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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 連豊力(Feng-Li Lian) | |
| dc.contributor.author | You-Ling Jian | en |
| dc.contributor.author | 簡佑玲 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:36:08Z | - |
| dc.date.available | 2007-07-30 | |
| dc.date.copyright | 2007-07-30 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-12 | |
| dc.identifier.citation | [1: Santi 2005]
P. Santi, Topology Control in Wireless Ad Hoc and Sensor Network, John Wiley & Sons, Ltd, 2005. [2: Rodoplu and Meng 1999] V. Rodoplu and T. H. Meng, “Minimum Energy Mobile Wireless Networks,” IEEE Journal on Selected Areas in Communications, Vol. 17, No. 8, pp. 1333-1344, Aug. 1999. [3: Li et al. 2003] N. Li, J. C. Hou and L. Sha, “Design and Analysis of an MST-Based Topology Control Algorithm,” in Proceedings of 22nd Annual Joint Conference of the IEEE Computer and Communication Societies (INFOCOM 2003), Vol. 3, pp. 1702-1712, San Francisco, USA, Mar.-Apr. 2003. [4: Li et al. 2005] L. Li, J. Y. Halpern, P. Bahl, Y. Wang and R. Wattenhofer, “A Cone-Based Distributed Topology Control Algorithm for Wireless Multi-Hop Networks,” IEEE/ACM Transactions on Networking, Vol. 13, No. 1, Feb. 2005. [5: Borbash and Jennings 2002] S. A. Borbash and E. H. Jennings, “Distributed Topology Control Algorithm for Multihop Wireless Networks,” in Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN 2002), Vol. 1, pp. 355-360, Honolulu, H.I., May 2002. [6: Blough et al. 2003] D. M. Blough, M. Leoncini, G. Resta and P. Santi, “The K-Neigh Protocol for Symmetric Topology Control in Ad Hoc Networks,” in Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2003), pp. 141-152, Annapolis, MD, USA, Jun. 2003. [7: Wattenhofer and Zollinger 2004] R. Wattenhofer and A. Zollinger, “XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks,” in Proceedings of 2004 18th International Parallel and Distributed Processing Symposium (IPDPS 2004), pp. 216-223, Santa Fe, New Mexico, Apr. 2004. [8: Cortes et al. 2004] J. Cortes, S. Martinez, T. Karatas and F. Bullo, “Coverage Control for Mobile Sensing Networks,” IEEE Transactions on Robotics and Automation, Vol. 20, No. 2, pp. 243-255, Apr. 2004. [9: Tan 2005] J. Tan, “A Scalable Graph Model and Coordination Algorithms for Multi-agent Systems,” in Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2005), pp. 1529-1534, Monterey, California, USA, Jul. 2005. [10: Poduli and Sukhatme 2004] S. Poduri and G. S. Sukhatme, “Constrained Coverage for Mobile Sensor Networks,” in Proceedings of IEEE 2004 International Conference on Robotics and Automation (ICRA 2004), Vol. 1, pp. 165-171, New Orieans, LA, USA, Apr. 2004. [11: Gueron et al. 1996] S. Gueron, S. A. Levin, and D. I. Rubenstein, “The Dynamics of Herds: From Individuals to Aggregations,” Journal of Theoretical Biology, Vol. 182, No. 1, pp. 85-98, Sep. 1996. [12: Gazi and Passino 2003] V. Gazi and K. M. Passino, “Stability Analysis of Swarms,” IEEE Transactions on Automatic Control, Vol. 48, No. 4, pp. 692-697, Apr. 2003. [13: Gazi and Passino 2004] V. Gazi and K. M. Passino, “A class of attractions/repulsion functions for stable swarm aggregations,” International Journal of Control, Vol. 77, No. 18, pp. 1567-1579, Dec. 2004. [14: Gazi and Passino 2004] V. Gazi and K. M. Passino, “Stability Analysis of Social Foraging Swarms,” IEEE Tracnsactions on Systems, Man, and Cybernetics─Part B: Cybernetics, Vol. 34, No. 1, pp. 539-557, Feb. 2004. [15: Parrish et al. 2002] J. K. Parrish, S. V. Viscido and D. Grunbaum, “Self-Organized Fish Schools: An Extraction of Emergent Properties,” The Biological Bulletin, Vol. 202, No. 3, pp. 296-305, Jun. 2002. [16: Passino 2002] K. M. Passino, “Biomimicry of Bacterial Foraging for Distributed Optimization and Control,” IEEE Control Systems Magazine, Vol. 22, No. 3, pp. 52-67, Jun. 2002. [17: Burgard et al. 2005] W. Burgard, M. Moors, C. Stachniss and F. E. Schneider, “Coordinated Multi-Robot Exploration,” IEEE Transactions on Robotics, Vol. 21, No. 3, pp. 376-386, Jun. 2005. [18: Ge and Fua 2005] S. S. Ge and C. Fua, “Queues and Artificial Potential Trenches for Multiagent Formations,” IEEE Transactions on Robotics, Vol. 21, No. 4, pp. 646-656, Aug. 2005. [19: Robotic Fan.Com] Robotic Fan. Com, http://www.roboticfan.com/college/knowledge/List_3.asp [20: フモиэみ写真素材集] フモиэみ写真素材集, http://www.yunphoto.net/jp/ord-49.html | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30099 | - |
