碩士 / 國立臺灣大學 / 電機工程學研究所 / 105 / Autonomous vehicle is a key technology that has been widely researched in recent decade and has many applications such as indoor navigation, rescue and transportation. The works of autonomous vehicle usually focus on navigation in an indoor environment or target tracking. There are two kinds of targets: (1) targets that positions have been detected by range sensors or locating systems and (2) targets that positions are unknown. Comparing to the former targets that have been researched wildly, the latter meet real scenario. In order to reach the unknown indoor navigation that GPS is denied and wireless beacon system has not been built, and avoid the same situation of exhaustive position tracking provided by Simultaneous Localization and Mapping (SLAM) technique, assume the target in the scenario is capable of broadcasting wireless signal. Mobile vehicles can track the target based on signal quality of the electromagnetic wave received, and then reach the applications such as indoor transportation, rescue, and mobile vehicle summation. This kind of problem is called source seeking.
This thesis proposes a signal source tracking control algorithm based on subgradient. The main purpose is to steer the mobile vehicle to the position of the target according to strength of signal received from targets. In order to avoid position oscillation of vehicle caused by sinusoidal probe signal used in traditional Extremum Seeking (ES), an optimization method called subgradient method used in computer science is used and modified for mobile vehicle path planning. In signal field, a common problem is signal delay which causes failure of computing gradient of signal strength. Mobile vehicle is designed to switch between moving state and pausing state to accomplish both tracking task and subgradient computation.
The major challenge of the problem is the unknown signal field. Mobile robot can only estimate subgradient in the corresponding moving direction, while subgradient in other direction at same position remains unknown. This make the mobile robot lose its judgment of choosing the direction with largest subgradient at the position, thus subgradient method failed. So, a searching algorithm making mobile vehicle switch alternately is raised by the proposed method. Effectiveness of the algorithm that combines multiple one-dimensional optimizations to multi-dimensional optimization is also analyzed as well. The reason why the other common optimization method, Newton’s method, failed is also analyzed in the end of this part.
In real scenario, there may be obstacles in the environment, so a design with obstacle avoidance feature of mobile vehicle is necessary. Thus, a punish term which is a function of the distance from obstacles measured by the onboard range sensors, is added into the computation of subgradient. When the mobile vehicle is approaching obstacles, punish term inhibit the translational velocity of the mobile vehicle to reach obstacle avoidance in the moving direction. If the design is applied to multiple one-dimensional searching algorithm, obstacle avoidance in multi-dimensional environment is completed. Although the signal field is assumed to be unknown, convergence of subgradient method applied on radio propagation model is analyzed to show applicability of the proposed method in real situation. Simulation and experimental results show that with certain constrains of the target, the vehicle mobile can track the signal source based on the proposed method.
| Identifer | oai:union.ndltd.org:TW/105NTU05442008 |
| Date | January 2016 |
| Creators | Yu-Kai Wang, 王昱凱 |
| Contributors | 連豊力 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | en_US |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 188 |
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