spatial information compared metric maps.It is almost impossible to do navigation with only topological maps presented.To take advantage of both metric maps and topological maps, we propose a hybrid metirc-topomap.In this hybrid map, the topological map is responsible for semantic meaning recording, and the metric maps is used for collision avoidance.We store images in topological nodes, which is used for robot localization.The paths in mapping stage will also be recorded as topological edges.This help robots navigate in a more human preferable way in the future.We use a neural network to compare view from the robot and the images stored in the topological map, and then generate similarity values.We propose an image base particle filter to generate an ``semantic pose", which can give localization results more flexibility.With the help of topological maps and semantic pose, our proposed method can shorten navigation consumed time and make navigation with higher success rate.We test our algorithm in a 800 square meters indoor environment.We record the consumed time and success rate for the 5 paths in the environment.The experimental results show that the robot navigation success rate of our proposed method exceeds traditional navigation methods for about 16%.The result shows that our method can help service robots navigate more robustly.
| Identifer | oai:union.ndltd.org:TW/106NTU05442037 |
| Date | January 2018 |
| Creators | Wei Shih, 石崴 |
| Contributors | 羅仁權 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 75 |
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