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Path Planning for Efficient Deployment and Collection of a Marsupial Robot Team
Marsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구

DC Field Value Language
dc.contributor.advisor이범희-
dc.contributor.author이훈수-
dc.date.accessioned2017-10-27T16:43:19Z-
dc.date.available2017-10-27T16:43:19Z-
dc.date.issued2017-08-
dc.identifier.other000000144955-
dc.identifier.urihttp://hdl.handle.net/10371/136819-
dc.description학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 이범희.-
dc.description.abstractThis dissertation presents time-efficient approaches to path planning for initial deployment and collection of a heterogeneous marsupial robot team consists of a large-scale carrier robot and multiple mobile robots. Although much research has been conducted about task allocation and path planning of multi-robot systems, the path planning problems for deployment and collection of a marsupial robot team have not been fully addressed. The objectives of the problems are to minimize the duration that mobile robots require to reach their assigned task locations and return from those locations. Taking the small mobile robot's energy constraint into account, a large-scale carrier robot, which is faster than a mobile robot, is considered for efficient deployment and collection. The carrier robot oversees transporting, deploying, and retrieving of mobile robots, whereas the mobile robots are responsible for carrying out given tasks such as reconnaissance and search and rescue. The path planning methods are introduced in both an open space without obstacles and a roadmap graph which avoids obstacles. For the both cases, determining optimal path requires enormous search space whose computational complexity is equivalent to solving a combinatorial optimization problem. To reduce the amount of computation, both task locations and mobile robots to be collected are divided into a number of clusters according to their geographical adjacency and their energies. Next, the cluster are sorted based on the location of the carrier robot. Then, an efficient path for the carrier robot can be generated by traveling to each deploying and loading location relevant to each cluster. The feasibility of the proposed algorithms is demonstrated through several simulations in various environments including three-dimensional space and dynamic task environment. Finally, the performance of the proposed algorithms is also demonstrated by comparing with other simple methods.-
dc.description.tableofcontentsChapter 1 Introduction 1
1.1 Background and motivation 1
1.1.1 Multi-robot system 1
1.1.2 Marsupial robot team 3
1.2 Contributions of the thesis 9
Chapter 2 Related Work 13
2.1 Multi-robot path planning 14
2.2 Multi-robot exploration 14
2.3 Multi-robot task allocation 15
2.4 Simultaneous localization and mapping 15
2.5 Motion planning of collective swarm 16
2.6 Marsupial robot team 18
2.6.1 Multi-robot deployment 18
2.6.2 Marsupial robot 19
2.7 Robot collection 23
2.8 Roadmap generation 24
2.8.1 Geometric algorithms 24
2.8.2 Sampling-based algorithms 25
2.9 Novelty of the thesis 26
Chapter 3 Preliminaries 27
3.1 Notation 27
3.2 Assumptions 29
3.3 Clustering algorithm 30
3.4 Minimum bounded circle and sphere of a cluster 32
Chapter 4 Deployment of a Marsupial Robot Team 35
4.1 Problem definition 35
4.2 Complexity analysis 37
4.3 Optimal deployment path planning for two tasks 38
4.3.1 Deployment for two tasks in 2D space 39
4.3.2 Deployment for two tasks in 3D space 41
4.4 Path planning algorithm of a marsupial robot team for deployment 42
4.5 Simulation result 49
4.5.1 Simulation setup 49
4.5.2 Deployment scenarios in 2D space 50
4.5.3 Deployment scenarios in 3D space 57
4.5.4 Deployment in a dynamic environment 60
4.6 Performance evaluation 62
4.6.1 Computation time 62
4.6.2 Efficiency of the path 64
Chapter 5 Collection of a Marsupial Robot Team 67
5.1 Problem definition 68
5.2 Complexity analysis 70
5.3 Optimal collection path planning for two rovers 71
5.3.1 Collection for two rovers in 2D space 71
5.3.2 Collection for two rovers in 3D space 75
5.4 Path planning algorithm of a marsupial robot team for collection 76
5.5 Simulation result 83
5.5.1 Collection scenarios in 2D space 83
5.5.2 Collection scenarios in 3D space 88
5.5.3 Collection in a dynamic environment 91
5.6 Performance evaluation 93
5.6.1 Computation time 93
5.6.2 Efficiency of the path 95
Chapter 6 Deployment of a Marsupial Robot Team using a Graph 99
6.1 Problem definition 99
6.2 Framework 101
6.3 Probabilistic roadmap generation 102
6.3.1 Global PRM 103
6.3.2 Local PRM 105
6.4 Path planning strategy 105
6.4.1 Clustering scheme 106
6.4.2 Determining deployment locations 109
6.4.3 Path smoothing 113
6.4.4 Path planning algorithm for a marsupial robot team 115
6.5 Simulation result 116
6.5.1 Outdoor space without obstacle 116
6.5.2 Outdoor space with obstacles 118
6.5.3 Office area 119
6.5.4 University research building 122
Chapter 7 Conclusion 125
Bibliography 129
초록 151
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dc.formatapplication/pdf-
dc.format.extent31695139 bytes-
dc.format.mediumapplication/pdf-
dc.language.isoen-
dc.publisher서울대학교 대학원-
dc.subjectMulti-robot systems-
dc.subjectmulti-robot path planning-
dc.subjectmarsupial robot-
dc.subjectdeployment-
dc.subjectcollection-
dc.subjectenergy constraint-
dc.subject.ddc621.3-
dc.titlePath Planning for Efficient Deployment and Collection of a Marsupial Robot Team-
dc.title.alternativeMarsupial 로봇 팀의 효율적인 배치 및 회수를 위한 경로 계획에 관한 연구-
dc.typeThesis-
dc.contributor.AlternativeAuthorLee Hunsue-
dc.description.degreeDoctor-
dc.contributor.affiliation공과대학 전기·컴퓨터공학부-
dc.date.awarded2017-08-
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Theses (Ph.D. / Sc.D._전기·정보공학부)
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