| dc.description.abstract | 群組機器人系統擁有可共同合作之特性，在許多應用上帶來了極大的優點，因此近年來受到了重視並且被廣泛地研究探討。而在群組機器人系統中，為了達成「合作」此一特性，即使在移動狀態之下，機器人仍舊必須維持住一個良好的通訊網路。而為了達成此一目的，一定量的資訊通常是必須的，但這在實現上很有可能會受到硬體的限制而造成問題。在本論文中，我們提出了一個群組機器人擴散演算法，使得群組機器人可以在移動狀態之下仍舊維持良好的通訊網路狀態。而此演算法的特點為，只利用極簡單的一項資訊：通訊密度，也就是個別機器人與其相鄰機器人們的通訊連接數，便可達成系統的需求。該項資訊在實際應用中是極簡單即可獲得的一項資訊，不需要一些複雜的設備，例如全球定位系統、攝影機或是雷達等。由此我們達成了低資訊量需求的一大優點。除此之外，在搜尋目標物的任務中，對於一個在環境中釋放機器人去偵測目標物的基地台，我們也根據了此移動演算法設計了一個機器人派遣法則。利用此派遣法則，基地台可以彈性地釋放出更多的機器人向外支援，並讓整體搜尋涵蓋面積隨時間線性增加，達成資源運用與搜尋速度並重的優點。最後在本論文中，我們討論了此移動演算法和派遣法則的模擬結果。同時，包括涵蓋面積，網路分裂率，花費時間與機器人停止率等數個特定參數，關於其模擬結果的數據統計值也做了詳細的探討，並且得出了一些極有益的參數關係特徵。 | zh_TW |
| dc.description.abstract | In recent years, the research topic on multi-robot systems has received great attention since the cooperation characteristics of robots provides lots of advantages. To achieve the cooperation characteristics and information sharing, robots must maintain a good communication network while moving in order to share information. But in real implementation, information quantity could be a problem due to the hardware limitation. In this thesis, a multi-robot dispersion algorithm is proposed for robots to move under maintaining a good communication network. In particular, this algorithm utilizes only simple information, which is the communication density, or in other words, the number of communication links. This information could be easily obtained without complex equipments such as GPS, camera or radar. Hence the advantage of low information quantity is achieved. Moreover, for a base station that releases robots to search for targets, a dispatch rule is proposed based on the dispersion algorithm. With this dispatch rule the base station could flexibly resupply robots to enlarge the coverage area linearly with time. Extensive simulations of the dispersion algorithm and dispatch rule are well studied. Statistics on certain parameters, namely coverage area, partition rate, spending time and stop percentage, are discussed and good characteristics are found in them. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:36:08Z (GMT). No. of bitstreams: 1 ntu-96-R94921015-1.pdf: 1288971 bytes, checksum: 216e5a13b93bebacb934044cd42cfa79 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 摘要 I
ABSTRACT III CONTENTS V LIST OF FIGURES VII LIST OF TABLES IX CHAPTER 1 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Literature Survey of Related Researches 3 1.3 Contribution of the Thesis 9 1.4 Organization of the Thesis 10 CHAPTER 2 11 PROBLEM FORMULATION 11 2.1 Problem Description 11 2.2 Biological Aggregation 16 2.3 Bacteria Foraging Behavior 17 CHAPTER 3 21 DISPERSION ALGORITHM AND DISPATCH RULE 21 3.1 Parameter Definition of Dispersion Algorithm and Dispatch Rule 21 3.2 Dispersion Algorithm 23 3.2.1 Biological Inspired Behavior 23 3.2.2 Multi-Robot Dispersion Algorithm 25 3.3 Dispatch Rule of the Base Station 32 3.3.1 Area Estimation with Prior Information 33 3.3.2 Area Estimation with Feedback Information 34 CHAPTER 4 37 SIMULATION STUDY 37 4.1 Static Environment: Dispersion of One Robot 37 4.2 Multi-Robot System: Dispersion of Multiple Robots 38 4.2.1 Partition Rate 40 4.2.2 Coverage Area and Effective Area Radius 41 4.2.3 Spending Time 42 4.2.4 Stop Percentage 45 4.3 Target Search: Dispersion and Dispatch 46 CHAPTER 5 51 CONCLUSION AND FUTURE WORK 51 5.1 Conclusion 51 5.2 Future Work 52 APPENDIX 53 A.1 Static Environment 54 A.2 Dispersion 55 A.2.1 Dispersion of Different Robot Number 55 A.2.2 Dispersion of Different Ratio of 63 A.3 Estimation Accuracy of Dispatch Rule 68 REFERENCES 73 | |
| dc.language.iso | en | |
| dc.subject | 目標物搜尋 | zh_TW |
| dc.subject | 群組機器人系統 | zh_TW |
| dc.subject | 擴散移動 | zh_TW |
| dc.subject | 派遣法則 | zh_TW |
| dc.subject | 仿生 | zh_TW |
| dc.subject | 資訊量 | zh_TW |
| dc.subject | 感測網路 | zh_TW |
| dc.subject | dispatch rule | en |
| dc.subject | sensor network | en |
| dc.subject | information quantity | en |
| dc.subject | multi-robot system | en |
| dc.subject | target search | en |
| dc.subject | dispersion movement | en |
| dc.subject | biomimetic | en |
| dc.title | 利用通訊密度設計群組仿生機器人之擴散與派遣移動演算法 | zh_TW |
| dc.title | Dispersion and Dispatch Movement Design for a Team of Biomimetic Searching Robots Using Communication Density | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 傅立成(Li-Chen Fu),陳永耀(Yung-Yaw Chen) | |
| dc.subject.keyword | 群組機器人系統,目標物搜尋,擴散移動,派遣法則,仿生,資訊量,感測網路, | zh_TW |
| dc.subject.keyword | multi-robot system,target search,dispersion movement,dispatch rule,biomimetic,information quantity,sensor network, | en |
| dc.relation.page | 77 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-16 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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| ntu-96-1.pdf 未授權公開取用 | 1.26 MB | Adobe PDF |
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